What We Mean When We Talk About Data Quality

A recent McKinsey article on the value of getting personalization right in the customer experience arena shows just how important a personalized, consistently relevant customer experience is to the bottom line. According to the research, personalization drives up to a 25 percent revenue lift depending on an organization’s ability to execute. The more skillful a company becomes in applying data to grow customer knowledge and intimacy, the greater the returns.

The experience economy is clearly in full swing. In every industry with a customer-brand dynamic, companies are competing with one another on the experience they are delivering to customers, patients, members, donors – anyone who interacts with the brand. As the McKinsey article notes, the competition is fueled by data – the divining line between winning and losing comes down to the skillful application of data to develop a deep understanding of the customer and orchestrating the perfect experience based on those insights, anywhere, anytime.

The challenge for organizations adapting to the joining of these two megatrends – the experience economy and digital transformation – is that the actualization of benefits largely comes down to successfully harnessing people, processes and company culture to truly become data-driven.

A recent Harvard Business Review article, “The Essential Components of Digital Transformation,” reveals the extent of the challenge, highlighting the common misconception that new technology, or even collecting more data, will magically lead to transformation. Yes, organizations are rethinking business models, but despite a projected $6.8 trillion in digital transformation investments by 2023, many businesses go about it all wrong, without a well-defined strategy or clear understanding of what they’re trying to accomplish. “What is needed,” the article states, “Is a shift in mindset, culture, and talent, including upskilling and reskilling your workforce so that they are future-ready.”

Understanding “Data Quality”

Underscoring the shift of all three components (mindset/culture/talent) is the organization’s approach to data; if the objective is to make data-driven decisions, then the question on everyone’s mind which will influence the mindset, the culture and talent is the quality of data. A top concern for marketers and businesspeople is how much they can trust the data to help them make bold decisions.

The answer to how much stock one can place in the quality of data brings us to the technology side of digital transformation, particularly the role of a customer data platform (CDP) in helping organizations deliver an omnichannel customer experience.

There is a common misconception among companies that a CDP is a magic bullet that will miraculously bring about a hyper-personalized customer experience. The problem with this line of thinking, however, is that data quality is not given its due as a key element of the equation.

If we think about a CDP’s purpose as a digital transformation tool that links data management with insights and action, to enable marketers to access the data they need to create segmentation models and ultimately to create campaigns based on insights, what’s missing from the majority of CDP’s in the market is the correct approach to data quality.

Just Say No to Reference Files

Asked about how they handle data quality, many CDP vendors will talk about identity resolution, touting basic matching techniques that anyone can do. But really what they mean is that they’re relying on someone else’s data quality; they’re outsourcing data quality by bouncing customer data against a reference file – gathered by a third-party organization and enhanced in some way.

The problem? Any organization intent on delivering a hyper-relevant omnichannel customer experience does not have the luxury to want for a quarterly update of a reference file where keys change with every iteration. We know customers expect brands to precisely match the cadence of their unique customer journey. In a 2021 Harris Poll sponsored by Redpoint, 82 percent of customers surveyed said that loyalty to a brand is dependent on the brand’s ability to demonstrate a thorough understanding of them as a unique customer. A thorough understanding, from the customer’s perspective, means the brand knows them as the same customer across every channel and offers consistent relevance because the brand knows their individual behaviors and preferences.

The approach to data quality is what differentiates the Redpoint rg1 omnichannel customer experience platform from also-rans who rely almost exclusively on reference files. What makes Redpoint stand out from the crowd is that our platform is the technology with which to build reference files. That means the platform’s advanced identity resolution capabilities, rather than rely on a third-party organization’s customer data, use probabilistic and deterministic matching to enhance a company’s own first-party data.

Democratization of Data

Redpoint solves other common problems as well, namely the data discovery challenge. For an understanding of the problem, consider the Alation State of Data Culture Report (June, 2021) where 34 percent of data leaders listed data discovery (do not know what data exists or who has what data) as a top challenge for using data to drive business value. Furthermore, 36 percent said data democratization was a top challenge, where not everyone can access data on their own, with 35 percent citing organizational siloes as a barrier for using data to drive business value.

Redpoint solves these problems by managing data governance at the point of data use (which the report said is indicative of top-tier, data-driven companies). This task used to be a central IT function, but is more often now moving to the point of use where data is applied at the business level. Pushing governance, curation, and the perfection of data out to the business unit breaks down the siloes that too often lead to inefficiencies in driving business value from data.

Redpoint solves for traditional wide-scale inefficiency by ingesting raw data and taking care of all data hygiene and data transformation tasks at the point of entry. All data harmonization – unlike many vendors – means that even if two strands of data are identically labeled, the platform will make sure they mean the same thing. From ingesting to creating an industry best golden record, rg1 covers the entire spectrum – from the edge to the business unit, or even in central IT if that is what a client prefers. Importantly, as an organically grown platform, all standardization and data management tasks are completed in the same application, greatly reducing process inefficiencies born of having a multitude of solutions in differentiated states.

Foster a New Data-Driven Culture

Lastly, let’s talk a little bit about data culture. We’ve covered the Redpoint difference as far as the platform being a through-line linking data, insight, and action. On the culture side, it’s important to remember that there is a difference between having quality data and knowing that you have quality data. That, in a nutshell, describes the Redpoint approach toward changing the deep-seated mindset that it’s too hard to adopt a data-driven strategy.

With In Situ, our best-in-class data quality platform-as-a-service, business users don’t just have democratized access to data, they see it and have the tools to answer any question they might have about it. With a dashboard that reveals all dimensions and metrics behind data quality, In Situ makes it possible to correlate good data with good business outcomes. The business users who interact with the data drive the improvement of data quality, becoming part of the curation process at the business unit level. This involvement establishes a line of sight between the use of data and outcomes, a key factor in developing and fine-tuning a data-driven culture.

With consistent visibility of data quality throughout the platform, marketers and business users no longer have to fly blind as they try to drive business outcomes. Importantly, as my previous blog notes, Redpoint is a rules-based platform. Trust in the quality of data is fortified because marketers and business users know that, as a rules-based system, data reflects every change – however recent. This is an important concept for delivering an omnichannel customer experience. By building audiences using rules instead of lists, marketers and business users are able to package different versions into content assets, building once and using an audience or asset across every channel and in every context. Lists are static and subject to decay, rules are dynamic and re-evaluate at every inflection point, guaranteeing precision with every customer interaction.

Ubiquity. Connectivity. Process efficiency. Supporting perfected data and a new data-driven culture around the curation of perfected data. This is the Redpoint difference, and it is why ambitious business leaders are turning to rg1 to rapidly transform customer experience and drive tangible ROI.

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Capturing Intent in an Omnichannel Customer Journey is Next-Level Marketing

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