Micron Technology CIO Creates Drives Enormous Growth

By Peter High, published on Forbes


Trevor Schulze has been an information technology executive across a number of major technology companies. When he joined Micron Technology as Chief Information Officer two years ago, the company, like others, was in the midst of defining its big data opportunities. Schulze, recognizing the tremendous growth and productivity potential that could be derived from better insights from the data, developed a dedicated team to champion these opportunities. Thus the Enterprise Analytics And Data IT Group was born. As he notes in this interview, the results have been tremendous, and for that reason, Schulze is a recipient of the 2017 Forbes CIO Innovation Award.

When asked about Schulze contribution, Micron Technology Chief Executive Officer D. Mark Durcan said, “We are supplying the new data economy with innovative technology that helps our customers take full advantage of their data assets. We are doing the same within Micron. The innovations that our CIO Trevor Schulze and his team are delivering provide Micron the insights we need to drive our business forward.”

Peter High: Please describe the innovative idea that you and your team in IT pursued. Please be specific, including the steps you undertook to implement the idea.

Trevor Schulze: Although localized point solutions will remain important to creating incremental value, integrated insights across the enterprise will be the competitive advantage we all chase in the years to come. Building this capability is a multi-year effort. Over the past year, we have put in place a few key practices to drive this idea forward and deliver tangible insights.

When we created our enterprise data science team, we intentionally placed this team within our Enterprise Analytics and Data IT group. This gives our enterprise data scientists direct access to our business intelligence teams that have invaluable experience working with the company’s most important data. The synergy between these teams decreases the time to locate and understand relevant data across business areas. Because data preparation is also time consuming, we chose to embed architects and data engineers directly within the team, working shoulder-to-shoulder to deliver quick turns through the highly iterative data science process, reducing data acquisition and preparation time by up to 50 percent. As we near solution deployment, the embedded architecture and data engineers take the lead to deliver solutions to decision makers and to embed algorithms into enterprise systems, while our data scientists begin working on the next business problem. This close collaboration allows us to deliver solutions at a faster pace.

Next, we wanted to bring the power of data science to areas of the business that lack data science expertise but that are data-rich and ready to drive change. Our supply chain and procurement organizations have been ideal early partners based on their extensive data, history of data-driven processes, and their position in the company’s value chain–it is hard to find other functions with the same degree of impact. In just its first year, the enterprise data science team has delivered machine learning solutions that create new insights into product availability, robust forecasts of customer demand, optimized raw material inventory, and many others. Each solution reveals new connections between complex business processes and their supporting data. This uniquely places our enterprise data science team in a position to deploy their deep technical skills across the wide and deep network of Micron’s processes and data.

Lastly, we knew that a truly integrated set of predictive models and insights would require all areas of the company to trust the methods and understand the insights. This step cannot be overlooked, as black boxes are rarely blindly accepted by the business. As a result, our enterprise data science team has been delivering a variety of education sessions explaining fundamental data science and machine learning concepts to stakeholders at all levels. These sessions range from department-tailored presentations to data science book clubs to detailed demonstrations of data science techniques. We have found that awareness and education is essential to driving adoption of data science solutions, building confidence with the insights they produce, and ultimately gaining support for further investment.

High: What opportunity or issue to be resolved led to this IT-led innovation?

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