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PHOTO: Sharon McCutcheon

Data is essential for the design and delivery of customer experience programs, but the value of the data depends on its accuracy, completeness and timeliness.

According to a SnapLogic study, 76% of companies are missing revenue opportunities due to lack of data insights, and 72% of companies surveyed said customer engagement and satisfaction are negatively impacted due to missing or incomplete data.

Additionally, more than half (54%) admit to relying on poor quality data to drive strategic decision making.

"To know that so many organizations are making business decisions using data they do not trust is alarming," said SnapLogic CTO Craig Stewart. "To get data analytics projects right, it’s critical that organizations review what data they have, the applications and sources it comes from and how they are bringing it all together."

To improve data quality, Stewart recommended that organizations improve their infrastructure and processes to help establish a reliable data analytics foundation. A key aspect is reducing data silos to minimize issues with accessing, analyzing and utilizing data effectively.

Understand Which Data Is Right for Each Use Case

Organizations need to use data from all available resources to gain a 360 view of customer interactions, said Jen Hsin, head of data science at SetSail. "For example, you might have data on email activities, but miss out on some important customer questions raised in Slack.  Or, in a different scenario, you might have all the instant messages, but the order forms were shared in Google Drive. Such lack of data coverage really reduces your visibility into the customer. Take stock of the data currently available to you, and map out how many types of customer activities are being captured.”

Yet some data is extraneous for some uses, Hsin added. "The same data that is a signal for its designated use case can turn out to be noise for a different application.”   

"As you gain experience with your existing data, start to formulate the key metrics and statistics that are effective for your CX organization," Hsin said. “For example, it might be the combination of certain firmographic data (like customer's technology stack), plus the customer's product usage trend. Knowing what data you need means you can focus your resources, and not be overwhelmed by all the other data types that are less relevant to you.”

Related Article: You're Drowning in Data: Now What?

Guard Against Information Bias

If humans really want a hypothesis to be true, we will find ways to confirm it, leading to confirmation bias, cautioned Said Matin Movassate, CEO of Heap. "This can be exacerbated by platforms that make users choose what to measure (and what not to measure). So companies need tools that can analyze a complete data set outside of what they might be more inclined to choose (which would then be riddled with biases)."

“Confirmation bias already exists across disciplines,” said Movassate. “According to a Gartner survey, more than half of marketing leaders are disappointed in their analytics results, but they shouldn’t be blaming the data.”

To combat confirmation bias, Movassate recommended using reliable data to back counter arguments and examples. "Teams need to proactively think not only about the insights they’re pulling from their data, but what data they’re choosing not to extract and analyze and for what reason," Movassate said.

Related Article: Cautionary Data Analytics Lessons From the Battle of Britain

Overcome Internal Turf Wars With Data Governance

Data is an IT issue: everyone in the company relies on data and the responsibility for that data must be shared across both IT and the business, said Lisa Loftis, principal with SAS Customer Intelligence, Best Practices.

“Governance ensures that the data being used for customer engagements are accurate, timely and fit for purpose which in turn, builds trust with customers,” Loftis said. “While business may view IT and the governance processes as unresponsive or additions that slow progress down, this is ultimately detrimental to CX goals. Governance and CX must work together as a team to provide the data quality necessary to fuel accurate analytics and customer engagements.”  

Proper governance is also essential in protecting data privacy, Loftis added. “As privacy regulations increase, governance expands to incorporate privacy and security as well as access control and data policy development. This component is critical for CX leaders in particular and is one in which they should play a big role.”

Related Article: Why Marketers Should Embrace Consumer Privacy Regulations

Confirm Data Accuracy

“Ensuring the integrity of your data has to start at the source” said David Finkelstein, CEO and co-founder of BDEX. "Companies that work purely with first-party data need to make sure their data is up to date by implementing a data cleaning routine. This may mean keeping in regular contact with customers to be able to update information relevant to their efforts, such as address, email or other changes."

Companies that use third-party data need to ensure they’re working with reliable data providers — ones that have the capability to scrutinize and filter out erroneous data, Finkelstein added.