Welcome To The Data Wilderness

Stacey Schwarcz — Sun, 09 Feb 2025 17:00:00 +0000

Stuck In The Data Dark Ages?

Crag Camp, New Hampshire

Is your company saddled with legacy systems and manual reporting?

Are you frustrated by data silos?

Still using Excel like it’s the 90s?

Lost In The Data Wilderness?

Root Glacier, Wrangell-St. Elias, Alaska

Are you overwhelmed by data but can’t get useful insights?

Do you think you might need a data scientist or a data engineer but not sure which one or what they really do?

Do you need to learn more about data and analytics or improve the analytical capabilities of your team, but you’re not sure where to start?

Do You Wish You Had A Guide Through This Data Wilderness?

Glacier National Park, Montana

Welcome to the Data Wilderness, the official blog of Ariel Analytics, where you'll find answers to these and other questions.

If you head up a business function and need to learn more about analytics, or if you head up an analytics team and find it difficult to communicate with the business side, then this blog is for you.

About Me

Lake Tahoe, California

I’ve worked with a lot of data over the years and I started to think about all of this data and many aspects of the fields of data science, data analytics, and data engineering as a sort of untamed wilderness.

As someone who loves hiking and being outdoors, the inspiration behind this blog is to combine my career interest in data analytics with my love of the outdoors. In each post I bring a little of the actual wilderness in with hiking photos and hopefully provide some inspiration for new places to visit and explore.

Data Analytics And Operations

Harding Ice Field, Alaska

I’m particularly interested in how this data wilderness impacts operations and back office functions. Many companies focus their technological resources on client facing systems while key operations and support functions struggle with legacy systems. Older and larger companies are particularly affected. They’ve had more time to accumulate a variety of back office systems, build up large amounts of data, and develop fortified data silos. Often data is poorly defined or isn’t consistent or standardized.

If you’re in operations or a support function this probably sounds familiar. What are the biggest analytics challenges for your company? What types of analytics issues do you encounter most often?

How To Foster A Data-Driven Culture

Building a data-driven culture is a challenge for many companies. Systems, tools, methods and business process all play a role.

How are you using analytics at your company?

Do you want to do more but not sure how to get started?

Do you need guidance in developing or implementing your analytics vision?

Is your company struggling with data challenges?

Do you want help building an analytics function or improving the analytics capabilities of your team?

Could you or your team benefit from data analytics coaching or training?

Ariel Analytics offers several solutions.

Check out our service packages and find out how ariel analytics can help.

Discover our Services Discover our Services

About the cover photo: Exit Glacier, Harding Ice Field Trail, Seward Alaska
Thoughts: This is one of the most memorable hikes I’ve ever done. If you want to get up close and personal with moose, bears, glaciers, and an ice field this is the hike for you. If you only have time for one hike in Alaska make it this one!

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

Stacey Schwarcz is the Founder and CEO of Ariel Analytics. She specializes in analytics for business operations, helping these functions improve their analytics capabilities. She is also the creator of The Data Wilderness ® Blog, which provides practical introductory analytics content for business professionals who are not analytics experts and want to learn more. LinkedIn

Where Will Data Science Take You?

Stacey Schwarcz — Sat, 08 Feb 2025 17:00:00 +0000

Is Data Science The Right Path For Your Company?

Do you feel pressure to hire a data scientist or build a data science team?

Does it seem like others in your industry are reaping the benefits of data science while you’re still struggling to automate your reporting?

Are you being left behind?

Let’s take a closer look.

So What is Data Science?

The short answer: a relatively new field that is often poorly defined and misunderstood. It typically refers to work involving AI/machine learning, statistics, probability, and/or some level of software engineering.

What Does a Data Scientist do?

A Data Scientist tries to answer difficult questions and predict future trends using advanced analytics and complex modeling techniques including (the much hyped) machine learning. They analyze large volumes of data and (sometimes) real time data, which requires knowledge of specialized tools and platforms. Data cleaning, an underestimated and underappreciated part of the process, is vital to success.

What Does Data Science Look Like?

Classic data science problems include: fraud detection in insurance and banking; natural language processing (text analysis) in media and publishing; healthcare analytics for patient outcomes; and consumer analytics for customer acquisition and retention.

What Data Science Is NOT: A Fast Way To Get Answers To Your Business Problems.

Data science is an adventure: an expedition, not a day hike.

It’s an R&D function and a long term investment. It requires dedicated, and often expensive, resources. Timelines may be long and outcomes can be uncertain.

Are You Ready For Data Science?

What is the state of your data right now?

Is your data organized and defined? Is your reporting in order? If not, you may be better off improving these areas first.

Do you have a defined problem and does it require a data science solution?

Do you have complicated problems that require complex models to solve and/or large amounts of data (think millions or billions of records) or high velocity data (think stock trading)? Beware of data science for the sake of data science or data science in search of a problem. Be sure that you know what your business problem is and that it requires a data science solution.

Finally, can you dedicate the resources needed, wait for a solution, and keep an open mind about results?

If the answer to any of these questions is “no”, you might not be ready for data science - yet.

So What If You’re Not Yet Ready For Data Science?

Consider some other ways to get the data insights that you need.

Do you need quick answers to key business questions? An experienced data analyst can help with this.

Do you need to automate or improve your reporting process? Consider a business intelligence or reporting specialist.

Do you have difficulty extracting or connecting data from multiple sources, or capturing data that isn’t currently in a system? A data engineer may be able to help.

Is your problem more of a software problem than a data problem? Look for a software engineer.

A data scientist is not a one-stop-shopping solution to all data problems. Different data professionals, including data scientists, have unique and specialized expertise. You may need a few different types of data professionals to fully address your data challenges.

Where Will Data Science Take You?

Data Science can do a lot for your company once it is ready and has a true need for data science and when you set realistic expectations.

If you’re not ready then data science won’t take you where you need to go and there are more effective ways to get what you need.

DO YOU NEED GUIDANCE IN BUILDING OR MANAGING AN ANALYTICS FUNCTION?
DO YOU WANT TO IMPROVE THE ANALYTICS CAPABILITIES OF YOUR TEAM?
COULD YOU BENEFIT FROM DATA ANALYTICS COACHING OR TRAINING?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

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About the photos: Shenandoah National Park, Virginia
Thoughts: The Old Rag Mountain hike is probably the most famous hike in the Shenandoah. It’s a challenging rock scramble so if you’re looking for a bit of challenge this is a great hike. If it sounds too challenging there are lots of less ambitious hikes in the Shenandoah or if you prefer to view nature from your car check out Skyline Drive.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

What Is Your Analytics Destination?

Stacey Schwarcz — Fri, 07 Feb 2025 17:00:00 +0000

Do You Know Where You’re Headed?

Do you wish you had better analytics?

Do you have a vision for what you want your analytics function to look like?

Do you know how to get there?

Let’s take a look at defining a data analytics vision.

What Does Data Analytics Mean To You?

Analytics and data science can have many interpretations. It can be as simple as improving your reporting process or as complicated as advanced AI.

What does your company need?

Transforming your analytics capabilities doesn’t happen overnight. As with any strategic undertaking it will go more smoothly if you have a direction and a plan for how to get there. Identifying your business needs and where analytics might help is the first step.

What are your biggest and most expensive problems and what do you think of when you imagine those problems solved?

Is it interactive dashboards and automated reporting, better systems, more detailed insights, process automation, predictive tools for forecasting/planning, AI or something else?

There isn’t one answer. It depends on what your company needs, the resources you can dedicate, and on your current state of readiness.

Where Are You Starting From?

To achieve your vision you’ll need take stock of your current state and set realistic goals.

How far is your analytics vision from your Current reality?

Consider the current capacity and capabilities of your team, your data infrastructure, and your analytics tools. Then think about where you want them to be and how you’ll get there.

How easy is it to get quality data from your systems? Can your current data infrastructure support the analytics function you envision?

How skilled is your current team in extracting and analyzing this data? What other advanced analytics capabilities do they have?

Do you have the necessary analytics tools to perform the analysis you need?

Once you’ve assessed the gap you’ll need a plan to get from where you are now to where you want to be.

Bridging The Gap

Once you’ve defined your vision and assessed your current state you need a plan to achieve your vision.

Try breaking your vision down

Create a 5 year strategic plan and determine where you want to be at year 1, year 2, and eventually year 5. Be realistic and conservative about what you can achieve by these milestones.

If your end goal is to take advantage of advanced AI, but your team currently struggles with reporting or it’s difficult to get quality data from your systems, then it’s going to take a while to build up to that goal. You’ll need to develop your people, processes, and data infrastructure.

Identify and Prioritize your projects

Think about where you’ll get the most value. Consider which projects are most critical and which are easier to achieve. Find the balance that provides the greatest impact vs the effort and resources required.

Think simple instead of complicated

Often simple or basic analytics solutions like better data visualization can provide a lot of value, especially where you currently have little to no information about a particular process or problem.

Starting slow and simple while solving big problems will help achieve buy in from the analytically skeptical while you build the data and analytics foundation needed to tackle more complex projects and deploy more complex analytics solutions.

Do you need guidance in developing or implementing your analytics vision?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

Discover our Services Discover our Services

About the photos: Minnewaska State Park Preserve, New York
Thoughts: Just a couple of hours north of New York City you’ll find some great hiking, including Gertrude’s Nose and the Ice Caves.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

Permalink

Bridging The Gap

Stacey Schwarcz — Mon, 09 Aug 2021 16:00:00 +0000

Analytics in Operations Part 7: Getting started with analytics in operations and series wrap-up

Bringing It All Together

Do you lead an operations function (or a part of a function) like finance/FP&A, HR, IT or business development?

Have you developed a strategy for improving analytics in your division/group/or team?

Are you trying to figure out how to implement it or where to start?

This is the final post in a seven-part series that considered:

The nature of operations and why this makes it more difficult to improve analytics capabilities in operations functions.
An analytics framework for understanding the analytics process better, particularly if you’re new to analytics.
The analytics challenges in operations and ways to improve data quality and get better data insights.
The coordination and the analytics knowledge, skills and roles needed to improve analytics in operations.

This post will wrap up this series with a focus on how to get started with analytics in operations as well as how to continue to build analytics capabilities.

Timeline

The first step in improving analytics capabilities in business operations is to identify your business and analytics goals as well as evaluate your current capabilities and potential. This information will help in creating an analytics plan.

Consider these key components.

Improving Capabilities

You won’t get much done without the right roles in place. Identify the people, roles, skills and coordination that you need to get started.

Keep in mind that it takes multiple stages, a lot of time to develop internal resources, and a variety of skills to develop analytics capabilities.

Data availability and Data Quality

The initial focus needs to be on data sources, data needs, data quality, basic analytics and reporting/business intelligence.

Coordinate with your technology team to develop the data infrastructure and processes to get the data and achieve the data quality that you need.

Reporting and Automation

If your company doesn’t have its reporting worked out, you’ll find that’s where much of your initial focus will be, whether you want it to be or not.

Work to reduce manual time spent on reporting to free up time for analysis. Start with improving your reporting process and developing better business intelligence and basic analytics.

Down the Road

Once you have a strong data and reporting foundation you can shift your focus to more [complex analytics efforts] where you may need additional and more advanced analytics skill sets like a data scientist.

Don’t Get Too Far Ahead

Advanced analytics may be stealing all the headlines but it’s not a good place to start. Improving your analytics capabilities is a journey and you need to start with the fundamentals.

Data First

Operations has a lot of data but you’ll spend all your time finding it and fixing it if you try to fast forward to advanced analytics. You must have a strong data, reporting, and basic analytics foundation before advanced analytics is possible.

Basic Analytics Next

If you hire an advanced (expensive) analytics team too soon, they’ll get pulled into day to day reporting or bogged down in data scavenger hunts. Many data science teams have unwittingly become data strategy and business intelligence teams. Remember that a data scientist is not one-stop-shopping solution for all your data needs and that a better strategy is to build a strong analytics team.

Higher Ground

Operations is about keeping everything running smoothly in an environment where day to day and cyclical responsibilities dominate. Within these functions data analytics can be seen as a separate specialization rather than integral and critical.

The key to improving analytics in operations is to make it core to the business and more relevant on a daily basis so that your people can see the value and embrace it.

Many data analytics challenges are really process and communication issues: they are cultural rather than technical.

Lost In the Data Wilderness?

Operations functions can easily be forgotten as they operate in the background. However, these are the functions that keep a company running and operating smoothly.

Safeguard your company’s profitability. Make sure that your operations functions aren’t lost in the data wilderness.

Your Data Wilderness Guide

The purpose of this series was to provide practical information for getting started with and advancing your operations analytics transformation. I hope that you have gained valuable insights and new perspectives, but it can be difficult to go it alone. Most companies will benefit from expert guidance to get on the right track, stay focused, and make faster progress.

How Ariel Analytics Can Help

As a small company we provide specialized data analytics expertise and personalized attention to guide our clients in accessing the power of their data and their people to make better business decisions.

When you work with Ariel Analytics, we will help your organization improve their analytics capabilities through data strategy, better analysis and models, report automation and interactive dashboards, and analytics training to boost employee skills and knowledge to achieve long term impact.

We offer several ways to work with us, check out our Guide, Level-up, and Immersion packages and our areas of expertise to learn more. Or book a free consult.

Why Choose Us?

Ariel Analytics specializes in data analytics and strategy for business operations. We are very familiar with the challenges in these areas including the struggle with legacy systems, unreliable or inaccessible data, communication/coordination gaps, and insufficient analytics resources and time for development.

We believe that companies have enormous untapped potential with respect to their people and their data, and this is particularly true in business operations where tried-and-true feels safer and innovation can be slow.

We specialize in data strategy and analytics, but we know that people are at the core of your business and we excel at bringing people together from all levels of an organization to understand and solve business problems.

We’re here to guide you so you can bridge the gap, maximize the potential of your people, process and data, and take your company to the next level.

Don’t get lost in the Data Wilderness, let us guide you!

Want more on this and other analytics topics? Join our mailing list! Our email subscribers get access to the complete series in an e-Book - Analytics in Operations.

DO YOU NEED GUIDANCE IN How to improve analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

Discover our Services Discover our Services

About the photos: Denali National Park, Alaska

Thoughts: Denali is one of my favorite national parks. I was last there in the summer of 2019, and the day we were there the park was experiencing a flooding issue that closed part of the road. Instead of taking the shuttle through the park we ended up walking the road for about 8 miles which was an interesting way to see Denali. If you leave the main park road, Denali is truly the wilderness with very few marked trails. It’s the only national park I’ve been to where they tell you go wherever you want, rather than “stay on the trail”.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

The Trees

Stacey Schwarcz — Tue, 06 Jul 2021 16:00:00 +0000

Analytics in Operations Part 6: Analytics knowledge, roles, and skills needed in operations

Analytics Capabilities

Are you trying to improve the analytics capabilities of your team?

Is it a challenge to find time for staff development while maintaining core operations responsibilities?

Do you wonder what roles you need and if you should hire new people or train your existing staff?

Let’s look at the knowledge, skills and roles needed to improve analytics in operations.

Raising The Bar

Business operations is a unique environment with a particular set of analytics challenges. Solving these analytics challenges requires changes and improvements in systems, tools, methods, business process, and communication and better coordination between teams. This in turn requires improving the overall level of technical and analytics knowledge among the business team.

Not just for analysts

It’s not just the analysts that need analytics knowledge; analytics is a team sport. Throughout operations, people need to increase general knowledge and awareness of technology and analytics. While everyone does not need to become a data analytics expert, understanding more about data and analytics will make it easier to work effectively with technology and data professionals. There are several areas where more knowledge and understanding is helpful.

Systems

A better understanding of existing systems and their limitations, as well as how data quality issues arise, will help in interactions with the technology team and analytics professionals. This will also help with understanding problems and advocating for solutions.

Tools

Upgrading existing tools requires understanding which tools are no longer effective, or efficient, and being able to evaluate alternative options.

Methods

Since nearly everyone must deal with data in some capacity, improving knowledge of basic data analysis methods will allow them to work more productively (e.g. learning to use existing tools more effectively and efficiently). Business professionals should learn enough about data and analytics to know when it might be useful to enlist the help of data professionals and how to direct and inform this work.

Business Process and Communication

As business experts, operations professionals will need to help analysts, analytics teams, and the technology team understand business needs and problems. Increasing knowledge and awareness of the technical side will make it easier for business operations professionals to communicate more effectively with technical teams leading to better project outcomes.

Analytics Roles

Defining analytics roles can be challenging. We’ve talked about analytics roles before but let’s get into more detail about the roles and skills that will be needed to improve analytics capabilities in business operations.

Busines Process Expert/Project Manager

A business expert is critical to determine business priorities. Business knowledge is the key input to any analytics project, followed closely by effective coordination. A team with a combination of business and technical skills will be most effective in solving business problems.

The primary contribution of the business expert is an understanding of the business. However, in order to inform the analytics process they will need some understanding of the current business systems and enough knowledge of analytics tools and methods that they can coordinate effectively with the analysts and the technology team. They’ll need to provide business direction and ask the right questions to keep the project on track.

While they don’t need the detailed technical knowledge of a data analytics expert, they will need a high-level (basic) understanding of data issues and when analytics is helpful.

Data ENGINEER

Data quality and availability is often the biggest roadblock in analytics efforts. Data engineers are central to ensuring good data quality and accessibility.

Data Engineers can help with setting up data correctly in a system, identifying and addressing the source of data issues, extracting data from systems, and getting the data into reporting and analytics tools. You may hear the term data pipeline in connection with this set of processes.

Data engineers typically have database and/or data warehousing expertise, including computer programming skills.

Data analyst

The goal of analytics is to derive useful business information from data. In the early stages of building out an analytics functions, a data scientist is usually not necessary, but data analysts will be critical.

The role of a data analyst is to analyze large datasets and derive insights from multiple data sources. They need to be able to identify the data they need and interface with the technology team as necessary. They should have a good understanding of basic analytics methods and tools as well as some advanced analytics knowledge.

They’ll need to learn and understand the business processes and business problems.

Business intelligence Analyst

Reporting is a critical area of need for operations and often occupies far too much employee time, from creating reports to accessing and using them in productive ways.

A business intelligence analyst focuses on report automation, efficiency, and the effective display of information to design and create interactive dashboards.

They need skills in data cleaning, data transformation, data visualization, and a basic understanding of databases and accessing data from various sources including data warehouses. They’ll typically have experience with one or more enterprise business intelligence tools (Power BI, Tableau, etc). They will also have some computer programming capabilities including SQL to query databases, and R or python (programming languages commonly used in data analytics).

Data scientist

Once you’ve established basic analytics capabilities you may find that you’re ready for and need more advanced analytics. At this point you’ll want to consider adding a data scientist to your team.

A data scientist has an advanced level of analysis, modeling, and computer programming skills. They can handle very large/real time datasets and can develop complex models. A data scientist also needs some business understanding and the ability to work closely with a business expert who can provide business knowledge context to guide the technical work.

There are many types of data scientists

A data scientist isn’t a monolithic role. There are a wide variety of specialized technical skills among data scientists, and the specific skills needed will vary by industry, company, and division.

A Word On data science job descriptions

There are far too many poorly worded and confusing data science job descriptions out there. Make sure that yours isn’t one of them. These often present as a laundry list of every data science skill and tool out there. If you need a specific set of skills, be sure to define the role appropriately and specifically. Don’t list skills or tools that you don’t need now or in the near future.

DEVELOP OR HIRE?

Business operations functions that are just getting started with analytics will need to add employees to many of these roles. In some cases, you may have people with the expertise to step directly into these roles or the ability to develop into the role with the right guidance. In other cases, you may need an external hire.

Improving data analytics capabilities will likely require a combination of training existing staff and hiring new people. Each comes with its own challenges.

Looking Within

Roles that require more business knowledge and are less specialized/technical will be easier for existing employees to step into. These roles include business knowledge experts, data analysts, and business intelligence analysts.

Every company has talented employees who want to learn more about analytics and/or have more advanced capabilities that are underutilized. Consider which of your analysts are interested in and capable of augmenting their knowledge and skills. It’s likely that you have people who have the foundational skills for these roles but need some training.

Why look within?

Internal resources may need to improve their analytics knowledge but they are already familiar with the nuances of your business processes and how to work within your culture to get things done.

making TIME for Talent dEvelopment

One of the key challenges in business operations is lack of time. Learning new skills takes time and requires the opportunity to apply that knowledge.

For an existing employee to grow into a role they’ll need mentorship and guidance. They’ll also need time away from day-to-day responsibilities for training as well as the opportunity to experiment and apply new knowledge and skills to real business problems.

This may be difficult at first, but it will pay off in the long run as manual data and reporting tasks are automated freeing up time for more valuable analysis work. In addition, you’ll likely end up with a more dedicated and engaged employee as you’ve given them the chance to develop and learn new skills.

Broadening The Search

When it comes to advanced analytics/technical resources like data scientists and data engineers, it’s less likely that you’ll have this level of skill or knowledge unless you’ve explicitly hired for or developed this skill set.

WHen To hire

New hires may be necessary if the skill set for an analytics role doesn’t already exist and would be difficult to develop in the appropriate time frame.

Talent acquisition Challenges

While new hires bring immediate access to advanced technical and analytics skills, the hiring process for data and analytics talent can be time and resource intensive.

There is also a learning curve for new employees to become familiar with your business, systems, processes, and company/department culture. In addition, they’ll still need time to learn new technical and analytics concepts. Technology and analytics is constantly evolving and your company’s needs may be a bit different from the company they came from.

When an advanced skill set is needed immediately and doesn’t already exist in house, hiring can provide a faster solution, but may not be quite as fast as you’d expect.

A Team Approach

Multiple people, skills, and roles are needed to develop analytics capabilities. These roles include data engineer, business expert/project manager, business intelligence analyst, data analyst, and data scientist. Each brings a specialized skill set. The strategy for filling these new roles will likely be a combination of developing internal talent as well as hiring new talent.

We’ve covered many aspects of analytics in operations. The next and final post in this series will consider how to get started with analytics and continue to build these capabilities in operations functions.

Want more on this and other analytics topics? Join our mailing list! Our email subscribers get access to the complete series in an e-Book - Analytics in Operations.

DO YOU NEED GUIDANCE to improve reporting and analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

Discover our Services Discover our Services

About the photos: Joshua Tree National Park, California

Thoughts: I enjoyed Joshua Tree much more than I would have expected. In addition to the trees themselves, there are a lot of interesting shrubs and rock formations. The Mojave section of the park has a lot of short trails which are great for less adventurous hikers. It also attracts a lot of rock climbers, although I didn’t personally do any climbing. Finally, drive the road through the park to see the fascinating transition from the Mojave to the Colorado desert.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

The Forest

Stacey Schwarcz — Mon, 24 May 2021 23:00:00 +0000

Analytics in Operations Part 5: Analytics and technology coordination for business operations

A High Level View

Are you developing an analytics strategy?

Is it difficult to get everyone on the same page?

Do you wonder if you should create your own analytics team or coordinate with other divisions/groups?

Improving data analytics capabilities requires multiple skill sets and departments working together. Every company is unique, and coordination will vary by company. There is no one right way or one-size-fits-all.

Let’s look at a couple of areas where challenges often arise, and where you’ll need to identify the best strategy for your company.

Where Does Analytics Belong?

Some companies create a centralized team that oversees analytics throughout the company. It’s expected that all analytics issues, questions and requests go through this centralized function.

Other companies have analytics resources integrated into several business teams. Sometimes this is the result of a deliberate strategy, in other cases it develops organically with business teams hiring analytics professionals or developing their own expertise to meet their individual business needs.

There are pros and cons to each of these structures.

Integrated

The advantage of analytics resources (one or more analysts) being embedded in the business team is that they are closer to the business process and problems and can devote time to the needs of the specific business team. They’ll have a better understanding of the process and easier and more frequent communication with the business team so they can develop more targeted solutions.

The Problem with IntegrateD Resources

The risk is that these analysts become detached from other analysts and teams and lose sight of the overall company direction. This can lead to a variety of tools and methods being used throughout the company, duplicated efforts across teams, and can compound data silos. An embedded analyst or analytics team is also more likely to get pulled into day to day operations tasks and ad hoc requests and away from analytics.

Centralized

The advantage of a centralized analytics team is that they can focus exclusively on analytics while working with multiple business teams. They are also more likely to be aligned with the overall company vision and maintain a coordinated strategy (systems, methods, and tools) across divisions.

The Problem with Centralized TEAMS

A centralized team is more removed from the individual business teams, so the risk is that they lack a complete understanding of the individual business process and problems. They will be more focused on common problems that affect multiple teams, rather than the unique challenges of specific teams. As a result, the specific needs of business teams may not be well met in this model leading to the development of rogue analytics teams. In addition, because they are more removed, these teams are more likely to be distracted by projects that are analytically interesting but don’t provide business value.

HYBRID

A third option is a hybrid model with a centralized analytics team as well as analysts embedded within some of the business teams to provide specialized attention while coordinating with the centralized team.

WHY CHOOSE A HYBRID MODEL?

A hybrid model may work well for a company that needs centralized coordination for systems, tools and processes, but also has business teams with specialized individual needs that won’t be well met in a centralized structure.

This model can help a company maintain overall coordination and consistency and minimize duplication of efforts across the company. At the same time, it provides a way for teams with unique analytics needs to address those within the business team, in coordination with the centralized team.

There isn’t one best way. You’ll have to decide what works best for your company and possibly experiment with different approaches.

Who Is Looking Out For Your Data?

Depending on the maturity of your company’s data infrastructure you’ll likely have significant data needs.

What is the state of your Data infrastructure?

Data needs will vary a lot by company based on its business needs and the maturity of its data infrastructure. In some cases, data warehouses have already been built and data is connected, centralized, accessible, and reliable (this is rare). In other cases, a lot of work must go into supervising the development of a data warehouse or somehow centralizing, cataloguing, and validating data sources (this is much more common).

Where are the resources?

A lot of early work in developing analytics is focused on the data itself. This includes identifying data sources, validating and cleaning data, setting up data correctly in a system, identifying and addressing the source of data issues, and extracting data from systems. This is generally the domain of data engineering. The technology team may or may not have the necessary skills and capacity or be receptive to working with the business teams. In some cases, dedicated resources will be needed.

How Will you coordinate?

Business intelligence work will require coordination and access to existing systems, data warehouses or other data resources. Consider in advance how the coordination with the technology team might work and start building a strong relationship early. Decisions will need to be made about whether the business side will have dedicated technology resources and, if so, how they’ll coordinate with the technology team.

What Works Best For Your Company?

Good coordination within and between teams is key to a successful analytics strategy. There are several ways to approach this, depending on your company characteristics. There isn’t one right way, but there are several things to consider when developing a coordination strategy.

Will you have a centralized analytics team, or will you integrate analytics resources into each of the business teams, or a combination of the both?

Will you have dedicated data engineering/ technology resources within the business team and how will you coordinate with the technology team?

The next post in this series will look at the [skills and roles] that are needed to improve analytics in operations.

DO YOU NEED GUIDANCE IN How to improve reporting and analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

Discover our Services Discover our Services

About the photos: Windham High Peak, Catskill Mountains, NY

Thoughts: The Catskill Mountains are just a few hours north of New York and provide good hiking opportunities, including the Windham High Peak area (a popular New York ski resort in the winter).

The new dog in town: You may have noticed a new dog in the photos. Meet Sebastian! He doesn’t have a company named after him – yet – but he’s shaping up to be a great hiking companion. Follow his outdoor adventures on Instagram @Sebastian_Times.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

Gaining Perspective

Stacey Schwarcz — Mon, 26 Apr 2021 12:00:00 +0000

Analytics in Operations Part 4: Addressing analytics challenges in operations

Getting Started

Do you wish you had better data?

Would you like to do more with that data?

Have you tried to make improvements, but it hasn’t been as successful as you’d like?

Operations has unique challenges that make it difficult to improve data and analytics capabilities but catching up is critical to improving overall company results.

Let’s consider ways to address analytics challenges in operations.

The Base – Better Data

The ability to access, analyze, present, and display reliable information depends on good data quality. The analysis becomes difficult or even impossible when there are underlying data problems.

Improving Data Quality

Ensuring data quality, consistency and accessibility require addressing some of the key analytics challenges in operations, including legacy systems, manual tracking, data silos, and duplication. These challenges are tied to systems, business process, and communication issues.

working with LEGACY SYSTEMS

We know that older systems often pose data extraction challenges. Depending on the system, there may be some workarounds. It may be possible to have a systems administrator set up custom data extracts for reporting and analytics that refresh on a scheduled basis. Alternatively, it may be possible to change the process for how data is entered or add new data fields to make it easier to retrieve key information.

Moving Past LEGACY SYSTEMS

However, there may be no quick or easy fix to this problem. You’ll need good communication with the technology team to identify potential short- and long-term solutions. In some cases, the only effective solution may be to upgrade or replace the system despite the complexity and expense.

The PROBLEM WITH MANUAL TRACKING

Manual tracking occurs when users enter and store data in spreadsheets and local tools, or on paper. This is a major source of data inconsistencies, missing data, and poor data quality. All data should be stored in a system.

Why it Occurs

This issue generally occurs for one of the following reasons, each with its own potential solution:

There is no existing system that is appropriate for the type of data that needs to be stored (e.g. no customer relationship management (CRM) system to track clients).

In this case, you’ll need to consider whether to invest in a new enterprise system or tool.

The appropriate system exists but isn’t configured in a way that allows easy/intuitive data entry (e.g. entering and tracking clients is overly complicated and/or lacks customization).

Resolving this issue requires coordination between the business and technology teams to understand business process requirements and identify technical solutions. For example, adding or adjusting fields or tables, or making changes to the user interface (where possible) can improve how data is entered and stored.

People don’t understand how to use the system effectively or don’t know that it exists, so they choose to enter and store data locally. The result is that the system isn’t used or is used inconsistently, leading to manual tracking and inconsistent data.

This can be addressed by providing clear business process direction on when/how/what data to enter as well as technical training on how to use the system.

This requires patience as it may take several iterations until there is widespread consistent use. However, if the system was worth the investment then it’s worth the additional marginal investment to ensure it’s being used effectively. Companies often invest in expensive platforms and then get frugal about the training and communication needed to ensure that the system is used optimally.

Eliminating manual tracking through system, business process, and communication changes will go a long way toward improving your data quality.

Connecting DATA SILOS

Data silos result in poor data quality and duplication of efforts. Data silos are generally the result of departmental silos related to the company culture.

This is a business process and communication issue. The best way to address this is by improving communication between departments/divisions and between more and less technical teams. This is much easier said than done but it will go a long way toward improving all aspects of data analytics in business operations.

The Climb - Using Data Better

While business process, communication, and systems challenges are at the core of data quality issues, knowledge of analytics methods and tools are necessary to turn this data into useful information.

tools alone are not enough

There are a wide variety of reporting and analytics tools, many of which have become increasingly user-friendly and require less technical knowledge. However, analysts still need proficiency in data and analytics methods to use these tools effectively.

Analytics Knowledge is key

Knowledge of analytics methods and how to use analytics tools is necessary to derive insights from data and develop better and more automated reporting. However, these skills are generally not as common as they need to be in operations.

Developing ANALYTICS EXPERTise

There are two primary ways to acquire these skills: train (upskill) or hire. There are several considerations when deciding whether to improve your in-house talent vs looking to external resources or a combination of the two. There are advantages and disadvantages as well as costs associated with each of these options. Later in this series, we’ll take a closer look at what roles are needed and the types of skills that need to be developed.

when tools matter

Once you’ve addressed business process, systems, and communications issues, and you have people with the necessary analytics skills and knowledge, then the right tools will improve how efficiently and effectively you can extract, transform and analyze large amounts of data.

time to upgrade?

There are a wide variety of analytics tools available, both general and specialized, but operations functions often rely on Excel and older proprietary tools which limit their analytics capabilities.

The specific tools are often less important than all of the other pieces. Consider which tools will be most effective and easiest to use based on your current capabilities. Determining which tools are right for your company will vary based on your business problems, your data infrastructure, and your analytics skills and knowledge. There isn’t a one-size-fits-all solution.

manual reporting wastes resources

Reporting is another area that is challenging for operations and often occupies far too much employee time, from creating reports to accessing and using them in productive ways. Manual processes are common and require employees to repeatedly run through tedious, time-consuming, and error-prone tasks. This leaves little time for more valuable analysis and developing more advanced skills.

Automating and Improving reporting

Report automation, as well as data visualization through interactive dashboards, is critical to get to a functional level of basic analytics. This will provide critical on-demand and up-to-date information and allow for more informed business decisions. It will also reduce the manual time spent on tedious reporting tasks, freeing up time for more complex analysis.

Improving data quality is the first step, followed by developing reporting and business intelligence skills and knowledge. With these in place acquiring a business intelligence tool will make it easier to develop and maintain automated interactive reports.

The Summit – Better Information

High-quality data is the key to reliable data insights, reporting, and analytics. With a solid data and reporting foundation, it’s possible to move to the next level and begin to take advantage of advanced analytics.

Data analytics challenges, including poor data quality, typically arise from a combination of systems, tools, methods, and business process issues, as well as weaknesses in communication/coordination between operations divisions.

Many data analytics challenges are really process and communication issues: they are cultural rather than technical.

Improvements in these areas will improve data analytics capabilities and effectiveness in business operations functions which in turn will make the company more effective with better business results.

The next post in this series will consider the coordination needed between various divisions and teams.

DO YOU NEED GUIDANCE IN How to improve reporting and analytics in your operations functions?
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About the photos: Harding Icefield Trail, Kenai Fjords, Alaska

Thoughts: The Harding Icefield is one of my all-time favorite hikes. It’s an all-day challenging hike, but if you’re in good hiking shape I highly recommend it. The views of the icefield are spectacular. You’re also likely to see some wildlife up-close. The first time I hiked the trail I came within 6 feet of an enormous black bear and later encountered a moose at a similar distance.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

Analytics In The Background

Stacey Schwarcz — Mon, 22 Mar 2021 23:00:00 +0000

Analytics in Operations Part 3: Analytics challenges in business operations

Falling Behind

Is it difficult to get the information that you need for critical projects?

Does every data request set off a data scavenger hunt?

Do you feel like your reporting is stuck in the 90s?

Good data quality and the ability to easily extract useful information from that data is critical to business operations and company success.

Unfortunately, common analytics challenges often get in the way. These challenges affect all companies and all divisions to some degree. However, the nature of business operations functions intensifies these issues.

Let’s take a closer look at the data and analytics challenges in these functions and where companies tend to lag in analytics.

Data Quality

One of the biggest impediments to analytics is data quality. Poor data quality leads to poor analytics results and wasted time and effort.

There are several areas where data quality issues arise in business operations and where companies may struggle.

LEGACY SYSTEMS

Data is at the core of any analytics effort and being able to easily access this data in a user-friendly format is critical. Newer systems are designed with this idea in mind, but older systems can present significant data extraction challenges.

Any company that has been around for more than a few years likely has older systems. System migrations are complicated and expensive so older systems tend to stick around for longer than they should.

Since operations is responsible for internal data tracking, they tend to have the bulk of these legacy systems, such as employee management, talent acquisition, finance/accounting, procurement, resource planning, etc. These systems are less noticeable to those outside of operations and they are more likely to be outdated than client-facing and other high visibility platforms.

MANUAL TRACKING

Another key piece of the analytics process is accurate and consistent data. There are numerous best practices to ensure good data quality when data is stored in a system.

Unfortunately, it is common in operations functions for data to be tracked manually by individuals in Excel spreadsheets or, even worse, on paper. This compromises data integrity and consistency resulting in unreliable or inaccessible data.

DATA SILOS AND DATA DUPLICATION

Data quality and consistency is critical to producing reliable data insights and reporting. This requires communication and coordination between departments as well as between business operations and the technology/systems teams.

Unfortunately, operations functions can be disconnected from each other leading to process and data silos. This results in data inconsistencies and data duplication where each function is tracking their own set of data but may be measuring the same process. For example, HR and Finance might each be tracking their own set of headcount data.

DATA QUALITY AND CONSISTENCY

All of these issues lead to data quality, consistency, and reliability issues, that make it difficult to produce reliable data insights, reporting, and analytics.

Data Insights

While data quality is the greatest challenge in analytics, the next challenge is transforming this data into useful information. Business operations functions generally struggle in one of more of the following areas.

MANUAL REPORTING

Reliable data insights and key performance indicators are critical to understanding, managing, and planning operations.

Reporting processes are typically built up as needed over a long period of time. These processes are repeated quarterly, monthly, and even daily. They often rely on a compilation of Excel spreadsheets or Access databases and require a series of manual steps to extract and transform data.

This tedious and time-consuming work takes analysts away from the more valuable and engaging analysis that they should be doing and increases the chance of errors and inconsistencies.

This is especially true in operations due to its cyclical nature, and because the focus is on keeping everything running smoothly rather than analytics innovation. As a result, there is rarely a comprehensive analytics strategy behind report development. Organically grown, manual reporting processes are common.

OVER-RELIANCE ON EXCEL AND LEGACY TOOLS

Being able to efficiently extract, transform and analyze large amounts of data has become critical as the amount and complexity of data has increased.

Newer tools and platforms can make these tasks easier and help with automation. However, because operations functions tend to be risk averse and have limited time to experiment, they are slow to adopt new methods and tools. They may still rely heavily on Excel or older propriety tools for analytics and reporting. While there is still a role for Excel, it’s not a great tool for automated company reporting and it’s not up to the task of more advanced analytics.

Relying too much on older tools and methods holds companies back and prevents them from realizing the full potential of their data and their people.

ANALYTICS EXPERIENCE IS LIMITED

In addition to modern systems and tools, operations functions also need people who are trained in analytics methods and how to use analytics tools. Those in operations haven’t typically hired for these skills, and due to the nature of these functions it can be challenging to find the time and support for people to develop these skills on-the-job.

A Different Perspective

Business operations functions by their nature are mostly out of sight and often forgotten unless something goes wrong. If your operations functions rely on outdated tools, systems, methods, and processes, then your company will be less efficient and less productive. Despite the many obstacles, improving analytics in business operations functions is critical.

The next post in this series will focus on ways to address these analytics challenges.

DO YOU NEED GUIDANCE IN How to improve reporting and analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

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About the photos: a few more from Grand Teton National Park, Wyoming

Thoughts: For a spectacular sunrise get up early and check out the summit of Signal Mountain. There’s a road to the top, so no need to hike up in the dark, unless you want to. If you’re lucky you might even see a bear or two.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

Frame Of Reference

Stacey Schwarcz — Thu, 18 Feb 2021 01:00:00 +0000

Analytics in Operations Part 2: An analytics map, systems, tools, methods, process and communication

An Analytics Map

Are you trying to improve analytics in an operations function (finance/FP&A, HR, business development, etc)?

Do you wish that you knew more about data and analytics?

Would it help to have a map?

The nature of operations leads to analytics challenges in operations functions that can undermine company performance. These issues arise because of weaknesses in one or more of the following areas: systems, tools, methods, business process, and communication. Understanding these components, as well as how they’re connected and impact analytics will help in understanding and addressing analytics challenges.

Systems

Systems are where data is entered and stored. There are a wide variety of systems used by companies. These include human capital management, customer relationship management, talent management, enterprise resource planning, and more.

WHY THEY MATTER

While largely the domain of IT, ensuring high-quality data requires input from business teams and good communication between business and technology teams.

Tools

A wide variety of analytics tools exist, from basic and user friendly to advanced and complicated, and from general to highly specialized. These include business intelligence/dashboard tools for reporting and data visualization, as well as more powerful analytics tools and programming languages for data analysis and data science.

WHY WE NEED THEM

Tools are used by anyone who needs to work with data, which is just about everyone now. These are software packages that can be used to extract, aggregate, transform, analyze, and visualize data. Excel is still the most widely used tool today but, as you may have experienced, it has its limitations.

Methods

Tools are only helpful if people know when and how to use them. Even Excel requires some basic data analysis knowledge.

WHERE THEY COME IN

To use these tools effectively analysts must know or learn analytics methods including data cleaning, data integration (combining data from multiple sources), data transformation (changing the structure or format of the data), statistical analysis, and data visualization.

More advanced analytics professionals (like data scientists) must have knowledge and experience with more advanced analytics methods such as modeling/forecasting or handling extremely large or real-time datasets.

Business Process

A business process is why systems exist and why tools and methods are needed.

THE STARTING POINT

These processes are necessary to run a business and will determine what type of data is collected and stored, and which reports are needed to support the process.

Understanding the types of problems a business is facing will influence the type of analysis needed to understand and solve these problems.

It’s critical to start with a good understanding of the business process. This will inform the data and analytics requirements and drive decisions about systems, tools, and methods.

Communication

Successful business processes require good communication. Multiple skillsets, departments, and people are needed to meet the analytics needs of business operations. Better communication skills are critical for everyone.

THE DIVIDE

Unfortunately, there is often a disconnect between the technology and analytics teams and less technical business teams. Better understanding of the challenges in each direction can improve coordination and project outcomes.

Full Circle

Systems, tools, methods, business processes, and communication are key pieces of a successful analytics process. Understanding each of these pieces and the connections between them provides more context when thinking about both the nature of operations and the analytics challenges in operations.

With this map in mind, the next post in this series will consider the analytics challenges in business operations.

Do you need guidance in how to improve reporting and analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

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About the photos: In memory of Ariel, companion, hiking buddy, and therapy dog.

Thoughts: Ariel was the inspiration behind the name Ariel Analytics. She was an avid hiker and a certified therapy dog who loved to visit the local nursing home and make people happy. She persevered through many challenges in life (including being in a shelter and having knee surgery - twice). She was an inspiration to all who met her. I wish I could say that she was a great guide though the wilderness, but while she had a fantastic memory and was a great communicator, search and rescue was not her calling. Sadly, we lost Ariel to cancer in February 2020, but her memory lives on as our company namesake.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

Behind The Scenes

Stacey Schwarcz — Thu, 21 Jan 2021 22:00:00 +0000

Analytics in Operations Part 1: The nature of business operations and why it leads to analytics challenges

Getting Clarity On Analytics In Operations

This is the first of a seven-part series about analytics in operations.

Operations refers to business support functions including finance, human resources, business development, sales, and technology: the functions behind the scenes that keep a company running.

What this series will cover:

Understanding the nature of operations and why this makes it more difficult to improve analytics capabilities in operations functions.
An analytics framework for understanding the analytics process better, particularly if you’re new to analytics.
The analytics challenges in operations and ways to improve data quality and get better data insights.
The coordination and the analytics knowledge, skills and roles needed to improve analytics in operations.
How to get started with analytics and continue to build these capabilities in operations functions.

Out Of Sight

Do you lead an operations function, or a part of a function, like finance/FP&A, HR, IT or business development?

Is your function falling behind in data and analytics?

Have you tried to make changes but haven’t been as successful as you’d like?

Operations functions have unique challenges that make it difficult to improve data and analytics capabilities. This results in data and analytics weaknesses that can undermine company performance, and because these functions are mostly out of view these problems don’t get addressed.

Let’s start with understanding the nature of these functions and how it leads to analytics challenges.

The Nature Of Operations

Flashy headlines around AI and machine learning can make it seem like analytics is all virtual assistants and complex algorithms, advising us on what to buy next or predicting the next market downturn (or claiming to).

People working in areas of advanced analytics generally work in environments with newer systems and cutting edge tools, where innovation is expected and risk is likely to be rewarded.

OPERATIONS IS A DIFFERENT WORLD

This is not the reality in most business operations functions. Systems and tools are older and often outdated, while the environment itself can be a challenging place to implement an analytics strategy.

A better understanding of the nature of operations will help when considering improvements in the data and analytics capabilities of these areas.

MAINTAINING CORE OPERATIONS IS KEY

The primary focus of operations is on keeping everything operating smoothly. If you think about a railroad, this literally means keeping the trains running on time.

MOST WORK IS CYCLICAL

Daily/monthly/quarterly deadlines and responsibilities are the norm, with a lot of reporting, planning, and scheduling.

LESS DATA ORIENTED

While this is beginning to change, many operations functions, such as human resources, have traditionally been less data driven and informed.

INNOVATION IS RISKY

If nothing is broken, trying new methods and tools is often discouraged while risk taking is rarely rewarded.

OPTIMIZATION with OLDER TOOLS

While optimizing company operations may be a core responsibility this is often accomplished using outdated tools, systems, and methods.

LIMITED TIME TO EXPERIMENT

The nature of operations leaves limited time to experiment with new methods and tools. Since developing analytics expertise often happens through experimentation this hinders the development of analytics skills.

EVEN TECHNOLOGY LEADERS ARE NOT IMMUNE

Surprisingly, this can affect companies that are otherwise technology leaders. Your company can be an industry leader in AI, marketing analytics or software engineering, while your support functions are lost in the data wilderness.

Losing Ground

Business operations functions face a variety of analytics challenges. The nature of these functions compounds the difficulty of making improvements in this area.

These are the functions that keep a company running and operating smoothly. Allowing them to fall behind in analytics will make it difficult for a company to get timely business insights and ultimately affect company profitability.

The next post in this series will provide more context for understanding and addressing analytics challenges. Sign up for our email list to be the first to know when the next blog is published!

DO YOU NEED GUIDANCE IN How to improve reporting and analytics in your operations functions?
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About the photos: Grand Teton National Park, Wyoming

Thoughts: Grand Teton is just south of Yellowstone. It doesn’t have the geothermal attractions of Yellowstone, but since it’s a bit less trafficked you have a better chance of seeing more wildlife.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

Data Pioneering

Stacey Schwarcz — Sun, 22 Sep 2019 18:00:00 +0000

Surveying The Situation

This is part 3 of a three part post on data quality and data strategy. See part 1 and part 2.

Do you encounter data quality issues that impact your reporting or analysis?

Do you wish you knew what was causing these issues and how to resolve them?

Let’s look at some of the most common data challenges.

Improving Consistency

Why We Care about Data Inconsistencies

In order to easily process, analyze, or report on data it must be consistent. This includes consistent spelling, number and date formats. There cannot be any exceptions because occasional data inconsistencies lead to automated reporting and analysis failures.

What Causes Data Inconsistencies

Data inconsistencies happen when data is manually entered into a system, especially when it’s manually tracked, and where standardization and controls are lacking. Standardization includes drop down lists for categorical data (think names of regions or divisions). Controls can prevent the entry of invalid data to ensure that a required data field isn’t left blank or that a month isn’t entered where there should be a complete date.

How To Resolve Data Inconsistencies

Addressing manually tracked data: To analyze or report on data it must be in a system. Understand the process and why it’s being tracked, ensure that it isn’t already in a system (efforts may be duplicated across divisions), and work with IT to make the necessary system changes and eliminate manual tracking.

Improving system data: Improve standardization and controls at the system level for these data items will resolve these issues.

Filling In The Blanks

Understanding a process requires comprehensive information about that process. If there is data about only some occurrences of an activity or some of the people then it will be difficult or impossible to extract useful insights about that process.

Where You’ll See Missing or sparse Data

Multi-step long term processes like business development and talent acquisition that require data entry by multiple people about a variety of activities over a period of time, tend to have missing or sparse data issues.

How To Resolve Missing or Sparse Data Issues

If you expect users to enter data in a specific way but find that they aren’t doing what you expected then you have a business process or communication problem. You’ll need to define the process for how and when data should be entered, and make that process clear to all involved. If you want your business development team to enter information about client calls, you’ll need to communicate that to the team.

Dealing With Difficult Data Formats

What is a difficult data format?

A difficult data format is any format that cannot be easily processed (think PDF files, word documents, text in an email) which make analysis complicated.

Why You Might Receive Data In This Format

It is fairly common to receive data in a difficult format even from mostly automated systems such as building entry systems. Older (legacy) systems especially homegrown systems are more likely to produce outputs in difficult formats. When receiving data second hand rather than data that was extracted specifically for you or your project it’s possible that the original recipient didn’t intend to run an analysis so that format was acceptable.

How To Fix It

Don’t spend time on difficult data formats unless absolutely necessary. The best way to address it is to identify the original data source and request an easy to use format (CSV or Excel). For newer systems this should be simple. Legacy and homegrown systems may require workarounds.

Finding Your Way

A lot of data challenges are related to how data is entered and many of these issues can be resolved with better definitions, a clearly defined process for data entry, and system controls that prevent inconsistencies like variant spellings.

Sometimes you can’t resolve the issue or the data isn’t collected the way you need it: it’s not frequent enough, detailed enough, or stored for long enough. This requires a more complicated (expensive) solution and you should carefully weigh the costs and benefits before proceeding.

Is your company struggling with data challenges?
Check out our service packages AND find out how Ariel Analytics can help!

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About the photos: White Mountains, New Hampshire
Thoughts: The White Mountains are home to the famous 6,288 foot Mount Washington, as well as many other 4000+ foot peaks. You can hike, drive or take the train up to the summit of Mt Washington. The mountains aren’t as tall as they are out west, but the extreme weather conditions result in a lower tree line for great views. The Lincoln-Lafayette ridge trail is a great hike and the AMC (Appalachian Mountain Club) hut system gives hikers the option of hiking hut to hut for several days with just their clothing and a toothbrush. The huts provide food, water, bathrooms (no showers) and a place to sleep, but they’re not cheap and you’ll need to book well in advance.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

The day we hiked Mt Washington the weather was perfect and visibility was great. We got lucky, this is pretty rare.

Charting The Data Wilderness With Better Data Strategy

Stacey Schwarcz — Sun, 22 Sep 2019 17:00:00 +0000

What If You Had Better Data?

This is part 2 of a three part post on data quality and data strategy. See part 1 and part 3.

Do you wish that you had better quality data or that you were able to use more of your data?

Are you missing important business insights locked away in this data?

It turns out that just having a lot of data isn’t very helpful, it needs to be good data.

A Path To Better Data

Improving your data strategy can help you transform your data into good data.

What is Data Strategy?

Data strategy is about managing all aspects of your data better including how data is created, stored, accessed, and utilized.

Why You Need Better Data Strategy

Improving your data strategy will make it easier to get the information that you need when you need it. It will reduce errors and unnecessary work. It will improve reporting consistency and the quality of the data in your systems.

What Better Data Strategy Will Do For Your Company

In the short term it will improve company reporting, business intelligence and basic analytics, providing significant business value. In the long run you’ll be in a better position to take advantage of more advanced analytics, obtain deeper insights, and develop predictive tools to take your company to the next level.

Making Incremental Changes

Trying to implement a comprehensive data strategy can be overwhelming, but you don’t need to tackle everything at once. You can improve your data strategy incrementally.

Start With What You Know

Most companies have a core set of data that they report on often (think sales or headcount). Ask yourself: Are there any challenges with this data that you can address? How difficult is it to get and use this data? Are their known consistency or accuracy issues? Can you improve your reporting and analysis by addressing these issues?

Continue With What you Need

Identify a set of functions or processes that you need to understand better. Work backwards to the data you need to inform this understanding. Much of this data will be familiar, but the rest will take you into pioneer territory where you’ll have the opportunity to improve more of your data.

Acknowledge That Data Pioneering Is Unpredictable

Be sure to allow enough time for data verification and cleaning. It’s better to be pleasantly surprised than to commit to deadlines you can’t meet due to unforeseen data quality issues. It’s safest to assume that any data that hasn’t been used before isn’t good data.

Get To Know Your Data

No matter what the next big technology trend is, getting your data in order and under control will allow you to thoroughly understand your business processes, help you make better decisions and take advantage of new technology.

It’s important to realize that you don’t need to tackle everything at once. Try an incremental approach: start with what you know and add what you need.

Is your company struggling with data challenges?
Check out our service packages AND find out how Ariel Analytics can help!

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About the Photos: Acadia National Park, Maine
Thoughts: Acadia is a great choice for east coast hiking with beautiful views of the ocean. Most of the hiking is moderate but for the slightly more adventurous there’s the Beehive Trail. If you’re looking for more of a challenge try the Precipice Trail which will take you up the cliff face, with iron rungs to climb and iron bars as hand holds along ledges. Make sure to get there before (or after) the peregrine falcons!

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

Beehive Trail

Precipice Trail

Wild And Free? Great For Nature Not For Your Data

Stacey Schwarcz — Sun, 22 Sep 2019 16:00:00 +0000

Is Your Data Living Wild And Free?

This is part 1 of a three part post on data quality and data strategy. See part 2 and part 3.

Do your projects get delayed or derailed due to data related problems: lack of data, missing data, or just bad data?

Is it difficult to get the data that you need when you need it?

More Data ≠ Good Data

Companies collect a staggering amount of data. Unfortunately, corporate data systems are not the oasis of clean and organized data that one might expect. Your analysis options will be very limited if your data isn’t in good order, which impacts your ability to make better business decisions.

The Challenges Of Corporate Data

There are many different types of companies and systems, but their internal corporate data challenges are similar.

What Types of Systems Are We Talking About?

There is a wide variety of internal corporate data systems. People management, client management (CRM), and enterprise resource planning (ERP) are just a few. What they have in common is they all collect and store company data and can suffer from similar data issues.

where do these Data issues start?

When it comes to system data, most data is either manually entered or auto-generated. Not surprisingly, manually entered data is more likely to be the source of data quality issues.

Even more problematic is data that’s not in a system at all. It’s not uncommon to see critical processes that are manually tracked, often with Excel spreadsheets created and maintained by an individual or department.

What are the most common Challenges?

When analyzing corporate data, especially data that hasn’t been used before, you’ll likely encounter a variety of issues. The most common are data inconsistencies, missing or sparse data, and ambiguous or indecipherable data. In addition you may receive difficult data formats that can’t be easily processed, especially when dealing with legacy and homegrown systems.

Charting The Unknown

Companies usually have a core set of well known data that they use and report on often, but this data represents a small amount of the total data collected. The rest is uncharted territory.

On Being a Data Pioneer

Having a lot of data that no one has ever used may seem like a gold mine of untapped potential, but if it’s not good data it can lead you to engage in projects that you can’t support because the data isn’t what you think it is or where you think it is. It’s very easy to run into problems with this data, and when that happens it can significantly extend project timelines.

So How Do You Get Better Data?

Internal data quality and control issues are a significant challenge for many companies. Most companies do some things well, but struggle in other areas. Your company may have good processes around client or customer data, while your internal data is an uncharted wilderness.

The best way to improve your data quality is by improving your data strategy. My next post explores this topic.

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About the Photos: Glacier National Park, Montana
Thoughts: Glacier is one of my favorite national parks. It has some great hikes that are generally well graded: a gradual climb instead of sudden steep sections. You’ll get above the treeline quickly so you have open views for much of the hike. There’s no rock scrambling or any technical hiking. If you’re not a hiker you can still see spectacular views from your car on Going to the Sun Road.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

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The Long View

Stacey Schwarcz — Sun, 18 Aug 2019 16:00:00 +0000

Getting Clarity On Advanced Analytics

Do you hear a lot about advanced analytics and wish you knew more about it?

Wondering what types of business problems it’s best for and when it might be helpful?

Do you secretly think its’s mostly just hype?

My last post on basic analytics is a good place to start if you’re new to analytics. This post will cover more advanced analytics and provide a better idea of the types of business problems it can solve.

Where Advanced Analytics Can Take You

There are certain types of problems that are more complicated and for these problems basic analytics is just the start of the process. Once you’ve verified facts and/or have found that something has changed over time and/or have established metrics and standards, you’ll need more advanced analytics. Let’s look at some of these methods.

You Need to Forecast OR PrEDICT Future Trends (Predictive analytics)

You may need to forecast or predict future changes in order to plan operations, estimate resource needs, or evaluate risk. This can include revenue forecasts, workforce planning, or client churn predictions. Forecasting and prediction methods are based on relationships and changes over time. Additionally, a strong understanding of the business context is key.

You Want to detect unusual activity (ANOMALY detection)

Anomaly detection is an area of predictive analytics that focuses on detecting unusual activity so you can prevent something from happening. Anomaly detection is used extensively in fraud detection (think credit card fraud). It is also used in manufacturing/industrial applications to identify slight changes in sensor readings that predict when machinery will malfunction allowing you to fix the problem before it occurs. This is known as predictive maintenance.

You need to Extract Useful information from WRitten words (Natural Language Processing)

Natural language processing (also known by its initials NLP) is an area of predictive analytics that is used to analyze text. Natural language processing is very helpful when you need to get useful insights from written text.

A retail company may want to analyze customer complaints without having a person read through all of the complaints; NLP can provide an overview of typical customer issues and identify areas where you’ll want people to do a more in depth review.

You Want to group people or Things (Clustering)

Grouping people or objects together based on data can offer insights about your business problem. For example, you might want to create customers groups based on their buying patterns so you can customize your marketing strategy. This type of analytics is referred to as clustering.

You Want to UnDERSTAND How People or Things interacT (Network analysis)

Developing an understanding of how people in your organization interact, can provide insight into your business operations. This involves network analysis, an area of analytics that relies on graphs and maps to understand connections. This is how social networks like Facebook can recommend potential friends or how Netflix can recommend shows or movies. Network analysis is also used extensively in logistics, distribution, supply chain management, and transportation.

The Building Blocks Of Advanced Analytics

These methods are more complex and generally require more advanced and specialized skills. They also require more time, more advanced tools, and can be expensive.

A NOte on Models and Machine Learning

Machine learning is currently very popular and many of these methods rely on machine learning and other types of quantitative and computational modeling techniques. I will explore these concepts in a future post. The purpose of this post is to give you a general feel for the types of problems that advanced analytics can solve without getting too technical.

Resources needed

You’ll likely need a data scientist for this work, but they’ll need to have expertise in the specific area that is relevant to your business problem. Data science is a broad field and covers many types of advanced analytics expertise. Experts in anomaly detection or natural language processing aren’t necessarily experts in network analysis and vice versa.

Making The Most Of Analytics

There are many areas where data and analytics is very useful for understanding and solving problems. This is the second part of a two part series. This post described advanced analytics methods and when they can help solve business problems. My previous post on basic analytics includes an overview of descriptive analytics as well as advice on evaluating the cost effectiveness of data analytics projects. A cost/benefit analysis is particularly important when considering any project that will involve advanced analytics.

How are you using analytics at your company?
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About the Photos: Rocky Mountain National Park, Colorado
Thoughts: Rocky Mountain National Park has some extremely challenging hiking as well as some easier hiking. If you’re taking kids check out Nymph, Dream and Emerald lakes on the Emerald Lake Trail. If you’re feeling adventurous and have four wheel drive, check out the views along Fall River Road, it’s one way only so there’s no going back once you start!

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

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Analytics To The Rescue

Stacey Schwarcz — Sun, 21 Jul 2019 16:00:00 +0000

Is Analytics The Answer?

This is Part 1 of a two part series on when analytics is most helpful. See part 2 for more on advanced analytics.

Can analytics solve your business problems?

Wondering if more data will help?

Not sure if it’s worth the time and effort?

Let’s explore when and where data and analytics is most useful.

Where Basic Analytics Can Help

When faced with a business problem it can be hard to know whether data and analytics can help solve the problem. There is currently a lot of focus on advanced analytics, but because simpler is often better, let’s start by looking at several situations where basic analytics can help.

You Need to Verify Facts

Analytics is extremely helpful when you need to quantify a situation, like how many new hires leave your company within the first 6 months. This is the most basic form of analytics, but verifying facts can help you identify and understand a problem better. It’s fairly common for company beliefs and ideas to be treated as facts even if they were never grounded in data. Not all beliefs and ideas are quantifiable but those that are should be verified.

You Want To COMPARE GrOUPS

Analytics can help when you want to evaluate differences between groups like divisions and product lines, or compare your company to other companies. You may want to compare the attrition rate of one department to another, or of your company to an industry benchmark.

You Want To understand how something changed over time

Analytics is key if you want to understand whether something has changed over time or identify important trends, like whether the attrition rate has changed over time and if so is it higher or lower than in prior years.

You Want to Evaluate A Relationship Or Understand patterns

Analytics can help you understand whether two things are related to each other. You might want to understand whether profit margins and the number of clients per sales manager are related; do sales managers with a lot of clients have higher margins or lower margins, or is there no connection? You can learn a lot from these relationships, but you’ll need an analyst with a good understanding of statistics to ensure that you are finding real relationships. It’s actually common to find relationships in the data that aren’t real. These are referred to as “spurious correlations”.

You need to Establish Metrics and Standards

Once you have verifiable facts and you’ve explored trends and relationships you’ll want to know what these mean for your company. You need to understand whether you should be concerned about what you found and if you need to take action. Analytics can help you set metrics and standards to evaluate what if any changes are needed.

Resources Needed

These methods largely fall under the category of descriptive analytics. They describe what’s going on and can provide insight into possible reasons for your business challenges. This can provide some or all of the information that you need to solve problems and make business decisions. A data analyst is a good choice for this type of analytics as long as you are not dealing with extremely large data sets or real time data.

Should You Proceed?

Now you know more about analytics methods and where these might be helpful, but how do you know when it’s cost effective?

Analytics can be extremely valuable: it can help solve problems and save time and money. However, more data and analysis isn’t always the solution. Analytics can be expensive since it may require special systems, tools, and talent. This is especially true of advanced analytics.

In order to focus your resources on more cost effective projects, consider the following steps for evaluating the cost-benefit trade off of analytics projects.

Understand the Problem

Start with a clear understanding of the problem. Not having a clearly defined problem from the beginning often results in wasted time and money. Defining your problem usually requires a combination of business and data analytics knowledge to understand both the business context and possible analytics solutions.

How Expensive is the PRoblem?

How expensive is the problem you’re trying to solve? If it’s minor and inexpensive it may not be worth investing the resources needed to fix it. On the other hand, if it’s a costly problem it may well be worth the investment needed to address it.

Where’s the Data?

Is data readily available to support or refute factual information and assumptions? Can you verify that the data is available and clean, and that it’s been used before. If not, this can substantially extend your timelines and increase costs. Be sure to investigate data availability before moving forward.

Verify basic facts and Assumptions

Often just verifying basic facts and assumptions can quickly determine if there is a problem and further define it. Sometimes anecdotal evidence can kick off a quest for a solution when there isn’t actually a problem, or it’s only a temporary or minor problem.

Evaluate the Level of Effort AND Resources

Determine what type of analytics you’ll need and how complex the analysis is. Advanced analytics will likely take longer and cost more, as well as come with a range of uncertainties. Consider if this is really what you need.

You’ll also need to identify the resource needs and whether you have these resources in house. Finally, since business context is key, you’ll need people with business knowledge involved in the project, so you’ll need to estimate the level of contribution needed from various departments.

Making The Most Of Basic Analytics

There are many areas where data and analytics is very useful for understanding and solving problems. This post covered basic analytics, including verifying facts, comparing groups, evaluating relationships and patterns, and establishing metrics and standards. It also looked at how to evaluate the cost effectiveness of analytics projects.

Don’t underestimate the power of good descriptive analytics. However, there are complex business questions that require advanced analytics solutions. My next post will cover this topic.

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About the Photos: Rocky Mountain National Park, Colorado
Thoughts: Rocky Mountain National Park has some extremely challenging hiking as well as some easier hiking. If you’re taking kids check out Nymph, Dream and Emerald lakes on the Emerald Lake Trail. If you’re feeling adventurous and have four wheel drive, check out the views along Fall River Road, one way only so there’s no going back once you start. https://www.nps.gov/romo/planyourvisit/old_fall_river_road.htm

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

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Perspectives From The Data Wilderness

Stacey Schwarcz — Sun, 16 Jun 2019 16:00:00 +0000

Are You Frustrated With The Analytics Process?

Are you managing an analytics team or project but you’re new to analytics?

Do you wonder why some analytics tasks take so long?

Is it difficult to pin down project timelines?

Let’s take a closer look at that analytics process to get a better understanding.

The Manager’s View

It’s not uncommon for companies to have an experienced manager who is not an analytics expert oversee an analytics team or manage an analytics intensive project. This can be a frustrating position to be in for someone who is not familiar with the analytics process.

It can be notoriously difficult to get accurate time estimates for analytics projects, which makes it seem like projects take too long. It can be hard to determine what’s really going on, whether efforts have gone off course, or if you need to step in and how to redirect or refocus your team when necessary.

Let’s look at things from the perspective of the analyst and explore ways to work more productively with your team.

The World Of The Analyst

As a manager it can be hard to understand what’s going on at the ground level and your analysts may find it difficult to explain. A better understanding of the analytics process may help.

Analytics is a multi-step / multi-Day Process

Even a basic analysis requires several steps usually spread over multiple days to get familiar with the data and the problem, conduct an initial data evaluation and exploration, and complete a first draft of the analysis. Any requried modifications or updates require feedback and additional time and steps.

Analytics is Also an iterative process

Analytics can be more art than science. Large or complex problems don’t have one obvious approach, method, or solution. There’s a lot of trial and error, which makes it hard to estimate how long it will take. Sometimes a team stumbles upon a great solution immediately while other times it takes multiple attempts.

Intense focus is Required

It is not uncommon for analysts to be working on multiple projects at the same time although this is usually not a good idea. Analysis requires getting deep into the details while maintaining a sense of the big picture. Mentally keeping track of all of the details makes it very tricky to work on more than one analytics project at a time. Once an analyst stops work on a project it takes time to re-immerse in the details and continue working.

Analytics Is a Team Sport

It can be very frustrating and unnecessarily time consuming to work alone as an analyst. It’s easy to get stuck on a particular method, a bug in a software program, or get too far into the details and lose sight of the big picture. Without a team, or at least another analyst to help, it takes a lot longer to get unstuck.

Data Quality Can Be unpredictable

When navigating corporate data it is not uncommon to encounter data quality issues like inconsistent, ambiguous, and missing data. Data cleaning can take a significant, and uncertain, amount of time. Even when data has been used before and has been cleaned, it may not have been used in the same way requiring additional data preparation.

The Challenges of Writing Code

Many analytics efforts require some level of computer programming such as SQL queries for extracting data from databases, and R / Python programs to process and analyze data or create a repeatable analysis. This remains true even as tools have become more advanced and require less coding. Similar to analytics, computer programming is also multi-step, iterative, and requires intense focus.

The Pain of Debugging

Debugging is a big part of coding and involves running code multiple times to find and fix problems. It's hard to estimate when you'll discover a bug or how long it will take to fix. Sometimes solving one problem creates a new problem adding to the time uncertainty.

How Can You Work More Productively With Your team?

Whether your project is report development, report automation, a new analysis, or a new model, now that you have a better understanding of the challenges you’re in a better position to address them.

Know Where You’re Headed

Before any data is analyzed be sure that you and your team understand the problem (ensuring that you’re solving the correct problem), then determine what sort of solution is expected and in what format.

What does the deliverable look like?

A set of numbers, an Excel Spreadsheet, a dashboard on a preferred software platform, or a full text report? Creating a rough mock up of the final deliverable in whatever format it’s expected in will help get everyone on the same page. It will allow you to iterate quickly on the design and general content before moving ahead with more complicated analysis so you can show quick early progress.

One Time or Repeatable?

If you create something great that people love they’ll want to see it again. Most one time requests turn into requests for repeatable analysis or dashboards, which requires more work, forethought, documentation and a plan for future maintenance / support. Be prepared for this likelihood.

Working as a Team

Analytics is a team sport. You need a team of analysts and business experts working together, even if they don’t all report directly to you. If you only have one analyst consider partnering with another team with one or more analysts. It’s easy to lose sight of the big picture when you’re deep in the details, have your team work together to maintain the right balance.

Coding As A Team

If your team does a lot of coding it’s best for the team to decide on one language that everyone uses. Establish guidelines for adding comments and documenting code. Well documented code should be clear enough that someone with business knowledge but no coding experience can generally understand what it does. Finally, have your team review, run, and test each other’s code.

Break the work down

Limit multi-tasking in favor of more focus. Asking analysts to switch between projects means more time to re-immerse in each project later. However, there are natural break points in an analysis. Work with your team to figure out where the break points are so work can be paused or handed off to another analyst.

On Project Management Tools

Let your team decide how best to share work. There are many project management methods, but if they don’t work well for your team than they’re a waste of time and resources. Let your team choose the one that works best for them.

Ask For a Tour

If you’ve never written computer code but you manage projects that involve computer programming it’s a good idea to sit down with one of your analysts and have them walk you through what they do to get a better sense of what the process is like. You don’t have to learn to code or understand everything, just a glimpse into their world will help.

More Understanding = Less Frustration

The analytics process can be challenging for managers and analysts. A better understanding of the process and what it’s like on the ground level makes it easier to work more productively with your team and reduce the frustration level for both you and your team.

Do you need guidance in building OR managing an analytics function?
Do you WanT to improve the analytics capabilities of your team?
Could you benefit from data analytics coaching or training?

CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

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About the Photos: Lucifer Falls, Buttermilk Falls, Taughannock Falls, Ithaca, NY
Thoughts: The finger lakes region in upstate New York has beautiful moderate hiking where you can see some spectacular waterfalls. It’s not exactly wilderness, so get ready to climb some stairs.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

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Beyond Unicorns

Stacey Schwarcz — Sun, 19 May 2019 16:00:00 +0000

The Search For Analytics Talent

Are you building a new analytics function or expanding an existing one?

Do you have high expectations for these analytics roles?

Does it feel like an impossible task to find the right person?

The search for analytics talent is often more difficult than it needs to be. Let’s explore ways to make the process more productive.

Do You Really Need A Unicorn?

There’s a lot of talk about unicorns, a multi-talented highly skilled individual who is both an analytics and a business expert.

Unlike the mythical creature analytics unicorns do exist but they are rare, expensive and…unnecessary.

Just as a data scientist is not one-stop-shopping for all your analytics needs, trying to find the perfect mix of business and analytics experience in one person is a frustrating and expensive way to build an analytics function. A focus on unicorns can quickly max out your hiring and workforce budget reducing the size of your potential team.

Consider A Team Approach

Building a strong multi-faceted and diverse team is a more productive and potentially less expensive way to build your analytics function. When you bring together several individuals with complementary skill sets including subject matter, institutional, and analytics expertise you’ll get:

More Context

Analytics is about defining and solving problems which requires a deeper understanding of the context of the problem and situation. A team that contains multiple people with diverse skill sets and company knowledge will ask more relevant questions and better define problems, making it more likely that they’re solving the right problem.

The Ability to Iterate faster For Better insights

Analytics is iterative by nature. It takes several tries to find the right method and solution. A well formed and functioning team will interact and share ideas leading to more feedback. They will generate and evaluate potential solutions faster than if you only had one or two analytics unicorns.

Greater Capacity

There’s a limit to how much work one person can do, no matter how talented. Having more people will provide more capacity and the ability to share and divide work.

Less dependence on one person

If you have one or two very rare resources there is a high risk that they will go somewhere else. Building a larger team will make you less reliant on any one person. You’ll also have the opportunity to train people opening up progression paths for your employees.

Matching Talent To The Timeline

Once you’ve mapped out your analytics vision and time frame, you’ll need to match the people to your plan.

what Do you need Now?

Building or expanding an analytics function is a process. You’ll need different skill sets along the way. If you’re developing a new function you’ll need a different set of skills than if you’re expanding one.

It’s important to identify the types of expertise that you need now and in the immediate future and use these as requirements to identify internal or external resources.

Avoid unnecessary requirements

Requiring skills, knowledge, and/or experience that you don’t yet need and may never need can cause you to overlook qualified internal and external candidates which will increase the time and cost involved in building a team.

Don’t Overlook Internal Resources

When building or expanding an analytics team consider looking within first.

Untapped potential

There are always employees with capabilities that exceed their current responsibilities who would appreciate and excel at a new challenge. Even when this requires additional training the costs may be less than hiring and on-boarding an outside resource.

Subject Matter Expertise

The best place to find people with knowledge of your existing business and business problems is by looking within. Internal candidates are more likely to have the business knowledge that will lend context to the problem.

Better Buy In

Your people know the culture of your company and have existing internal relationships that will make it easier to get things done when interacting with other departments.

Looking Beyond Unicorns

Your team will likely be a mix of internal and new resources. Being realistic, specific, and focused in defining requirements will make it much easier to identify appropriate resources and build a strong cost effective team.

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About the photos: Denali National Park and Denali Highway , Alaska
Thoughts: When you’re tired of Alaska glaciers and looking for something different Denali is a great option. If you’re out at dusk you may see the Dall sheep. (They’re standing in for unicorns in this post). Beyond the park entrance, Denali is truly the wilderness. You can hike anywhere you want but there are no trails so you’ll need to know what you’re doing. For everyone else there are bus tours and ranger guided hikes. Full disclosure: all of the photos in this post were taken from the park road or from the Denali Highway which is the old Denali access road.

Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.

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Welcome To The Data Wilderness

Stuck In The Data Dark Ages?

Lost In The Data Wilderness?

Do You Wish You Had A Guide Through This Data Wilderness?

About Me

Data Analytics And Operations

How To Foster A Data-Driven Culture

How are you using analytics at your company?

Check out our service packages and find out how ariel analytics can help.

Where Will Data Science Take You?

Is Data Science The Right Path For Your Company?

So What is Data Science?

What Does a Data Scientist do?

What Does Data Science Look Like?

What Data Science Is NOT: A Fast Way To Get Answers To Your Business Problems.

Are You Ready For Data Science?

What is the state of your data right now?

Do you have a defined problem and does it require a data science solution?

Finally, can you dedicate the resources needed, wait for a solution, and keep an open mind about results?

So What If You’re Not Yet Ready For Data Science?

Where Will Data Science Take You?

DO YOU NEED GUIDANCE IN BUILDING OR MANAGING AN ANALYTICS FUNCTION? DO YOU WANT TO IMPROVE THE ANALYTICS CAPABILITIES OF YOUR TEAM? COULD YOU BENEFIT FROM DATA ANALYTICS COACHING OR TRAINING? CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

More from Old Rag

What Is Your Analytics Destination?

Do You Know Where You’re Headed?

What Does Data Analytics Mean To You?

What does your company need?

Where Are You Starting From?

How far is your analytics vision from your Current reality?

Bridging The Gap

Try breaking your vision down

Identify and Prioritize your projects

Think simple instead of complicated

Do you need guidance in developing or implementing your analytics vision? CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

Bridging The Gap

Bringing It All Together

Timeline

Improving Capabilities

Data availability and Data Quality

Reporting and Automation

Down the Road

Don’t Get Too Far Ahead

Data First

Basic Analytics Next

Higher Ground

Lost In the Data Wilderness?

Your Data Wilderness Guide

How Ariel Analytics Can Help

Why Choose Us?

DO YOU NEED GUIDANCE IN How to improve analytics in your operations functions? CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

The Trees

Analytics Capabilities

Raising The Bar

Not just for analysts

Systems

Tools

Methods

Business Process and Communication

Analytics Roles

Busines Process Expert/Project Manager

Data ENGINEER

Data analyst

Business intelligence Analyst

Data scientist

There are many types of data scientists

A Word On data science job descriptions

DEVELOP OR HIRE?

Looking Within

Why look within?

making TIME for Talent dEvelopment

Broadening The Search

WHen To hire

Talent acquisition Challenges

A Team Approach

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The Forest

A High Level View

Where Does Analytics Belong?

Integrated

The Problem with IntegrateD Resources

DO YOU NEED GUIDANCE IN BUILDING OR MANAGING AN ANALYTICS FUNCTION?
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COULD YOU BENEFIT FROM DATA ANALYTICS COACHING OR TRAINING?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

Do you need guidance in developing or implementing your analytics vision?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

DO YOU NEED GUIDANCE IN How to improve analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

DO YOU NEED GUIDANCE to improve reporting and analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

DO YOU NEED GUIDANCE IN How to improve reporting and analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

DO YOU NEED GUIDANCE IN How to improve reporting and analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

DO YOU NEED GUIDANCE IN How to improve reporting and analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.

Do you need guidance in how to improve reporting and analytics in your operations functions?
CHECK OUT OUR SERVICE PACKAGES AND FIND OUT HOW ARIEL ANALYTICS CAN HELP.