360Science is a data quality company specializing in matching, deduping, unifying, linking and verifying contact and business data. Using purpose-built Artificial Intelligence, proprietary phonetic and fuzzy matching algorithms, context-sensitive lexicons, and a contextual scoring engine, 360Science defeats the errors, inconsistencies and challenges commonly found in customer data.

One of our principal tenets is “All Your Data, All Your People.” This means weaving accessibility throughout our products both in terms of ease-of-use and connectivity. We’ve engineered our tools to be powerful and easy to use for all types of users across an organization. A simple way of connecting to the data that matters comes with the territory.

Over the last several months we’ve observed increasing requests from our users asking for an integration with Snowflake. This trend isn’t all that surprising given Snowflake’s focus on empowering people to make data-driven business decisions with quick and easy access to a single trusted source of data. An integration with 360Science could take that trust to a whole new level. So we built it.

Now, users can easily access the data in Snowflake using the world’s most powerful and easy to use matching engine with 360Science’s Cortex. Have you ever wanted to quickly and easily:

• match data from a variety of feeds and sources to data in Snowflake?
• dedupe data in Snowflake?
• link records across multiple tables in Snowflake?

Here are a few reasons why you might want to take a closer look at Cortex’s Snowflake integration.

1. 360Science and Snowflake are both focused on improving time to intelligence and increasing the quality of that intelligence. 360Science + Snowflake means faster analyst access to data and a higher quality of data for querying.
2. Snowflake believes DBAs should be spending more time on high value activities and less time on costly wastes like preprocessing. With Cortex, we handle normalization and standardization for you, so you can bring your data as it is and get to answers faster.
3. Just like Snowflake, we believe performance at scale is critical. Data volumes are growing alongside the number of questions a business needs to ask to make informed and strategic decisions. Cortex uses in-memory processing making it much faster than traditional solutions. Whether you’re dealing with 100,000 records or a billion, we can help you find answers quickly.
4. Accelerate analytics and BI for all users. Yes, yes, and yes. Democratizing data alone isn’t enough to empower everyone to make data driven decisions. The tools used to query, process, and enhance data must be powerful enough for a Data Scientist while also being so intuitive an inexperienced knowledge worker can pick it up and use it. Cortex combines a drag and drop workflow canvas with Smart Settings making it simple for everyone to achieve outstanding match results.

The customer feedback loop serves as a guiding light when we’re deciding where to go with our product roadmap. The data quality space evolves rapidly and we’re fortunate that we can be extremely agile when it comes to adding high demand features. We’re tremendously excited at what this integration means for our customers and look forward to enhancing Cortex with additional integrations very soon. 

Want to know more? Read the press release on 360Science, here.

The post Cleaning the Cloud: 360Science Announces Snowflake Integration appeared first on Think360.

It’s a good problem to have, really – the business is expanding, customer volumes are increasing, interaction and engagement are thriving, and revenue grows. 

But along with the positives of taking on more and more customers are certain growing pains. More collections points – eCommerce, email, social media, apps, and more – equals greater volume and variety in the kind of data you need to digest before you can make use of it. 

As the business scales and you take on increasingly more customer data, don’t get blindsided by some of the common challenges faced when data grows.

Top 3 Challenges in Scaling Customer Data Management

Consider this: if you could process large volumes of data in minutes instead of hours or days, what would need to change? The frequency in which you investigate? The speed at which decisions can be made? The questions being asked?

1. Getting answers fast enough

Speed is a challenge for multiple reasons. Adding new customer data platforms and tools can take a long time to get fully onboarded, let alone until realizing value. Once they’re are in place, processing speed can mean waiting hours or days for data. And the accessibility and intuitiveness of the platform has a major impact on speed when it comes to actually utilizing that data to extract insights.

2. Fractured Identities

Customers engage across multiple channels, online and offline, resulting in a different identifier each time. Without a common key to link these interactions, it’s impossible to be sure that they all apply to the same person. That means no unified customer view, which in turn means mistakes in segmentation, insights, and personalization. This is detrimental to the customer experience and as a result, bad for business.

4. Siloed Data

Not only is customer data fragmented, but the different bits are typically stored in their own systems — email, loyalty programs, digital engagements, on-site, etc. — and these systems don’t talk to one another. Manual attempts to connect them are notoriously time consuming and fragile, often falling apart when changes are introduced. As companies add new customers and new channels, the problem only grows more complex.

How to Maintain Large Volumes of Customer Data

Businesses are increasingly taking on more integrations and relying on AI-powered automations to perform background tasks, adding to the complexity of managing large volumes of data.

1. Make your data accessible

It can be a challenge to collect the right data from diverse sources, and an even bigger challenge to do so at the speed your business demands.

2. Look for solutions capable of managing larger data sets

Whether on-site or in the cloud, look for data quality and other customer data tools that are built to support scaling massive data sets. Integrations with leaders in data management solutions like Snowflake, Spark, and Alteryx indicate a commitment to large-scale data and complex use cases.

3. Stop bad data before it enters your system

Scaling with your customer data isn’t just about making more room for data, but keeping that data clean and error-free. That means embedding data quality checks in every step of the customer journey, like AI-powered data quality functions that verify accounts at form submission or validating addresses at checkout. So the data you have is exactly the data you need.

4. Remove unnecessary data prep

One step to simplifying large-scale customer data management is removing unnecessary tasks wherever possible, and automating anything that can be. Data prep work like normalizing and standardizing customer data from various sources/schema will be seamless with a solution which integrates these steps effectively into the workflow. By removing as much of the inherent burden of managing customer data as possible, IT and data engineers have more time for other projects.

To recap: what goes into a truly scalable customer data solution?

Optimizing your customer data strategy can surely introduce its fair share of challenges. Contact data solutions need to be as nimble as the strategy pulling in that data.

But sometimes the best solution doesn’t have to be the most complicated. Leveraging future-proof technologies means considering all the ways your workflow has evolved along with the customer data you manage – and considering that a different way is possible.

Keep it simple – when you pull back focus, there’s really only 5 key points that you need when it comes to customer data solutions. Save this as a checklist next time you’re evaluating a potential partner to make sure they’re the right fit for you and your data.

1. Flexibility

Customization available at every step to work with your existing infrastructure and ensure you’ll never outgrow the platform.

2. Scale

Able to process billions of records quickly, no matter the source, so no data goes unused.

3. Speed

Ingesting data and querying it on the spot, with real-time validation so customers are never left hanging.

4. Interoperability

Fast, easy, comprehensive ways to get data in from anywhere and out to any system, so you’re never held hostage by one product or suite of tools.

5. Quality

Above all, accurate and error-free data is the goal. Effective data cleansing can make your database really trustworthy, but done incorrectly will be detrimental to your working day, your corporate and personal goals, and your customers.

The post Overcoming the Challenges of Customer Data appeared first on Think360.

In the wake of a global pandemic, companies are ramping up investment in data and analytics in order to better respond to the change and uncertainty facing us in 2021.

When asked, “Which will be the top 3 game-changer technologies for your industry to emerge from the COVID-19 crisis?”, 36% of company boards placed data and analytics as top priority in 2021,  ahead of artificial intelligence (AI) in 2^nd place at 24%, in a report recently released by Gartner.

At a time when the future can be hard to predict, organizations are recognizing the need for data always on tap that they can trust to make strategic decisions. 

As we witnessed in 2020, the ability for businesses to adapt and pivot virtually overnight is fundamental to maintaining growth even in the most challenging of times. Even prior to the pandemic, an undeniable shift towards data democratization has been under way. Top tech leaders are utilizing accessible, accurate data and analytics to provide the answers needed for the next wave of customer demand and drive sustainable value. 

How Organizations are Staying Competitive in 2021 with Data & Analytics: 

1. Total Experience Strategy: The 360-Customer View

Never has accurate, timely information been more vital: in the wake of COVID-19, smart organizations are using analytics-ready customer data and AI to get individuals the answers they need, and fast. Using advancements in data quality, organizations are delivering these answers at scale. Data quality solutions at the point-of-entry respond to inputs in real-time and prevent inaccurate information from ever coming in, and customers no longer need to worry about missing out on critical and timely messages due to out-of-date and inconsistent contact details across multiple accounts. 

2. “Hyperautomation”: Streamlining Workflows Wherever Responsible

In a time of social distancing and lockdowns, accurately uniting the customer experience across virtual and in-person touchpoints has never been more vital. From healthcare to retail, organizations are forced to look for new ways to securely and safely interact with individuals, households, third parties, and vendors, while ensuring that communication is relevant and timely to the recipient. 

But sometimes automating the customer journey means looking at streamlining internal workflows rather than customer-facing ones first. Accessible data solutions that allow teams to easily save and share repeatable workflows alleviate strain from overworked staff that – these days – are often spread out over remote locations.

3. Anywhere Analysis: Enabling Access to Data Everywhere, Anywhere

“By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs. “ – Gartner Top Priorities for IT: 2021

In response to a global shift in priorities, tech leaders are looking to arm employees with insight-ready data to make informed decisions faster. But as Gartner notes, “Data literacy is not about turning everyone into a data scientist.” Employees are being empowered to utilize complex data from across the business without the need for highly specialized skills. Organizations that prioritize and enable easier access to high quality customer data in 2021 will better position themselves to adapt quicker to new opportunities.

4. Future-Ready Infrastructure: Pave the Way for AI and Machine Learning Opportunities

When it comes to AI and machine learning (ML), the heat is on: According to Gartner, Boards of directors place data/analytics and AI as the No. 1 and No. 2 priorities in the year ahead. In the wake of the pandemic, AI and ML have proved critical for massive corporate overhaul and operational realignment when new demand patterns emerge. 

Breaking down data silos is a key component of achieving a holistic view of the customer and an essential part of building out databases primed for AI and machine learning initiatives. New modes of integrating and disseminating data are accelerating AI and ML-driven analysis, resulting in more scalable solutions with higher business impact.

The past year has disrupted the way customers and businesses alike consider and conduct their lives, and the global economy has had to adapt in unforeseen ways. But, true to form, when faced with adversity, the world has made dramatic shifts seemingly overnight and helped us all consider a different type of future. Priorities are continuously being reset, and consumers, entrepreneurs, and enterprises alike have suddenly found themselves looking at where to invest their resources in a very new way. The things we deemed important in 2019 have receded in the wake of 2020. 

In an unprecedented time, the same old strategies just won’t do. Those that are able to embrace a new wave of thinking about how to utilize data and analytics, rather than force-feed users and customers with what’s no longer working, will be the ones to thrive in a time where we’re all looking for answers. 

The post Staying Competitive in 2021: Focus on Data & Analytics appeared first on Think360.

A name is a person, but the person is more than their name — just ask Taylor Swift. He’s a 29-year-old photographer from Seattle.

What is name matching?

Name matching is about more than just the person’s name.

Across industries and organizations, the volume of consumer profile data collected is growing at an exponential rate — and most companies rely on this data day in and day out.  With the sheer volume of records being entered and re-entered by businesses across systems, how do companies ensure that contact data is accurately and correctly matched? Between spelling variations, misinterpretations, lack of address standardization, and cultural differences, there are a million ways that contact data can be corrupted, wrongly matched, or duplicated.

To solve for these unique challenges, we must first understand the complexities of contact data. 360Science attempts to do just that in a new whitepaper, “Understanding The Complexity of Name Matching,” by explaining what it is that makes name matching is such a unique challenge—one that is only becoming more complex with time—and offering solutions to some of the most common contact data challenges. The following are some highlights from the paper, offering insight into the contact data conundrum along with some recommended measures to ensure ongoing data cleanliness.

Why is name matching important?

• CRM and customer data are unique — and matching people data is more complex than other form data matching.

There are several ways that contact data can be skewed. Whether it’s a press of the wrong key, lazy input, a lack of standardization, or someone’s information heard wrong, these mistakes cause system-wide errors and issues. The challenge is made clear by simple questions like this one. Do you agree or disagree that these two records are the same person?

• Elizabeth A. Herrera, 7800 Beverly Ave Suite 300, Wilshire La Brea, CA 90037 and
• Betty Hereira, 7800 Beverlie Blvd, Los Angeles CA 9003

Think about how you made your decision. First you’d look at the first and last names separately and together, then each individual part of the address and the address as a whole, and then need to assess the name and address together, before making your binary yes-no decision.

Sure, this may only take a second for one entry. But as author and 360Science CEO, Rob Heidenreich, points out, “Now think about repeating that decision scanning a 1,000 record spreadsheet, a 10,000 record marketing list, a 100,000 record CRM Database, or a 300 million record data store. Mind you, that even a small 1,000 record spreadsheet equates to 5-million time consuming comparisons, checking every record against the each other.”

• Increasing globalization only exacerbates this contact data matching issue.

As we all know, different cultures do certain things differently, and this can include spelling, first and last name order, and other contact information. Because there are no global standards that dictate how a person’s contact information should be entered into a database, it’s difficult to match that contact information across systems and entries. Heidenreich states that “These variations of global names and westernization from the non-English speaking world impose major challenges for a name matching as the variations are not isolated.”

• Address standardization simply doesn’t achieve what you think it does, making the contact matching issue even more difficult to solve.

In the white paper, Heidenreich says that “there is no such thing as achieving standardization with address data.” He uses the US Postal Services as an example of why this is true: “The CASS certified software can ‘validate addresses to the delivery point and verify that an address is deliverable’. It said nothing about making certain it is the same every time. The USPS primary concern is not about standardization – they care about deliverability.” When checking the USPS database for the address of a City Hall in a Texas suburb, for example, you’ll get 6 different city names returned as totally acceptable for that same address. Adds Heidenreich, “To be clear — address validation is a necessary component of data quality — but you have to understand it’s limitations.”

• Despite its complexity, contact data matching is a problem that technologies must work to solve.

Heidenreich wraps up the white paper by stating that “Contact data matching is uniquely difficult, and the use of contact data is exploding. From marketing to homeland security, the underlying technologies for data matching logic must support these unique challenges in our ever evolving world.” Around the world, businesses’ reliance on accurate contact data will only increase exponentially. Technologies able to simplify matching will help alleviate a real, major pain point—transforming businesses and saving them millions in the process.

To learn more about the unique complexities of name matching, along with tips to address these challenges and clean up your database once and for all, download the full whitepaper now.

How to Effectively Match Names in Contact Data 

Featured resource: The Complexity of Name Matching eBook

To effectively match contact data, you need to understand the context of the full contact record – not just a single field. We have to talk about the whole contact record.

CRM and customer data is unique – and matching people data is more complex than other form data matching. By the time a contact record is received in the database, it’s usually corrupted in numerous ways, whether by lack of standards, miskeyed data, hearing errors or lazy input. The entry of that data is a collection of disparate data sources from various systems, and data collection methods resulting in a host of errors and issues.

Learn more about how various algorithms and solutions tackle name matching and why. Download The Complexity of Name Matching, a free resource from 360Science for a deeper dive.

The post Understanding The Complexity of Accurate Name Matching appeared first on Think360.

The Science Behind the 360^o View of the Customer.

helpIT Systems isn’t just sporting a brand new logo and an entirely new look. Today, we’re celebrating the next phase in our journey – as 360Science. This marks an important milestone in our company’s history, as we move to better reflect our continued commitment to our client’s data quality. Meet 360Science.

360Science means the science behind the 360-degree view of the customer – providing data-driven organisations with the means of maintaining a high degree of accuracy regardless of where the data lives or is entered.

The evolution of helpIT to 360Science is a recognition of the need for “democratisation” of Data Quality. This means providing users across the enterprise with the tools to access up-to-the-minute clean and accurate data. At the heart of 360Science remains the matching engine on which all our deployments have been built, matchit. Watch the Video to Learn More.

But there’s more. 

We’re also releasing two exciting new products: Cortex and Verify.

Cortex provides a powerful yet accessible workstation solution with shareable, repeatable workflows that meets the needs of anyone from data scientists to users across the business. Watch the demo video here.

Verify is 360Science’s ground-breaking address verification solution. Powered by matchit, the same matching engine at the heart of all our products, Verify is able to deliver better accuracy and PAF match rates than ever seen before, with superior performance and the same top-notch support that you expect from us.

Some Things Stay the Same

Behind the new name, 360Science is still the same dedicated team providing you the best possible solutions in contact data matching and preparation. Our VAT number, company registration number and ownership remain the same and our licence and support agreements continue unaffected.

We’re excited at the opportunity to expand upon our offerings and to continue to grow alongside the partners and clients who have joined us along the way.

Steve Tootill | CEO

The post helpIT Systems is now 360Science appeared first on Think360.

Customer data is often a brand’s most valuable resource. Frequently thought of as the “oil of the digital era”, clean, reliable customer data is the key to forming a relationship that extends beyond that of your competitors. It’s the lifeblood of a company, but cleaning dirty customer data and maintaining data hygiene can be a costly and complex task. 

Despite the undeniable value of amassing volumes of subscribers, records, and contacts, examples of mismanaged and poor quality customer data surrounds us every day. Enterprise companies such as Yahoo, Under Armour, eBay, and Uber have all experienced data breaches that have damaged reputations and cost millions in settlements. 

Large volumes of data are cumbersome to manage at the best of times. And customer data can be downright impossible to manage without the right strategy in place. 

Now throw in the threat of increasing regulations and penalties for when it is mishandled, and quality data is suddenly less of a luxury and more of a necessity. 

Due to the dynamic nature of customer data, maintaining the quality of this data can feel a little like chasing your own tail. Regular data cleansing, and a sound strategy behind it, suppresses or modifies data that is incorrect, incomplete, irrelevant, or improperly formatted and is fundamental when working with customer data.

3 Steps to Cleaning Customer Data

An ongoing data quality strategy should include processes for cleansing existing data, merging accounts, removing duplicates, and establishing a benchmark in data quality. Creating a repeatable process ensures that accurate and timely data is maintained and minimizes the need for manual, time-intensive processes. 

1. Standardize customer accounts across multiple CRMs and databases

Before anything can be done with your data, the first step is to ensure its accuracy. To clean existing records, contact data can be “scrubbed” with record handling software that corrects, normalizes, applies standardization to customer details such as address, email, or phone number. Seamless record linking strategies will take on this step inherently in order to ingest the volume and variety of enterprise-level data.

Do this: At this stage, keep an eye out for gaps or inconsistencies in customer information that will help you in merging records and preventing future challenges.

2. Merge and unify business accounts & customer records

Businesses today create and depend upon large volumes of data, and each department likely depends on their own segment of data integral for their field. As a result, data within a company inevitably becomes siloed and disparate, and merging quickly becomes complex due to the variety of databases, file formats, structure, schema, and outdated records. 

And while joining various datasets seems like a fairly straightforward task at first glance, innumerable inconsistencies and challenges with customer data can make it a challenging one to fully automate. 

To ensure a complete, 360-view of the customer—one that is accessible across the organization—software for maintaining quality data must be able to analyze and match with human-like perception so you don’t need to comb through results line-by-line.

To create an even more powerful view of your customer, ideal solutions should apply a hybrid of record matching algorithms, like that of matchit, that mimics an expert human user to add real-time information to existing customer profiles. An intuitive solution like matchit won’t require data that is cleansed, verified, or correctly formatted. However, downstream processes and users may benefit from standardized outputs. Look for a solution that offers a built-in normalization tool to do the heavy lifting in these situations and save on hours, if not days of pre-processing.

3. Prevent duplicate records at point-of-entry

Cleansing and wrangling data is an integral part of maintaining a clean CRM, but point-of-entry implementations are integral to keep duplicate records out. Without preventative measures to check dirty data at the door, that freshly-cleaned data will quickly decay to the tune of 30% per year. 

Fully embracing data quality means more than simply a one-and-go deduplication process – but an ongoing data cleansing strategy likely involves revisiting legacy technology or systems that can better process and analyze incoming data at scale. 

If new systems are required, look for solutions that make data management accessible to everyone, regardless of coding experience, in order to empower and educate users across the business.

As companies look to acquire more automated, AI-driven solutions, customer data is being held to a level of quality previously thought to be unattainable. Now, this optimized, squeaky clean and regulatory compliant level of quality is becoming the expected standard as stringent policies such as GDPR become the norm.

With the right data cleansing strategy in place, enterprises can better lean on the customer data they already own to adapt rapidly-evolving technology at scale, foster new growth and operations within the business, and enhance customer relationships.

The post How to use Data Cleansing to Maintain Customer Data appeared first on Think360.

When it comes to managing Big Data, it’s hard to picture a solution that can effectively manage the massive quantities and variations of customer and business data, much less in a way that actually alleviates stress instead of compounding it. 

Yet while complex, those endless tables of alphanumeric information contain key data points that, when properly joined and identified, can reveal the kind of trends, patterns, and insights that drive nearly limitless revenue. 

To truly harness this revenue, the accuracy of that data must be matched by its accessibility. Translation: your customer data is only as strong as your employee’s access to it. Consolidating various touch points into one database allows employees to see an entire customer journey and deliver a more personalized experience. 

Therefore, a reliable, investment-worthy solution should include a fast and accessible way for users across the business to work confidently with their data. But what else goes into data matching software? So you can feel confident about the software you invest in, ask potential data matching partners the below questions – and be wary of anyone who is hesitant to give you a thorough answer.

Considerations for Evaluating Data Matching Software 

Let’s dive into the top nine questions we frequently here from customers regarding data matching:

1. Is the software able to ingest any data source while still analyzing the data accurately? 

The most effective solution will not only perform out of the box but will also be adaptable and expand to incorporate any format of data, regardless of your brand or industry. 

Comprehensive analysis of customer or business data, including names, addresses, phone numbers, and contact information no matter the country, should be able to ingest a variety of data. Consider all your customer touchpoints in order to create a 360-view: documents, web pages, databases, and all social media will need to be considered. 

2. How many languages does the data matching solution cover? 

The most reliable results can only come through broad multi-language capability. Ask particularly for transliteration expertise when it comes to challenging languages (i.e. Arabic, Chinese, Korean, Russian – even Klingon!) and how they are considered.

3. How thoroughly does the data matching solution consider possible record matches?

You want a product able to identify possible matches by the same means that humans do – contextually. A truly intelligent solution should consider names, nicknames, initials, as well as any possible misspellings, typos, or misalignment – regardless of language.

4. Does the data matching solution rate the reliability of each potential record match? 

The most useful and thorough contact matching and verification solutions include multiple ways of analyzing and breaking down the match rate. These features give you a larger understanding of how to fine-tune the matching process for your needs while rating the accuracy of your matches.

5. Can the matching software identify links between records with phonetic typos – like knowing the difference between “Knotten” and “Naughton”?

Your solution needs to interpret records contextually so it can connect the right record to the real-world people and things they represent, even taking into account phonetic variations and possible misspelling of the same name.

6. If my needs require multiple products or software integrations, do they work together – and how?
A suite of products designed to seamlessly integrate is more likely to provide greater synergy and consistency, and higher quality analytics than if you combine standalone products. 

7. Are there customization options that can be easily scaled with growing customer data over time? 

The most responsive and cost-effective solution will meet your immediate needs, add additional features as required; grow with your future needs; and handle massive and complex datasets with efficiency and accuracy. 

8. How does the matching solution fit into my existing infrastructure? 

Look for data matching software that can be layered onto existing infrastructure and major CRM platforms. Modular solutions that fit into existing core systems provide flexibility and scalability in terms of future growth. 

9. Is this data quality solution built from the ground up specifically to handle the nuances of customer and business data?

When not just data quality, but customer data is a primary focus of a company, the product is continually developing, improving, and expanding its capability to account for the ever-evolving changes in human language, processing, and technology.

Finding the Best Data Solution for Your Business

No matter what you land on, remember: be cautious, demanding, and ready. 

This software is going to be in charge of cleaning and deduplicating your company’s most valuable resource – its data! Take your time, be cautious, discerning, and make sure the partner you choose understands your unique use case and isn’t over-promising something they can’t deliver on. 

This is where part two comes into play: be demanding. Ask the questions above and make sure partners take you through comprehensive reviews that demonstrate the software’s ability to handle complex and massive datasets without sacrificing accurate results. 

Last but not least, be ready. Nothing is worse than spending the time and investment on software only to outgrow it in a year. The right solution could be the last data matching software you ever need to onboard, if you go about it right, so make sure scalability and interoperability are baked in.

The post What to Look for in a Data Matching Solution appeared first on Think360.

It’s time we talked about it. Enough’s enough. I mean, really, why are we settling for less? When it comes to our data, there’s no reason we should be struggling with the same old issues: slow time-to-answers, a complex UX, endless wrangling, all for inaccurate results that require manual review. 

It’s true – legacy data matching solutions often fail to deliver accurate results without sacrificing the rest: in other words, delivering on velocity without forgoing value. 

Conventional approaches don’t provide the kind of intelligent results that are truly accurate. What’s more, they’re often bloated platforms loaded with superfluous features. Instead of being helpful, they’re not user-friendly, and they’re hard to train, maintain, and scale. 

And while vast improvements have been made in the way of customer data management, maintaining the quality of that data has been missing an accurate, usable solution that brings data management to any business user’s fingers and decrease time-to-insights.

…Up until now, that is. 

What’s wrong with data matching solutions today

When it comes to dealing with customer and business data, conventional solutions aren’t equipped to fully account for this kind of complexity. Instead, they’ve left a lot of room for growth in terms of efficiency, scale, and match rate. 

Before 360Science, data quality solutions:

• Weren’t user-friendly: Cumbersome interfaces that require programming skills and a background in matching algorithms aren’t user friendly, and they certainly don’t empower anyone to tackle the big questions. Business users are more likely to turn to Excel spreadsheets for answers than deal with a confusing software.
• Didn’t grow with the business: The rate of technological advancements is fantastic, unless you’ve just adopted a piece of software that’s already out-of-date. Enterprises manage more diverse amounts of data than ever before, and solutions need to be able to adapt to this quickly changing marketplace. 
• Not equipped for Big Data: Legacy solutions may be just fine for small datasets, but they show their age when trying to deal with tens to hundreds of millions of records. Enterprises are already busy enough juggling the volume, veracity, and variety of their data without having to worry if their system can keep up.
• Delivered inaccurate, unreliable results: It wasn’t long ago that a deterministic approach was used solely to match data. This approach isolates only the ‘certain’ data and severely increases false matches.
• And access to data was still siloed: Legacy approaches kept data management largely in the hands of a developer, programmer, or IT employee. Even a simple merge between disparate datasets meant researching what schema each department was currently using, and writing lengthy conversions, cleaning, and transformation rules. No one else in the organization knew how to do this kind of painstaking wrangling (and frankly, no one else would want to).

…and it comes at a high price.

With customer data, it’s highly likely that the same customer has records in multiple databases with incomplete, imprecise, or contradictory data. As a result, we often have a limited understanding of our customers, preventing us from building the kind of customer experiences that deepen relationships and drive revenue. 

Throw in the sheer volume of customer data generated on a daily basis, new software and new formats of data, and maintaining the quality of customer data seems downright impossible.

At first glance, it may seem like all data matching solutions produce the same results, but this couldn’t be further from the truth. Behind every platform is an approach that can ultimately make or break your data quality. In the end, you’ll want a matching solution that intelligently analyzes your data with human-like perception and can do so at scale.

What to really look for in a data matching solution

The word “intelligence” gets thrown around generously these days. Some of what brands are calling “artificial intelligence” are in fact narrow intelligence at best. Scour their site and you’ll be hard-pressed to find exactly what “AI” actually means.

Except that with data matching it should be exceptionally clear. “Intelligence” means more than intuitive design and enterprise-ready controls; via proprietary approaches, powerful performance, and a scalable architecture, an intelligent data matching solution interacts with your data and revolutionizes how users perform matching tasks. 

Algorithms Designed for Matching

Traditional matching algorithms are not designed to handle the uncertainty inherent to customer data, nor are they good at combining that uncertain data to provide answers. An intelligent matching solution doesn’t just rely on out-of-the-box deterministic or fuzzy matching algorithms to handle the intricacies of customer data – they develop a proprietary means that push the envelope and take accuracy much further. 

Performance Power that Scales

Think of it this way: if you’re selecting a data matching solution, shouldn’t the engine that powers it be a little more “muscle car”, and a little less “unicycle”? We’re talking a unique scoring engine backed by massive computing power to resolve customer records, with configurations refined and customized to ensure the best match quality for your customer data. This approach surpasses deterministic and fuzzy matching and provides unparalleled match rates at scale, even when the data systems don’t have exact linking keys. 

Future-Ready Scale

The platforms that manage your data needs to be able to grow with the business and the increasing amount of information it’s bringing in. A future-ready approach is ready to take on with growing volume and variety of customer data, and can get you there without burning through endless man-hours or resources. 

User-Empowering UI

In this day in age, we should be seeking to make data just as accessible to business users as it is to engineers. Be wary of platforms that try to lure you in with flashy, pretty interfaces, and looking for the kind of deep configuration that your data scientists are accustomed to. When out-of-the-box defaults aren’t enough, you’ll need solutions that put the control back in the hands of the user with deep configuration settings. Drag-and-drop canvases speed up the process and let anyone save and perform complex jobs without breaking a sweat. 

Faster Time to Answers

Truth be told, it’s not one but a handful of traits that truly make up a blazing-fast solution. Innovations in performance and UX have brought new meaning to the term “speed”,  delivering answers in minutes as opposed to hours (or days!) with competing solutions or traditional algorithms for customer data unification.

1. Raw data invited: Intelligent solutions let you bring your data as-is, raw, dirty, and without an ounce of wrangling required. 
2. Code-friendly or code-free: These days, high-performance UI’s allow anyone to safely match and dedupe data without extensive background in Python or Soundex. Look for solutions that offer both code-free and code-friendly options to maximize control. 
3. Optimized performance: Innovative matching solutions today are harnessing in-memory and multi-threaded processing to deliver scalable efficiency. Translation: enterprise matching jobs are tackled in minutes.

So what’s in store for the next generation of data quality solutions? Personally, I’m interested to see what this new generation of business users will do now that they finally have all this data within reach. This is the first time in history anyone can access an intelligent matching solution and perform complex jobs with ease. It’ll be interesting to see where we go from here, and what is possible with that kind of access.

It’s an exciting time for data quality solutions. We’re finally moving past the legacy solutions of the past and challenging platforms with new advancements in technology. These advancements have opened up the gateway for innovation for those who are looking for it – and know how to use it.

The post What to Look for in “AI-Powered” Data Quality Platforms appeared first on Think360.

Multi-channel, cross-channel, integrated, digital: whatever you call it, organizations today needs to connect to their audience wherever, whenever they are — and they’re everywhere.

To complicate it further, the more you interact with consumers on different channels, with different departments, at different stages of the buying process, the harder and harder it becomes to accurately say who it is you’re actually talking to.

When you interact with your audience in lots of ways in lots of places, you end up with hundreds of customer identifiers representing just a single, real-life human being. 

That’s because the person you know as Becky Smith is also known as bsmith@somemail.com, or R. Smyth (according to your customer service team), or Rebecca Smyth at X Data Lane (your third-party data provider). When your contacts are interacting with you on multiple channels, it’s easy to make the wrong connections between data points or miss them altogether.

This means you could be overlooking valuable audience segments, duplicating efforts, or incorrectly targeting customers with a confusing and costly experience. 

You are not alone. According to Gartner, over 90% of marketers have difficulty connecting more than three channels in the buyer’s journey. When was the last time you connected with less than three channels from any brand before spending money with them? 

This fragmentation is potentially the biggest obstacle keeping brands from achieving true people-based marketing: that is, speaking to real individuals on a real level. 

The good news? It’s relatively easy to fix—if you know where to look. 

What is Data Unification?

Data unification is essentially merging customer records pulled from various channels, platforms, devices, and more, into a single, consolidated view, so you can tie them all up into the same person in a reliable way. We like to think of it as a single source of truth that gives you complete trust in your data.

The threat of a fragmented database is always present. If you’re like many marketers and looking to supplement the data you already own with new information from additional first, second, and third-party sources, you run the risk of muddying up your data with duplicates and mismatched records. And to add even more confusion; 60% of marketers are looking to increase the number of technology vendors they work with. 

While customer records stay static, real people have a pesky habit of moving around, changing their tastes, and just, well, getting on with their lives. This makes it hard to keep profiles up-to-date and accurate, so you need to stay on top of refreshing that data as close to real-time as possible. 

Connecting the Dots with Data Unification

Marketers who don’t work with data matching partners report that they find it three times more difficult to use offline data to target and personalize. And they’re nearly three times less confident at recognizing consumers in digital channels.

Data unification enables marketers to convey the right message to the right user at the right time in the buyer’s journey.

6 Ways to Improve the Customer Experience with Data

1. Take the Guesswork Out of the Game

Scattered customer records, be gone! Accurate matching gives you detailed insights into consumer habits, preferences, purchases, and so on by unifying and linking available data to a single, unified record.

You’ll also have a better understanding of the impact of marketing campaigns, lay the foundations for better segmentation, and make strategic, educated plans for future marketing activities. Let unbreakable trust in your data bring you new inspiration: never again will you have to throw out the data and “go with your gut” when insights fail to make sense.

2. Streamline Marketing Messaging

A customer could have signed up for your marketing messaging multiple ways, and without properly merging and de-duplicating customer contact data on a regular basis, you inevitably build up duplicate profiles. As a result, your customers receive the same messages numerous times, leading to confusion, distrust, frustration, unsubscribes, and potentially spam blocking.

With data matching and unification, you can merge or delete the duplicates and maintain a single,  unified record, streamlining the messaging process for both you and your customers.

3. Track Users Through the Buyer’s Journey

As you get an accurate picture of your customers, you get a 360-view of their buying habits: how they prefer to interact with your brand, on what device, with what search terms, for what reasons. This unified view allows you to deliver superior customer experiences on an individualized level. 

Personalized deals and content can be automatically triggered based on the stage of the funnel the user is in. And because these campaigns are often instantaneously triggered in real-time, you need to be able to count on the accuracy of your data 100% of the time. 

5. Pave the Way for Impressive Advanced Automation

As SaaS platforms get increasingly complex today, fragmented data means you may not be using your expensive software to its fullest potential. With reliable, enriched insights, marketing systems can develop highly personalized, triggered, and automated messaging to customers with minimal effort and maximum reward for marketers. 

6. Deeper Customer Insights

Data matching allows employees to access or accurately build a unified customer profile, pulled from multiple data sources and interactions without the need for advanced skillsets. These complete and granular customer profiles provide rich insights that enhance retargeting campaigns, cross-sell and up-sell efforts, and drive meaningful, relevant interactions for acquisitions.

Not only will you get more accurate analysis derived from the data as a whole, but individual records will contain a more complete view of the customer, so you can accurately act on things like average lifetime value, purchasing history, and behavioral patterns. 

The Ongoing Customer Journey

Organizations and their employees today often find themselves drowning in data, and yet starving for actionable information. Customer data is growing ever more complex – but there’s good news. 

With data unification, individuals of nearly any educational background are finally gaining easier access to these valuable insights. Juggling multiple datasets and confusing algorithms is finally a thing of the past. Finally, employees are gaining access to a more efficient way to perform these tasks without the need for a data scientist to glean meaningful insights from their own data.

Today’s top-tier marketer is experiencing never-before-seen ROI, not from acquiring or enriching their data with new insights, but making the data they already own more accessible, cleaner, and reliable with data unification.

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The data quality role is once again ranked as the most important full-time role staffed in the CDO’s office. But despite the importance CDOs place on data quality, little has been done to actually solve the issue. 

Data quality has always been perceived by organizations as a difficult thing to achieve. In the past, the general opinion has been that achieving better data quality is “too lengthy” and “complicated.” 

There are many assumptions and opinions that disrupt the understanding of data quality strategy and what is required to implement, maintain, and succeed with it. To truly understand what is standing in the way of succeeding in a digital world, we’ve gathered some of the most common misconceptions plaguing digital companies today. 

The Top Three Myths about Data Quality

Myth #1: It’s a one-time affair

Fact: Data quality is a journey, not a destination.

Up to twenty percent of contact data is flawed at point of entry. Even if you spent a year cleaning your database and deduping records, a good percentage of those records will inevitably be out-of-date. 

Individuals change jobs, companies change names, families move; the velocity at which your data changes is staggering. To get ahead, the focus needs to be on real-time data quality and ongoing maintenance. When left untouched and without regular maintenance, that data will naturally degrade by another twenty-two and a half percent over the course of a year.

Data quality is nothing like the collective project that goes from an idea to the end product. It is an ongoing process that requires effort and investment. Being “digital” as an organization means an ongoing search for new ways to optimize business models, provide even better customer experience, and explore new business vectors.

Myth #2: Any data quality software will do 

Fact: Success in data quality is directly connected to the budget allocated

As we have previously written about, a correlation exists between data accuracy effectiveness and the amount of budget allocated to the discipline. As businesses continue to prioritize customer experience and innovation, they’re looking to increasing technology budgets to support that growth. Globally, technology leaders expect to increase budgets by 52% in the next year – the highest reported in 15 years. And this growth is happening fast: in just four years IT budgets have increased by 10%. 

Before jumping onto the tech bandwagon, consider what will make the biggest difference in your company’s handling of data now and in the future. A deeper understanding of your data shortcomings and challenges will inform the selection of the best solutions or tools for your needs. Accuracy is the fundamental measure by which a data quality project should be measured, so there’s no such thing as ‘close enough is good enough’. 

Budgeting for intuitive resources is paramount to achieving optimal success with data innovation, not just today but as the business transforms and grows.  Budgets should identify all resources to clean, manage, and maintain the integrity of data effectively, otherwise, revenue, brand reputation, customer experience, or R&D can stagnate and possibly postpone the general positive changes within the company. 

Myth #3: If it ain’t broke, don’t fix it

Fact: Rapid digital growth demands transformative data quality

Many competitors in the digital space have learned the value of data and analytics, and having made investments in popular data management software, consider themselves to be digitally transformative and on the cutting edge of growth. But what worked once will not necessarily guarantee success in years to come. 

When it comes to keeping up with the fast-moving pace of technology, companies quickly become complacent, adopting the “good enough” approach that ultimately halts innovation and growth. Blockbuster Video, Kodak, Toys R Us, Nokia, Atari, Motorola were all once gigantic and thriving, yet despite their size and once-powerful positions, failure to innovate resulted in their downfall. Constant innovation is the linchpin of success in technology.

Time to Face Reality

As fast as we see more companies adapt to technological and digital transformations, the potential for rapid failure is even greater. Companies are acquiring and innovating with data management at such a historic rate that figures make it clear that failure to adapt will quickly backfire.

Fortunately, change is on the horizon and data quality is becoming increasingly more accessible for the company’s that recognize the need. Over the last two years, data quality tooling and procedures have dramatically changed, ignoring them is inexcusable. 

Companies can no longer afford to ignore data quality initiatives; now is the time to take the data bull by the horns.

The post The Truth About Data Quality appeared first on Think360.