Skillful Approach to Data Quality Matters to an Agile Data Strategy

By   /  August 1, 2017  /  No Comments

data qualityWhat is the state of Data Quality in the enterprise?

According to the 2017 Global Data Management Benchmark report conducted by Loudhouse for Experian Data Quality (EDQ), hopes for getting the most value out of data are high, but not everything is quite in place to achieve that.

The survey finds, for example, that 61 percent of U.S. respondents use data to increase revenue, 56 percent use it to better serve customers, and more than one-third in each instance use it to enhance marketing efforts, reduce risk, find new revenue streams, and empower new initiatives. The belief that data plays a role in powering all these business opportunities goes hand-in-hand with the fact that 84 percent believe data is an integral part of forming a business strategy.

Interpreting the data to define organizations’ Data Management sophistication, however, EDQ finds that just 18 percent have an optimized level of Data Quality, including having in place a central data role accountable for company-wide data assets, monitoring Data Quality as part of standard business practices, and having a platform approach to profiling, monitoring, and visualizing data. The highest percentage, 39 percent, are reactive, meaning that they have good knowledge of the impact of Data Quality, but their business does not have data-specific roles. Also, Data Quality fixes happen only in departmental silos, while homegrown manual processes and tools are the main Data Management methods.

Seventeen percent – a 1 percent increase since the 2016 study – are classified as inactive when it comes to Data Quality, and need to work harder to evangelize the importance of Data Quality within their business and develop standardized processes for fixing and monitoring Data Quality issues.

Such results speak to the point that too many businesses still don’t have strategies and tools in place to effectively govern and unlock the potential of their data. So, too, does the fact that the global market for Data Quality tools is expected to grow from $ 610 million this year to $1.4 billion by 2022, according to MarketsandMarkets.

To that end, Experian Data Quality has been tasked in recent years to bring its data strategy and practices portfolio – which revolves around providing comprehensive Data Management solutions to help clients maintain the accuracy of customer records, reduce errors, and avoid the extra costs associated with bad data – to the broader Experian universe, says Thomas Schutz, General Manager of Experian Data Quality North America. Transforming data to make it meaningful and actionable for clients has increasingly been an aim of the parent company, which has business activities in the credit services, decision analytics, and marketing and consumer services space.

Putting the Pieces Together for Fast Action

“Over the last year and going into the immediate future,” Schutz says. “The focus has been on taking EDQ’s disparate capabilities and tools and joining them into a suite of Data Management products with a simple philosophy: Putting fast, self-service, time-to-value tools in customers’ hands that aren’t technology burdensome and that let business users rapidly unlock insights from quality data for making downstream business decisions.”

The EDQ solutions include systems that today are par for the course for Data Quality, such as address-, email-, and phone-verification, as well as data matching to identify and consolidate duplicate records and provide a single customer view. Data enrichment services make it possible to append a variety of information types, such as demographic, behavioral, household and geographic, to customer and prospect records.

Experian Pandora, its Data Management solution, is a key asset for making data fit for purpose, featuring profiling, transformation, enrichment, and monitoring capabilities for consolidating, cleansing, standardizing, and prioritizing data across the entire organization in a transparent and collaborative way. It’s designed to help companies understand the state of their data, including root causes of quality issues and their impact to the organization. It leverages Experian’s own reference data resources in-context to help companies with their data-driven decisions, too.

“The idea is that we want our customers to get the output they want quickly,” Schutz says, laying out their requirements and potentially taking advantage of EDQ’s light consulting services to achieve their goals.

“We also can provide digital content and information in user communications, so they can learn from their own experience and those of like businesses to get time-to-value relatively quickly.”

Rather than creating a major and lengthy IT-centric data strategy at every turn for various projects – whether departmental, function-based or geographical – “we want to let customers interact with data, giving them a suite of products that in some instances in minutes, but at the outside a month or two, let them at a strategic level operate their data strategy as they want to,” he says. That’s critical given how quickly things change: A project that began with a two-year timeline a couple of years ago might not have appropriately accounted for the European Union upcoming General Data Protection Regulation, for instance, causing the strategy to have to be edited midstream, he notes.

The needs of a department in a massive global bank, he explains, are not that different from the needs of a regional credit union. “They all need consulting and expertise and we can offer that, but they also need tools to evolve with them very quickly, and that can be deployed with a light technology burden very quickly,” he says. “We have to be more agile and enact clients dream strategies faster.”

Experian Data Quality Evolution

Schutz says that a few years ago there was more of a track towards becoming more like other players in the space, with a focus on scale and delivering very robust technology solutions with a heavy ratio of consulting for large entities. “We had done the same but we have also pivoted to capture what it is to be agile, to have a lean start-up mentality,” he says – and yet still benefit from having access to the various data assets and infrastructure across the entire Experian entity.

It will remain critical for EDQ to make delivering its capabilities to customers in an agile and self-service fashion, enabling them to govern and profit from a single “golden record” view of their customers. Going forward, empowering clients to further deal with the evolution of data governance and privacy as per regulatory requirements will gain more attention too.

Ultimately, says Schutz:

“We are focusing on the business user and the space evolving to move out of IT and empower that end user to do more with their data. We think the industry will evolve that way and we continue to look to take our products and business in that direction.”

Photo Credit: graja/Shutterstock.com

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.

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