Bridging the Trust Gap of Data

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How can analytics companies get clients to trust data


Data

The art of data science is the coupling of mature discipline of statistics and computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data.

Knee-jerk, emotional and subjective decision-making has been replaced by objective, data-driven insights that allow organizations to better serve customers, drive operational efficiencies and manage risks.

However, despite the recent surging success of data science, there still remains a trust gap between analytic companies and clients.

Despite the frequent declarations from executives about developing data-driven processes with analytics, only one third of decision-makers actually trust their analytics, according to a survey conducted by Forrester Consulting on behalf of KPMG, titled “Building Trust in Analytics.”

Seventy percent of organizations agreed that by using data and analytics, they exposed themselves to reputational risk (e.g. data breaches, mis-selling of products and services).

“We need to find ways to establish societal trust in how organizations operate in the emerging data-driven society,” said Sander Klous, partner at KPMG in the Netherlands, said in the report. “The simple reality is that we are moving quickly into a world in which our behavior and decisions are heavily impacted by systems fueled by data.”

Additional reasons for distrust, per the report, focused on executives’ lack of confidence in their company’s internal data efforts.

Ten percent of organizations believe that they excel in quality of data, tools and methodologies, while only 13 percent believe they excel in the privacy and ethical use of D&A. Additionally, less than one-fifth (16 percent) believe they perform well in ensuring the models they produce are accurate.

This gap between the outward belief in the power of analytics, and the lack of confidence in analytics held by executives, represents a potential problem, especially as investments in big data analytics continues to ramp up.

How do analytics companies bridge this gap?

Well, according to John Landsman, director of strategy and analytics at eDataSource, the answer is in the access and value of the information.

“We show them our extensive data platform, its tools for access and reporting, the simplicity of its user interface, and the speed of its responsiveness,” Landsman said. “Because our data address critical marketing needs, it’s not hard to show the data’s power, through the value it provides when put to use.”

However valuable and accessible though, Landsman admits, if the presentation isn’t easy to understand, clients will not grasp the power of it and therefore lose trust.

“The massive breadth and volume of data available to marketing managers is a serious challenge. They’re busy, time-strapped people,” Landman said. “Whatever the data’s importance and potential value, they won’t use it if it’s not curated, packaged and delivered in such a way that managers can easily understand what it’s telling them, and what they may need to do about that.”

The issue has forced analytics companies like Dataroma to integrate the source of information into a single entity.

“Today, marketing professionals are being hit with an onslaught of data sources as their marketing technology stack is ever-increasing. Although the marketing department has access to plenty of information it is of zero value until it is made sense of,” said Katrin Ribant, chief solutions officer at Datorama. “This requires a single source of truth where a MarTech stack’s source systems can be converged. Once the marketing team’s data is integrated, analytics can be applied not only to visualize the latest happenings but also better manage and optimize marketing campaigns.”

Companies such as Origami Logic, in an effort to hone the packaging or delivery of content, focused on the insight it provides.

“Effective marketing analytics – the process and computations that turn data into actionable insights – depend on the refinement, integration and organization of data,” said Opher Kahane, CEO at Origami Logic. As marketing organizations embrace data science models, data and analytics need to go hand-in-hand.”

Despite the current challenges though, Landsman remains confident in the evolutionary growth of data science and the trust therein.

“Trust will continue to improve, but that will be driven, in part, by the increasing sophistication of marketing managers on the client side who will continually challenge the derivation, validity and actionability of data they’re buying,” said Landsman. “To stay in business, data vendors must rise to those challenges.”

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