Keys to Maximizing Data Value

Data is king in today’s digital economy. This critical asset should be exploited to its maximum potential, yet the sheer volume of data pouring into businesses makes it challenging to effectively process and analyze information.

Data value is the measure of the worth and utility derived from data, often associated with its monetization potential, says Piyanka Jain, CEO of data analytics consulting firm Aryng in an email interview. “Not all data holds the same value, as certain information can drive more significant value than others.”

Building a successful data strategy at scale, one that generates maximum value, goes beyond collecting and analyzing data, observes Ryan Swann, chief data analytics officer at investment firm Vanguard, in an email interview. “An organization’s data strategy needs to be connected to a clear mission in order to enable [it] to prioritize data initiatives, allocate resources efficiently, and foster an engaged workforce.”

Aiming High

Jain believes that the most effective way to maximize data value is by ensuring that the data is well-connected and not isolated within silos. “When data elements are interconnected and form a unified fabric, they can collectively tell a more comprehensive story,” she says. “It’s about establishing a single source of truth within your organization where every piece of data contributes to a larger, interconnected narrative.”

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Credibility plays a major role in maximizing data value, observes Kathy Rudy, chief data and analytics officer with technology research and advisory firm ISG via email. “If your data isn’t recognized by your stakeholders as credible, you’ll spend more time defending the results than acting on them, creating cycles of uncertainty and rework.” She notes that leveraging data tools, models, and cleansing techniques are one step, but working closely with data stakeholders to help them understand data sources, as well as the data models being used to drive analytics, is key.

Swann agrees. He believes that a vital aspect of maximizing data value is ingraining data and analytics best practices across the organization, making data more accessible, actionable, and valuable. “This means equipping everyone with accessible learning journeys and multimedia resources so they can understand how they can use data to help them make informed decisions regardless of their role, tenure, level, or expertise.” Swann adds that creating a culture in which employees are encouraged to use data and analytics resources will help boost tools adoption and analytics use cases.

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Data Protection

Organizations can inadvertently damage data value by failing to ensure its quality and integrity. “Making decisions based on bad data, or constantly having to qualify the data, will create reservations about the quality of the data and will instantly hurt any analytics program,” Rudy says. Another not-so-obvious danger is failing to properly use existing data because team members lack the skills needed to analyze and deliver value. “An organization might be sitting on a ton of data, but because there’s no one to produce and deliver results from the available data the assumption is there’s no data to help support decision making.”

Organizations inadvertently damage data value when they keep their data disparate and isolated, Jain says. “When data is isolated in various departments or systems, it loses its context and potential for generating value,” she explains. “Additionally, when multiple versions of truth coexist, trust in data diminishes impacting its overall value.” In essence, failing to connect data and maintain a single source of truth limits an organization’s ability to effectively utilize and monetize its data assets.

It’s imperative that data teams work in close partnership with business colleagues to create a unified approach to data and business strategy, Swann says. “Close collaboration between data professionals and the business provides valuable and continuous insight, refines processes, builds efficiencies, and reduces friction across key operational areas.”

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Measuring Results

Swann says the metric he’s found most successful when judging data value is multi-year net present value (NPV). “We’ve also been successful with control experiments and A/B testing that has allowed us to attribute incremental value to the data/analytic product.” Additionally, qualitative measures, such as impact/adoption rates and even anecdotal insights reflecting how team members are becoming more productive and effective at leveraging data and analytics, are key indicators, he adds.

Jain believes that the best metric for assessing data value is its impact on revenue and profit. “While data can be valued for its contributions to various aspects, such as employee satisfaction or compensation, its ultimate measure should be its influence on financial outcomes,” she explains. “Whether it directly affects revenue growth, cost savings, or profit margins, the financial impact serves as the most tangible and universally understood metric for assessing data value.”

Going Holistic

Swann credits his organization’s holistic approach with effectively calculating data value. “We have a dedicated team within our chief data analytics office [that’s] charged with rigorously calculating all the dimensions of NPV, including over a multi-year period,” he says. “This team works in close partnership with business stakeholders and finance to articulate the concrete value of our data efforts to senior stakeholders.”