1. Pacing. This type of analysis helps you measure progress against goals. These goals can be based on historical data, industry benchmarks or user defined. The important thing to remember is that progress isn’t necessarily linear. For example, some organizations close 50 percent of their total bookings on the last day of the quarter. Others, plateau towards the end. You need a way of understanding how you’ve done historically (or how your competitors are doing) and measure your current progress against that. Pacing analysis gives you a big-picture overview of your trajectory and progress against goals.

2. Parts of whole reporting. This will help you understand the moving parts that are helping you reach your goals. If you’re on target for bookings for the quarter, you’ll be able to see which product lines, sales reps, campaigns or other assets are contributing most to that. If, on the other hand, you’re behind, you’ll be able to pinpoint the cause.

3. Scenario analysis. When faced with a big decision, it often makes sense to consider a worst-case scenario, a best-case scenario and the most likely scenario. This is what scenario analysis does for you. It projects possible future outcomes. You’ll get a view of how these outcomes might occur, for example, if your biggest client has a bad quarter, they might cut out your services to save money, or they might rely even more on you in order to focus on their core competencies. Most likely, they’ll have an okay quarter and nothing will change.

4. Cohort analysis. Also known as segmentation, this marketing-analysis technique lets you see how people engage with your content or product over time. Say you roll out different versions of your website, and you want to understand how much your millennial audience engaged with the new site versus the old one. Cohort analysis will tell you that. It’s the way to see different usage patterns and the evolution of usage over time.

5. Correlation. The classic question in this category: what’s the correlation between diapers and beer? The answer is that when a dad goes to the store to pick up diapers, often times he’ll pick up a six-pack. So diaper sales are positively correlated with beer sales. Correlation analysis can help you find these types of unexpected relationships.

Once you have these 5 business analysis techniques in your toolbox, you’ll be able to cover a lot of analytical ground—and have the information you need to make more informed decisions as a result.

Need help crafting your business analysis strategy? Contact Us.

The post 5 Kinds of Business Analysis Techniques Every Business User Should Know appeared first on GoodData.

Enter GoodData for Yammer, which will show you any trend you want, when you want to see it.

In partnership with Kanjoya, the leading platform for emotional engagement, GoodData combines your operational reporting and emotional sentiment in the same dashboard, providing full context on the structured and unstructured data in your Yammer networks.

We’ve built GoodData for Yammer around best practices and KPIs for measuring internal and external Yammer networks, enabling the measuring and scoring of everything from the big-picture health of your network to individual posts:

• Highlight key trends and topics in your network, groups and even individual posts.
• Perform ad-hoc analysis on key data in Yammer networks (internal and external) around users, groups and Yams.
• Ensure you’re making the most out of your ESN by tracking and analyzing internal Yammer usage and adoption.
• Pit historical trends against current data to see the health of your user base and group activity trends. For example, you can track engagement this quarter, versus previous quarters versus all-time to see how usage patterns are changing and to measure the effect of any programs or campaigns.
  
• Receive text and email alerts if your metrics demand immediate attention.
• Gain a complete overview of your social investments by adding additional data sources, including Facebook, Twitter, Google+, Enterprise Social Networks (ESNs), CRM and other internal data sources.

The Wisdom of the Cloud

GoodData for Yammer is actually an application built on top of GoodData’s leading business intelligence (BI) platform. Thanks to our cloud-based architecture, GoodData scales to accommodate Big Data and easily connects into all types of data sources (cloud-based or on-prem).  This openness allows us to integrate with leading technology companies like Kanjoya, for example, which uses their natural language processing (NLP) engine to provide rich sentiment and emotion scoring for all Yams in the GoodData for Yammer application. Moreover, because all data is on the cloud, it is more secure. (To learn why, read this.)

In short, the cloud is enabling the new era of BI. For the first time ever, it is easy to collaborate with other platforms to compile, analyze and score disparate information sources. Users on any device can see and interact this information in an intuitive visual form, gaining insight on exactly where to improve their business. That capability, more than anything before it, will truly enable you to see the ROI of your network.

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#1 It’s a new thing.
As a business user, your problems are still the same. What has changed is how you get the solutions to those problems; these answers now come from an unlikely source: your big data. More than 85 percent of organizations have big data efforts in action or planned. Big data gives you access to more data, tools and resources to get answers to your business questions, in a different, better and faster way.

#2 It’s complicated.
If almost 90 percent of data spreadsheets have errors, you’re wasting a lot of time using data that is limited, outdated and may or may not be accurate. Intuitive BI platforms, like GoodData, provide out-of-the-box solutions to do the hard work for you.
Today’s systems are designed to eliminate complexity. They are automated, flexible and crafted to meet your specific business needs—without human intervention and errors.

#3 It’s expensive.
Between marketing automation, CRM and social media platforms, you have a lot of data to keep track of. But you don’t have to break the bank to do it. New big data solutions cost less and can be accessed quickly. Regardless of your role, big data solutions now exist that help you identify problems and react to them instantaneously, saving time and money. The reality is that your business can’t afford to not have these systems in place; data is your most valuable asset.

#4 The more data, the better.
How much data you have matters, but not as much as the type. Using KPIs and tracking metrics, you can know what data to collect and understand how it can be used in making smart, data-driven business decisions that generate ROI.

#5 It’s reserved for the number geeks.
Making sense of your big data no longer requires a data scientist or analyst. Dashboards make it possible for anyone, from the SMB to Average Joe, to get data-driven insights and make data driven decisions at the click of a button.

How well do you know your Big Data?  Now is the time to take charge and translate your volumes of data into business value.

The post 5 Misconceptions of Big Data appeared first on GoodData.

Our vision has always been to enable business people to make smarter decisions so they can turn their data into a source of revenue, profit and competitive advantage. Our mission is to make that insight effortless and real-time for users and their companies, by running and managing GoodData’s powerful infrastructure ourselves.

Our cloud-based platform and apps — mash-ups of reports, analytics, metrics and best practices —  are the result. They prove that big data can be user-friendly. Our apps simultaneously crunch data from the Web, mobile devices, social media and legacy systems, and generate a visually stunning  user interface can easily understand. The result? Better data, better insights and greater  monetization. This year’s CODiE award, announced by SIIA at All About the Cloud conference, offers gratifying proof that we are exceeding our mission.

We’re excited and humbled to receive our CODiE because it means that our years of work and dedication have truly paid off for our users — who have always been our  number one priority.

Once again, we  thank you all.

Contact us to learn more about GoodData.

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A Sea Change in Trading

Stock trading is about being the first person to get an order on the books. But you don’t just want any order, you want to create a set of moves that maximize your potential while reducing risk.

For a long time, the first trader to jump on the phone and execute the right orders would win. Fifteen years ago, the Securities and Exchange Commission (SEC) allowed computerized stock trading for the first time. A new era of high-frequency trading (HFT) was born.

When traders realized the potential of computerized trading, they built algorithms that could analyze trades in record time and conduct high-volume trades in nanoseconds. Faster than humans, algorithms can adjust their strategies in real-time, testing the market by placing orders en masse, then canceling them if the market doesn’t provide the desired response.

The race to make the first and best trades quickly grew so tight that traders were placing their computers inside the data centers of some markets, reducing the distance that information packets had to travel to nearly nothing. As a result, the most wired traders were executing the best deals before everyone else, from banks to insurance companies to individuals. HFT became the only way to gain a real profit from short-term trades.

The Big Data Revolution

A similar arms race is occurring with big data today. If you ignore it, it’s like trying to conduct a trade on a rotary phone while others have their computers in the data center. Big data is speeding up and adding intelligence to the ways in which we define our progress, our markets and our potential.

Guesswork, experimentation and the hard labor of crunching numbers to make decisions have long been a part of business. Big data is, if not eliminating them, shrinking them significantly. Ignore it, and you’ll find yourself with outdated technology and an empty wallet. The moral of the big data story is turning into the same one as with computerized trading: Adapt or die.

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Buffett spends countless hours researching each of his equities. Before making a decision, he reads annual reports, news publications and any other information he can get his hands on. His decisions are informed by research, and based on the strict fundamentals that work best for his style of investing.

The way Buffett operates is not unlike the best use cases for big data. Among other things, his success lies in his ability to make good decisions in accordance with quality benchmarks. This is the key to using big data well, and to business success in general.

Big data users can learn from Warren Buffett. His brain, in effect, is a kind of big data engine. Here are three lessons that Buffett provides for big data users:

1. Know What You’re Looking For

By analyzing large amounts of data from diverse sources, we can gain context and correlations that wouldn’t have otherwise occurred to us. Buffett pulls from actuary reports and bond publications; big data can suck information out of nearly everything, from social media to expense reports.

But it is all for nothing if you don’t know what you’re looking for. You need structure around your queries.

Buffett has a few rules of thumb. He looks for low-volatility stocks with a low price-to-book ratio. They should be profitable and growing, among other considerations.

Similarly, if you ask your data analytics platform the right questions, you’ll know the best answers when you see them. Perhaps you’re looking for a Scotch-drinking customer base that prefers premium product and prioritizes convenience over shopping around. If you know that before you ask your data engine to generate their social media preferences, you’ll get a much better result than if you asked the same question about Scotch drinkers in general.

2. Understand How the Object of Your Research Works

Buffett once said: “Never invest in a business you can’t understand.” Given Buffett’s rational investment style, one can surmise the meaning behind that quote. If you don’t understand the mechanics of your investment, you’re relying too much on emotion and guesswork. Or else the business itself isn’t viable.

Likewise, if you don’t understand how the organization or customer base you’re running a big data query on works, you’re relying too heavily on assumptions. You could ask questions of the data that have nothing to do with the reality of the situation. Asking questions about the rate of email responses in a customer support organization that relies mostly on forums won’t get you very far.

3. Use Solid Benchmarks

Some of Warren Buffett’s more famous quotes illustrate the precision and simplicity of his benchmarks:

• “Rule No. 1: never lose money. Rule No. 2: don’t forget rule No. 1.”

• “Only buy something that you’d be perfectly happy to hold if the market shut down for 10 years.”

• “It’s far better to buy a wonderful company at a fair price than a fair company at a wonderful price.”

These benchmarks demand the best. They don’t compromise. Arrange your own benchmarks to similar standards, ask the right questions, and make data driven decisions that reflect your high standards and style.

Make Data Your Servant

Maybe one of Warren Buffett’s biggest secrets to success is that he made the data work for him. If you aspire to do the same, data will quickly become your best friend.

Want to find the secrets hidden in your data? 

The post Warren Buffett, the Human Big Data Engine appeared first on GoodData.

Now, big data promises to consolidate the CIO’s strategic position even further. With it, everyday business users can analyze massive amounts of social, mobile, financial, shopping and operational data. Processed and analyzed correctly, new data can deliver the insight managers need to make better business decisions. It’s the CIO’s role to facilitate that insight.

That means simply running the right systems and ensuring continuous performance is no longer enough. Instead, CIOs need to think strategically about how the technologies and products mesh with your business users’ process. Do the products empower or confuse? Do they open users to new ways to explore data, find answers and make smart decisions? If you don’t provide the right tools and options, you’ve squandered the big data promise of greater efficiency, more sales and improved marketing campaigns. Guess who business users will blame for the wrong tools and implementation?

On the other hand, if you provide the right big-data apps and platforms, your business colleagues will sing your praise for enabling them to turn data into insights they can act on. Think of it: A CIO as rock star. Here are three ways to make sure you deliver legendary impact.

1. Ask the right questions. First, assume that big data will help you find new sources of revenue and profit. But for that to happen, you need to identify the internal and external data that impact your business.

   So ask yourself: Where is the data you need? How do you get your hands on it? Which stakeholders do you have to convince to share it? What’s the best way to distribute the right data to the right department?

2. Set the table. You must put in place the infrastructure that allows business users to access the data they need. It may not be a cut-and-dried situation. The customer service department, for example, requires different data models than marketing or finance. At the same time, you don’t want to overwhelm your IT systems and staff. With your parameters in place, shop for a big data platform that fits your pre-defined needs. This preparation will make you resistant to hype.

3. Work across stakeholders. Marketing, sales, finance, facilities — all of these departments are now in your wheelhouse. Meet with stakeholders to find out where each organization needs the most improvement, and set up your data analytics platform to address these needs. Work out the correct correlations, benchmarks and data models.

Big data can help your company find new customers, eliminate operational inefficiencies, and uncover new ways to make and save money. Even better, with today’s apps and platforms, any company can achieve these benefits. Big data isn’t just for big companies, any more.

Watch this space for our series exploring how companies of all sizes can achieve meaningful impact with big data — and how CIOs can make themselves legends throughout their organizations.

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The post How Big Data Can Help You Become a Legendary CIO appeared first on GoodData.

But retail isn’t a one-size-fits-all proposition, even if you bring in a CEO with gold stars on his report card. By slashing prices and bringing in hipster apparel, the new CEO lost the retailer almost $1 billion. Despite his former success, he failed to address Penney’s core market of budget shoppers who prefer form-forgiving clothing.

The Wisdom of Data

Think of how the situation would have been different if, rather than relying on intuition and dismissing data to the contrary, the Penney team looked at data to guide their plans. Knowing that their core audience prefers conservative clothing, Penney could have found appropriate new brands for shoppers. By reading the numbers of which types of pricing work best, Penney could have been more strategic about its discounting approach.

It should be a lesson for us all. Company leaders—CEOs and boards—should hold decision-makers accountable for making data driven decisions. Otherwise, the risk of a gut-based disaster like Penney’s becomes very real.

C-Suite Data Insights

It is not enough to simply say that decisions should be data driven. CEOs should hold each of her direct reports accountable for providing visibility into their piece of the data pie. No one should be making decisions based solely on intuition or gut. The CEO has to demand visibility into all aspects of the business, so she can make the best strategic decisions for the entire company.

The VP of Sales should provide visibility into the sales funnel for the quarter. In addition, he should be showing how this quarter is performing against the historical ramp, so problem with funnel can be identified early and action can be taken.

It is the CMO’s job to use data analysis to innovate new solutions for branding, company reputation, customer targeting and product pricing. Car insurance companies, for example, are delivering different rates to people who drive to the same destination, but use different routes to get there, according to Forbes.

The COO should be optimizing the company’s resources, from equipment to carbon-neutrality. Analytics make it possible to ensure that every moving part of the business is performing at benchmark levels and is in line with company budgets.

Using analytics, the CFO should discover new financial efficiencies across departments. He can trim the fat in an organization, reduce financial risk and use data to drive budget decisions.

Analytics provide a high level of detail about customers, and the systems driving this visibility are powerful and sometimes complex on the back-end. The CIO must ensure the health of all the IT systems behind the company’s big data analytics. If they’re in-house, the CIO should have a solid maintenance and tuning regimen; if analytics are SaaS-based, the CIO is in charge of setting them up and collaborating with service providers to ensure seamless execution.

The CEO has the Final Word

By analyzing massive quantities of data from diverse sources, analytics can be used to pinpoint trends and new opportunities that otherwise would never be discovered. It is up to the CEO to gather information from each team member and decide how data results should best be used to increase the company’s competitive edge. Armed with data insights, she can use the collective experience to make the best decision. But for this to happen, the CEO is on the hook to make data and data visibility an integral part of the company’s culture.

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As our awareness of the environmental problem increases, so do the capabilities of our technology. We could very well innovate our way out of the most dire environmental problems.

Big data is playing a promising role. Here are some of the areas where big data is making a difference:

Measuring the Ocean
Australia’s Integrated Marine Observing System (IMOS) collects information from a huge network of underwater sensors. Video cameras and measuring devices are mounted on vehicles, ocean observation stations, satellites and even on ocean animals. The sensors read any number of indicators, from species movements to ocean temperature, salinity and carbon storage. More than 300 researchers receive data from IMOS, resulting in the publication of 1,000 or more scientific studies to date, according to the Human Face of Big Data.

Mapping Forest Damage and Animal Habitat
The Carnegie Airborne Observatory monitors large-scale changes to land environments via spectroscopes and lasers mounted on a twin prop plane. This flying big-data gathering device has, so far, mapped the chemical composition of the Amazon rainforest to track carbon impact, discovered why different plant species filter chemicals and carbon the way they do, and more.\

Instant WILD, an iPhone app, sends images captured by cameras placed in remote, wild locations to people who have downloaded the app on their mobile devices. When people see animals on the app, they send a notification to the Zoological Society of London, which owns the app. This saves scientists hours of staring at video footage—and hastens the discovery of information about important animal species.

Bio-Engineering Fossil Fuel Replacements
An array of companies are developing potentially game-changing technologies like carbon capture and biofuels. Synthetic Genomics is using big data genome sequencing to custom-design organisms, such as bacteria and algae, to perform tasks such as energy production. These organisms could eventually replace fossil fuels.

Corporate Sustainability
Many companies already know how many gallons of water they consume a month, and how many tons of waste they produce. With big data analytics, businesses can take that knowledge a step further and use it to inform the timing and nature of more environmentally friendly decisions. A company could note, for example, that summers are growing hotter, leading to a greater air-conditioning expense for the foreseeable future. The company could make the decision to invest in becoming more energy-efficient sooner rather than later.

The Bottom Line
Measurement and analysis lead to new insights. Those insights lead to better decisions. That is the bottom line with big data. When it comes to the environment, we have a track record of poor decisions. It’s time to reverse that trend, and big data is poised to be a key player for years to come.

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Executives ignore this development at their own peril. Data skills are a key component to growing a business, keeping an organization healthy and even, increasingly, climbing the career ladder. In the not-too-distant future, those without analytics abilities will be left behind.

The good news is that regardless of specialization, there are a handful of general ways that any executive can apply data analytics to improve their results and job performance. Here are the five most common ones:

1. Justify the Need for Resources and Prove ROI

Need more staff or budget? Data analytics is one of the best ways to prove your need to other stakeholders. By using analytics to monitor and evaluate your part of the organization, you can draw conclusions about which activities are driving the most results, inside and outside of your department.

Historical data can show you what activities bring the most ROI, and therefore should be supported most. If you’re in sales, for example, you might discover that most of your closed sales started with happy hour meetings, so you could grow your happy hour budget. You can also compare historical and current data to find weak spots—and make the case that you need to build your bench to fill them in.

2. Plan for the Future

Scenario analysis built on models, forecasts and other types of data can help you understand the most likely outcomes of decisions. Predictive analytics can help you identify the most likely scenarios for hiring, capacity planning, budgets, equipment needs and other considerations. Instead of relying on guesswork and near-term intuition, actively plan for the future.

3. Recruit the Right People

Hiring managers have long understood that the best resume doesn’t necessarily make for the best worker. Data analytics are already helping fill the gap between a fancy resume and the right characteristics. Large national retailers, for example, have a huge hiring burden, according to The Economist. A retailer might have 1 million employees and an annual attrition rate of 50%, meaning the company must hire 500,000 people per year. They’re using data analytics to look beyond resumes and applications to pinpoint the important habits that make the best employees. The Economist named a couple of unexpected correlations:

• People who deliberately install a browser, such as Chrome, rather than using their machine’s default browser are assumed to make better employees, perhaps because they’re proactive.

• For certain jobs, such as call center support, people with criminal records tend to perform better than those without.

Data analytics, in other words, adds new richness to the hiring of new workers.

4. Compensate and Reward Your Employees

Data can also simplify the process of identifying the best performers. Data brings a new level of granularity to the process. You might already know who your best closers are, but data might point to the person most responsible for filling the pipeline. If one of your helpdesk staff has the ability to answer her tickets the first time, she has a bigger impact on the business, because she’s not only more efficient than other workers, but is sending a positive brand association through every thorough answer. With data analytics, pinpointing such performers becomes easy, and it’s also possible to calculate financial rewards based on the real impact they have on the business.

5. Make the Right Decisions at the Right Time

Data analytics bolsters the probability of delivering the right service or information at the right time. Is it the right time to give your star performer a raise? Do you want to include an add-on service for that prospect you’re pitching, and should you contact them on Tuesday or Wednesday? What specific information should you include in your campaign geared at retired tech CEOs living in San Luis Obispo? Data analytics are important not just for giving you the right information, but for enabling you to execute at the best possible time.

All in all, big data analytics are your friend. Use them wisely and use them well. Also know that not all data analytics platforms are created equal—if it’s too complicated to understand, consider finding a platform that you can work with.

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