What companies get wrong about data transformation

For years, IT and data leaders have been striving to help their companies become more data driven. By most accounts, companies are making the necessary investments, as evidenced by the majority of heads of IT (52%) saying data analytics and machine learning will drive the most IT investment at their organizations this year, according to CIO.com’s State of the CIO survey.

But technology investment alone is not enough to make your organization data driven. It requires the right vision, culture, and commitment.

“A lot of organizations have tried to treat data as a project,” says Traci Gusher, EY Americas data and analytics leader. “It can’t be treated as a project; it has to be treated as a function. Until organizations start to treat data as an imperative operational unit, they’re going to continue to struggle to get any kind of consistency and quality in data.”

Such strategic missteps may signal an ongoing issue at the C-level, with company leaders recognizing the importance of data and analytics but falling short on making the strategic changes and investments necessary for success.

A recent report from Alation and Wakefield Research found that 71% of data leaders are “less than very confident” that their company’s leadership sees a link between investing in data and analytics and staying ahead of the competition, with 51% expecting to get half or less of the amount they say they need. In fact, two-thirds of data leaders said company leadership was an obstacle to getting the funding they need, with 35% of that group citing lack of support from company leadership and 42% saying company leaders make promises but don’t follow through.

One remedy for this disconnect has been to give data a seat at the C-suite table, with many companies over the past several years hiring chief data officers (CDOs) to helm data initiatives. But in 2021, Harvard Business Review noted that the average tenure of a CDO is between two and two-and-a-half years.

“It’s not because they’re checked out,” says EY’s Gusher. “It’s because they either are not getting the resources they need, are not getting the funding that they need, or they’re being viewed as ineffective because they’re not making progress. I think that speaks volumes to the type of commitment that organizations have to make around data in order to actually move the needle.”

What actually works

So if funding and C-suite attention aren’t enough, what then is the key to ensuring an organization’s data transformation is successful? Companies that commit to treating data as a product and to transforming their culture are the ones that succeed, says Doug Laney, innovation fellow of data and analytics strategy at West Monroe. Laney, a former distinguished VP analyst at Gartner, studied how companies used their data when he was with the research firm.

“We found that companies that treat data more as an asset have a market-to-book value ratio that’s nearly two times higher than the market average. And companies that sell data products or data derivatives of some kind have a 3x market-to-book value ratio,” he says.

According to Alation, companies that have a strong data culture outperform their peers. In its survey, Alation found that 90% of companies with top-tier data cultures met or exceeded their revenue goals over the past 12 months. The company defines data culture as consisting of three key disciplines:

• Data search and discovery: the ability to quickly and easily find the right data for a specific purpose
• Data literacy: the ability to draw valid conclusions from data, including understanding the limits of interpretation and awareness of common biases
• Data governance: the overarching process by which data assets are managed to ensure trustworthiness and accountability, including compliance with policies and regulations

Companies with top-tier data cultures have widely adopted all three disciplines across departments, Alation says.

Of course, building a vision and culture around data that gets your company to that point is the trick. The first step, according to EY, is to adopt a visionary core data strategy. Such a strategy should connect how data will inform, support, and drive an organization’s short- and long-term strategic business plans. It should also reduce threats identified in enterprise risk management plans and help capitalize on opportunities. This requires a dedicated data team with leadership, resources, and executive support, EY’s Gusher says.

“All too often, companies think of data as a technology problem, and it’s just not,” says Gusher.

Anatomy of a data strategy

Specifically, EY says a well-developed data strategy should include several key areas:

1. High-priority use case identification: This should serve as a guiding light for the data strategy, including clear expectations on data monetization and informing the transformation of data as an asset. It should also include development of a data supply, including both internal and external data sources.
2. A data governance plan: This plan should specify how data will be managed, including the policies, stewardship, and operating model for orchestrating fit for purpose data management.
3. High-level architecture plan: To enable the execution of use cases and governance, the organization’s architecture should be informed by the types of technology needed to integrate, transform, enable, and consume data.
4. A plan to increase data literacy: Democratizing data across the enterprise and getting data into the hands of decision-makers at strategic and tactical levels is essential.

A fifth vital element is getting change management right, says Mike Giresi, chief digital officer of electronics manufacturer Molex, adding that the key to making your company more data-driven is helping everyone understand why they should do things differently.

“I think the primary reason the majority of these efforts fail is that the change management aspect shouldn’t just be about training people how to do something better,” Giresi says. “It’s really about connecting them to why they should want to do it differently and then incentivizing them with a culture that reinforces that. It has nothing to do with the tech. It has everything to do with understanding the value proposition that the company is performing against.”

And this change effort must come from the very top, Giresi says, requiring a lot of CEO engagement.

“It’s great for the grassroots to be connected to it and support it, but ultimately you’re going to have to change the mental model of business unit leadership in terms of what they value and how they’re supporting and incentivizing change,” he says.

And that makes educating the C-suite on the importance of data transformation a key CIO remit today.

Analytics, Chief Data Officer, Data Management