Dow CDO Chris Bruman: We needed a new approach to data quality
By positioning IT at the center as a solutions hub where business units are data owners, Dow's IT organization can respond more quickly to deliver business and customer value.

Last year, Dow took a bold step to make better use of its data. With a goal of eliminating isolated islands of data and making better use of business intelligence as an enterprise asset, the company launched an internal organization that seamlessly integrated IT and the company’s global business units under one umbrella.

The structure of this organization, called Business Data Services, resembles a bicycle wheel, with IT as the hub and spokes reaching into Dow’s business units, geographies, and functions.  The initial goal in creating the group was to accelerate the delivery of digital business process tools and services and increase adoption to deliver stronger business results.

As the 125-year-old company evolves to meet changing market needs, and as enabling technologies like AI and machine learning are added to the mix, this integrated and collaborative approach has changed how IT makes decisions about which digital solutions are right for Dow and the markets they serve, explains Chris Bruman, Dow’s Chief Data Officer.

Click on the podcast player links embedded below to listen to the CIO Executive Council Future Forward podcast conversation with Chris Bruman and read on for edited excerpts of this two-part conversation.  

On maintaining focus:

The external disruptions and challenges we’re facing today really highlighted, probably more than ever before, the need to focus on data as a foundational priority. But we must strictly prioritize on specific areas and not become too distracted with one-off ideas—be narrow and focused, versus boiling the ocean.

I would rather have a few focused areas that are impactful for the business, where we can significantly make improvement, rather than hundreds of areas and barely make progress.  By focusing on a few areas that are aligned to our business objectives, we get wins for the company, our customers, and our people.  With proved capabilities, we begin to understand more about what generates the most value and how we can go faster in terms of adoption for even further value. So certainly, narrow and focused is the theme for us right now.

On creating a data hub:

We began looking at the need for a new approach into data quality and data governance for the company in late 2020. We were working on different aspects, including reporting, data migration, data management, and so on, and wanted to pull those all together to create a stronger hub for data for the company. So, we launched Business Data Services in the second quarter of 2021.

IT doesn’t own most of the data in the company. Several other business functions are accountable for the data, which, if not managed well, can quickly become fragmented. As the hub, we’re bringing the facilitation around governance, as well as better processes, and easier-to-use tools. But we rely on the business, functional, and geographic spokes to perform the cleansing and the long-term maintenance of our data.  The model is important because IT can provide useful guidance and best practices to the data owners in the company.

On leveraging AI and machine learning:

Today, there are a lot of different initiatives related to data cleansing as we’re moving data from one location to another or from one instance of our tools to another. When we do that, we rely on a lot of manual checks. That’s where machine learning can help us by automating that activity. Instead of having someone work over a weekend to check the data during a ‘go-live’, in the future, we can provide a report that points out a few anomalies to validate. So, they spend maybe 30 minutes versus six hours on a Saturday by automating the validation of data to see if it is accurate and clean.

Like many other companies in every industry, Dow is focused on ESG and sustainability as a business imperative. So we’re going to continue to look for use cases where we can leverage AI and ML to automate some of the data management in this important space as well.

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