In light of these challenges, Thomas Davenport highlighted eight ways CDOs can demonstrate value:

1. Add an “A” to the CDO title

Taking responsibility for analytics and artificial intelligence allows CDOs to demonstrate value, particularly if insights lead to actions that boost employee satisfaction, enhance customer relationships, or improve supply chains.
CDOs don’t necessarily need to have extensive data analytics knowledge to embrace these responsibilities, he added, given that the role is largely focused on business rather than technical details.

2. Embrace data products

Packaging a combination of data, analytics, and AI to solve a specific business or customer problem is a proven way to create value and maintain it over time. To bolster such efforts, tap data product managers to oversee the entire process and serve as a liaison between business leaders and the data and analytics teams.

3. Measure and document results

You can’t value what you don’t measure, and the CDO contribution is no different. CDOs can enlist finance as a partner to help certify the value of various data initiatives. “It’s hard to get a whole lot of documented ROI from just data and data management, and AI and automation often just point to time saved,” Davenport said.

4. Build relationships with business peers

While you can’t convince everyone of the value of data and analytics, seek out other leaders who see it and can serve as champions for key data products. Once they’re onboard, they can become change agents and help win over the rest of the organization.

5. For low-maturity organizations: Zero in on a few use cases

Targeting a handful of strategic use cases — for example, supply chain optimization or customer next-best action — and getting them done successfully will demonstrate value quickly for companies that are relatively new to the data game.

6. For high-maturity organizations: Build out analytics/AI infrastructure

More-experienced companies need to focus on initiatives at scale. This means creating reusable datasets and features to reduce data scientist busywork and prioritizing large-scale re-architecting of data platforms to meet the needs of a robust data and analytics pipeline. Whatever the approach, demonstrate value every step of the way, Davenport said.

7. Focus on governance without the baggage

Making it easy to do the right thing with data will encourage people to make decisions that protect data privacy and comply with regulations. The best approach is “governance by design,” which builds guardrails into data architecture and systems. Marketplaces and catalogs, along with reusable assets, will help promote data practices that deliver value.

8. Create a data-driven culture

This last objective might be the hardest: In a NewVantage Partners survey, only 21% of firms reported that they have a data-driven culture, and it’s not for lack of effort.
Most companies miss a beat on culture because they focus on data literacy alone instead of taking a targeted, multifaceted approach that leverages culture-oriented professionals, Davenport said.
“If you are in marketing, your needs for data and analytics and AI literacy are going to be different than if you’re in finance or human resources, so they need to be segmented by function,” Davenport said. “If we’re going to make any progress in culture, you have to have some metrics and indicators of progress.”

This article was originally posted by: MITSLOAN