Chief Data Officers: Measuring the Value of Data

Jul 25, 2021 |
Jitesh Ghai Jitesh Ghai

Chief Product Officer

Those of you who are in the chief data officer (CDO) chair know that expectations for the CDO role are changing. The one common theme is that CDOs are experiencing a job that varies by the day. It began with compliance and minimizing the risk associated with data. Now CDOs are being asked to measure and increase the value of data. To do this they need to democratize data, making it more trusted, relevant, and accessible – but not make it a wild west.

For Informatica, our CDO Executive Advisory Board (EAB)  is an opportunity for us to learn from CDOs and provide a forum for them to share their ideas and challenges. Data valuation and data literacy were top of mind at last week’s virtual gathering of the EAB, with members attending from organizations around the world – including Charles Schwab, VMware, and New York Life – as well as the public sector, telecommunications, and other financial services firms.

Valuing Data

Businesses are starting to grasp the importance of valuing data as an asset, but how do you determine what that value is? How do you build that language and framework and make them available within your organizations?

To help answer these questions, we were joined at our board meeting by special guest Douglas Laney, Innovation Fellow, Data & Analytics Strategy, at West Monroe Partners, former VP and distinguished analyst with Gartner’s CDO research and advisory group, and author of the book Infonomics. Doug’s presentation of the ideas underlying “infonomics” sparked a lively exchange with the board. Here are some takeaways from our discussion:

Define data as an asset. The definition of an asset is something owned or controlled, exchangeable for cash, and that generates a benefit. Data meets that definition, but many pundits believe that data doesn’t have value unless you use it. This is contrary to how other assets are valued – they have economic benefits whether they’re used as a revenue stream or for expense savings.

Manage, monetize, and measure. What do we do to assets if they have value? We manage them with discipline, standards, and processes and figure out how to monetize them externally and internally to generate value. So, we need to manage, monetize, and measure data as an asset. But you can’t manage well what you don’t measure well.

Information Valuation Models - you can't manage what you don't measure well.

Apply economic concepts to data. You need to think of supply and demand and pricing elasticity in the context of data. And data has many forms, ranging from raw to enriched. For example, it’s easier to monetize enriched analytical data that underpins data science models.

Focus on data literacy. Data literacy represents a major cultural aspect to doing more with data, around politics and change management. By moving toward self-service analytics and providing support for the foundational steps you need to take related to delivering value from data, you can guide the training and inject data literacy concepts into business culture, communication, and collaboration.

Don’t fixate on any one type of data. You can share, publish, and monetize raw data, which has great general utility, but typically consumers want data more specific to a particular use case, which becomes more expensive to build and deploy. Because data is nondepleting, you can package it up in a variety of ways.

 

Data product types - you can package data in a variety of ways.

Look at intrinsic values for data valuation models. Foundational measures such as accuracy, completeness, and scarcity have typically greater potential value. Also consider whether data is relevant and timely and look at how having or not having data impacts various processes. Take financial measures as well, such as cost value, market value, and economic value.

Flipping the Conversation on Risk

Many organizations are focused on risk mitigation. Laney advises to flip the conversation around: What’s the risk of not capitalizing on your data? Board members agreed that the conversation around risk is important. For example, if you lose data, what would the impact on customers be and how much trouble would you be in?

As well, CDOs are often seen as an enabler of commercial value, as opposed to data having value in its own right. Laney added that infonomics on data monetization is a way to have conversations with business stakeholders to inspire executives to do more with data. He downplays asking, “What is our data worth today?” and encourages people to think about the trajectory – how are you improving real or potential data over time? To participate fully in the data economy, you need to know what you have and what it’s potential or actual value is.

Also, you should make the case that data is not an IT asset but a business asset. The real value of data is often hidden behind the customer. Data is seen as a background element, so it’s hard to see what data part led to an outcome – look at the value chain of the data. Think about and explore data and what we know about customers at the start of project.

Expanding on Data Literacy

For the final portion of the meeting, the board segued into a discussion on how they foster data literacy in their organizations, led by Susan Wilson, Informatica VP Data Governance and Privacy Leader. Here are some of the ideas that were shared:

  • Data folks bridge the literacy gap between business and IT. Implement new solutions and get people at the table to make sure we do the right thing.
  • Build a relationship between a data steward and a data owner in each division. Have a consistent understanding of data practices and share the same language across the organization. Don’t assume a level of understanding. Do a needs analysis, starting with executives – meet them where they are.
  • Start a book discussion on data literacy – mark a passage and pass it on and ask the recipients to do the same. And, have data scientists have one-on-one sessions with executives and bring outcome stories to life.
  • Create an organization with a charismatic leader who can promote what data science is. And set up a formal program with certification or badges that can help generate enthusiasm and encourage employees to share when they complete the program.
  • Put an embargo on literacy tools (which organizations sometimes see as a panacea) until the governance group can get together and define how they’re going to measure success and objectives.
  • Look for a quick win – value that the business wouldn’t realize on their own. It can be as simple as recognizing that business units are measuring the same thing but doing so inconsistently. Connect the dots by working together with the business.
  • Don’t boil the ocean when getting started with a data literacy program. The temptation is to mushroom and expand it but what really works is to put a ringed fence around a set of topics.

Next Steps

At our next gathering of the CDO Executive Advisory Board, we plan to discuss data democratization and cloud modernization. In the meantime, if you’re interested in further exploring topics that are top of mind for CDOs, I encourage you to check out our CDO hub.