Q&A: Five Questions for IBM’s Chief Data Officer

Share this post:

Inderpal Bhandari, Ph.D., was named IBM’s Global Chief Data Officer in December, 2015. He began his career in IBM Research in the 1990s, then spent the next couple of decades working in data. He became a Chief Data Officer in 2006 for a healthcare company, the first in that field. The following topics and questions were discussed by Bhandari yesterday during IBM’s Chief Data Officer Strategy Summit in San Francisco.

1) What is the role of a Chief Data Officer? Why do companies hire them?

The role of chief data officer (CDO) is a new role in the C-suite as organizations use big data and analytics to transform their business models and business processes. Gartner predicts that 90 percent of large companies will have a CDO in place by the end of 2019.

A CDO develops strategic, innovative capabilities that deliver data-driven insights to enable growth, productivity, and improve customer experience.

A CDO should also develop a clear data strategy, govern and manage the data as an asset, deliver the centralized source of trusted data, build deep data and analytics partnerships with different business units within a company as well as external partners, and develop and scale talent in the areas of data engineering and data sciences.

2) What is a good data strategy?

The focus of the data strategy should be on monetization. Such an approach leads to questions such as, “How does the company create value for its customers?” “What data do you need to deliver that value proposition?” “What do you need to do with that data?” “How do you manage that data so it is and remains fit for that purpose”?

3) What is the role of cognitive computing in today’s organizations?

Cognitive systems put content into context, organize the signals received from the environment, evaluate patterns to extract meaning and communicate actionable insights to users along with supporting evidence.

In a cognitive business, where greater precision replaces guessing, approximations and averaging, data is its foundation. Unstructured data, for example, is 80 percent of the data in the world, and most of it’s not being analyzed. Yet most of us conduct our lives around unstructured data because it includes photos and videos, social media, satellite images — even handwritten notes.

At IBM, we are building cognitive systems that can ingest unstructured data in all of its forms to integrate cognition into every organizational process, including sales, operations, technology, etc.

4) What role does cloud computing play in a data and analytics approach to business?

The cloud provides the scale that is required to implement cognitive systems. It is also the platform for change that’s providing the impetus for undergoing major transitions by businesses.

Uncertainty comes with the territory when executing a cloud- and data-driven strategy. However, uncertainty can be managed through constant communication with others in the C-suite as well as teammates in IT, finance and other departments. Ensure that everyone involved understands the overarching goals and the path to implementation.

The business should also understand that setbacks are learning opportunities. When an organization is building tools that haven’t been built before, it is important to communicate that setbacks — along with milestone wins — are all part of the process.

If an organization is solving business problems, creating value and continually gaining additional insight to improve, it’s probably succeeding. But take the long view: implementing a transformation takes time. Don’t force it. When altering the way people look at data, it doesn’t happen overnight.

5) What did you work on in IBM Research that you’re most proud of?

Before the practice of analyzing Major League Baseball stats led to the popular book and movie, “Moneyball,” IBM Research was helping National Basketball Association coaching staffs in the late 1990s with Advanced Scout, a data mining tool to help defeat opponents.

The application helped NBA coaches make game-day decisions, including the best positions for players and best match ups of player combinations. Advanced Scout used an algorithm to find patterns among statistics and video tape to devise new strategies. At one point, 25 NBA teams used Advance Scout.

As the lead IBM Researcher who developed it, Advanced Scout was a very early application for data analysis users who weren’t in technical fields.
____________________________________

A version of this story has appeared in Forbes.