Break down data silos to unlock great customer experience

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Takeaways

  • Mining more kinds of data improves customer experiences, but breaking them out of data silos remains a major challenge 
  • Centralized data governance rules and standards are an essential prerequisite to improving CX through data
  • Merging operational data with experience data is a powerful new way to improve CX

For all the zettabytes of data companies have amassed to date, surprisingly little of it influences how customers interact with and use products and services.

That’s because most of the data winds up in silos of various kinds, never to inform those experiences. Indeed, eight in 10 businesses today still lack a comprehensive data strategy, according to Accenture–and just as many do not have a centralized digital platform to manage data. Conversely, organizations that are able to draw on a broader range of data can discover unexpected insights about what customers want or don’t want, and when and how.

“Companies can create amazing experiences if they pull the right data from their silos and get it to the right people in their organization,” says Isabelle Zdatny, an experience management leader at Qualtrics’ XM Institute.

Problem is, pulling together all the right data streams is especially difficult when it comes to designing digital customer experience (CX). According to a recent study by XM, executives cited data and systems integration as the biggest obstacle in improving CX. That’s because customer experience data touches many different functions and groups in a typical enterprise, each with different formats and requirements.

“Data can be an incredible asset, but only for companies that have their data act together,” says Bill McKnight, president of McKnight Consulting Group, an IT research and management firm. “Making better use of siloed data to serve customers is one of the biggest gaps standing in the way of better bottom-line results for many organizations.”

Here are three strategies enterprise experts say can help CIOs close critical data gaps in customer experience.

Combine data architecture with search to break down silos

Organizational silos have always been a fixture of the modern enterprise; data silos have naturally followed suit. Since data silos can’t be eliminated altogether, one key to unlocking the value they hold is to establish a strong data architecture — a collection of rules, policies, and procedures that govern how data is stored and integrated. 

Without a common way of managing all data, it’s too difficult to bring it to bear to improve customer experience. As a result, many companies spend months simply “grooming” data so it can be understood by other systems, says David Stodder, senior director of The Data Warehousing Institute (TDWI), an IT management consultancy. “These days, it’s easier for a business department to whip out a credit card and spin up a cloud service to solve a problem,” says Stodder. “But that often just creates another silo.”

While centralized data governance is a key ingredient to solving the silo problem, technology can also help break down (or see through) data silos to improve customer experience. Advanced enterprise search, for example, makes it easier to scour myriad databases to address a particular problem, and do it quickly enough to matter to customers.

By making more types of data easily accessible, companies can also use search tools to quickly gather useful data streams so they can be mined by machine learning systems to uncover new insights.

Build experience around two main data types — operational and experience data

Companies can eliminate other blind spots in customer experience by using a specific methodology for how they collect and analyze the right data. The framework XM Institute uses with enterprise clients focuses on two core groups of information: experience data and operational data.

Operational data includes all datasets derived from daily business operations; that includes objective and measurable data from CRM and sales systems, website analytics, ERP, IT operations, and contact center wait times. Operational data describes much of the “what” behind digital customer experience. 

Experience data attempts to define the “why” behind the digital experience, says Zdatny. It pulls in evaluative data about how customers are thinking and feeling about their experiences, their attitudes, and perceptions. That might include survey data, social media comments, natural language processing data from customer service calls, customer chat data, and so on. 

Building an “X” and “O” data framework doesn’t just help “unsilo” valuable hidden data; it can serve as a discovery platform for new experiences and even predictive insights. 

“Combining experience and ops data allows you to personalize individual experiences at a very minute level,” says Zdatny. “When you’re able to have all of that data together, you can then take that understanding of individuals and project that out onto a much broader population and predict future events.”

Once you unlock data from silos, train your leaders to look for it

One long-term challenge in unlocking great experiences is purely organizational: Valuable data often comes from unexpected sources, and managers may not always be aware of or focus their efforts on the right ones.

“Executives tend to assume they know where data resides because they use it every day to manage a business,” says Chuck Goldman, a senior strategic innovation executive at Elastic. “But what if the most valuable insights are hiding somewhere else?” 

Several years ago at a large communications and cable provider, Goldman recalls, data managers who were exploring customer churn issues decided to look beyond the “usual suspects” — for example, CRM data and customer service logs. Instead, they began looking at technical data coming off set-top boxes.

The company found that churn ticked up after a hardware update caused delays in the time it took customers to change channels. Once the company fixed the hardware issue, churn rates improved.

“You have to break people’s assumptions about where the data lives to solve business problems,” says Goldman. “The direct result is amazing experiences for your customers.”

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