How to deliver on the data promise

Post by Naras Eechambadi

Two recent studies have interesting findings about the state of customer data and its use across many organizations. Both indicate that companies continue to struggle with harnessing customer data to create measurable value. These companies may benefit by framing and addressing this problem differently.

The first study, The Data-Centric Organization 2018, by the Winterberry Group finds that data-driven transformation remains a common organizational priority.Despite this, few companies have successfully implemented strategies for using customer data for advertising and/or marketing purposes and most have few results to show for it. The surprising finding is that this proportion has declined dramatically, from close to 29% in 2016 to just over 12% in 2017, a steep drop within just one year! Likewise, the number of respondents who felt their organizations are data-centric dropped from 24.3 % to 9.8%, an even steeper drop. The primary stumbling block to success seems to be the paucity of talent, particularly finding people that can derive value from data. Less than a third of the respondents are very or extremely confident they have the right expertise, skills and experience in house.

The second study, Marketing Leaders Strive to Master Data, but Hit Limits, is from Gartner and based on a survey of 300 respondents in large organizations. It highlights similar results, i.e. marketing leaders highly value market and customer insight capabilities, but reality falls short of expectations. More than a third of them say that data analytics, customer insights and math skills are the most difficult to recruit for.

One of the reasons companies don’t realize the full benefits of the data promise is their inability to link the output of data science to strategy and action. Data scientists spend an inordinate amount of time on data "janitorial" tasks, to get data sets ready for analysis. This squeezes the time available for them to generate insights and the operationalize them.

Talent shortages and the data science output gap need not be hard constraints limiting companies’ ability to profit from their customer data. Given the clear challenge in sourcing these skills and making them effective contributors, companies can benefit by framing the problem differently. Customer data platforms (CDPs), like Quaero, help solve both issues.

CDPs address the data management issue, enabling data scientists to stitch together data sets for analysis and to operationalize the models into production when ready. They also address the application chasm between data scientists and marketers. CDPs create a common platform where marketers and data scientists interrogate the data in a consistent manner, creating common ground for understanding, interpreting and acting on potential opportunities.

Quaero is the only CDP purpose built to enable such use cases and users. Through a no-code interface, Quaero connects and activates a variety of big data environments, analytics, and machine learning tools. Marketing and other customer facing functions are empowered to focus on their key use cases, saving time and money. They can also partner more effectively with IT to ensure that they meet security, governance and audit controls while simultaneously enabling business users and analysts to be much more effective and efficient. While CDPs may not eliminate the need for data engineers, scientists and analysts with deep technical expertise, they can help them be much more productive and reduce the number of these scarce resources that are needed.

Moreover, CDPs that enable such users also help ensure that these talented (and expensive) staff are more fulfilled in their jobs because they can get more done and have more measurable impact. As a result, retention becomes easier reducing the need for constant recruitment, training and waiting for new staff to come up the learning curve.

Rather than trying to hire more or better data scientists and lamenting the paucity of these skills in the market, executives may want to look at systemic investments that make their existing resources more productive, enabling them to fulfill their data promise on time and under budget.

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