How to prioritize data strategy investments as a CDO - Journey to AI Blog

• Updated on: 2022-05-26
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My first task as a Chief Data Officer (CDO) is to implement a data strategy. Over the past 15 years, I’ve learned that an effective data strategy enables the enterprise’s business strategy and is critical to elevate the role of a CDO from the backroom to the boardroom.

A company’s business strategy is its strategic vision to achieve its business goals. Data that can be managed, protected, and monetized effectively will provide insights into how to achieve those goals. A CDO works in collaboration with senior executives to steer a business to its strategic vision through a data strategy.

Strategy environments contain complex moving parts, points of view, and competing needs, all working toward three goals:

A CDO’s priority is not just to learn the strategic needs of the business and senior leadership, but also to implement a data strategy that helps leaders achieve their goals faster and embrace data as a competitive advantage. When prioritization of these goals is decided and agreed upon by all, the enterprise can more easily achieve true alignment, resulting in a collaborative, data-driven environment.

Strategic alignment also ensures that competing day-to day responsibilities will not challenge the CDO role. Quick wins and fighting fires are a part of the job, but it is only when they are in service of an enterprise-wide supported strategy that a CDO will have a comprehensive impact on a business.

When I joined IBM in 2016, our business strategy centered on hybrid cloud and AI. As a result, I could align and evolve our data strategy with that focus going forward. For example, how do we grow revenue in AI if many leaders don’t fully understand what an AI enterprise looks like?

At IBM, we embarked on a data strategy to transform the enterprise into a data-first business, with AI infused into every key business process. Our data insights sharpened our definition of what AI meant to an enterprise, which also fed directly back into our business strategy. Thus IBM, itself, served as client zero and became a showcase of our solutions in the data and AI space.

In terms of implementing that strategy, we set several key pieces in place, such as:

All these pieces worked together to set us up for a successful strategy pivot in 2021.

From 2019 to 2021, our GCDO successfully aligned our data strategy to top-line growth with a strong focus on revenue. Sharpening our focus resulted in more than doubling our contributions to enable revenue growth, for our product sales and consulting teams, over the last three years. This year – 2022 – we are on track to increase our contribution by 150%. These additional revenues accrue to all our major brands and channels: hardware, software, business services and ecosystem.

Aligning our data strategy to support revenue and profit was a smooth transition because our central GCDO acts as an extension of all lines of IBM’s businesses and functions.  We have assigned data officers throughout all major business units, and we meet with them regularly to ensure strategic alignment.

Another part of our pivot was an education and mindset shift to design thinking. We worked directly with the people involved in the end-to-end process, an often underused step to transformation. The power of design thinking surfaced pain points directed to data, and it provided new opportunity benefits that rippled out to teams across the enterprise.

 Our future data strategy will maintain a foundation of top-line revenue focus. Looking forward we are excited to additionally focus on using the power of a data fabric, improving user experience, and tapping deeper into our ecosystem of partnerships.

A data fabric architecture is the next step in the evolution of normalizing data across an enterprise. Its untapped potential provides an exciting opportunity to expand within our own data efficiencies and strategy.

A data fabric is an architectural approach that automates data discovery and data integration, streamlines data access and ensures compliance with data policies regardless of where the data resides. A data fabric leverages AI to continuously learn patterns in how data is transformed and used, and uses that understanding to automate data pipelines, make finding data easier, and automatically enforce governance and compliance. By doing so, the data fabric significantly improves the productivity of data engineering tasks, accelerates time-to-value for the business, and simplifies compliance reporting.

It is an exciting time for the future of data. We can now mine the capabilities of a data fabric architecture to provide a more positive user experience that gets data into the hands of those who need it most with trust, transparency, and agility. The significance of a data fabric architecture is magnified with the emergence of the virtual enterprise and ecosystem partnerships.

In 2022, the IBM GCDO strategy also includes an increasing attention to our business partnerships, a growing space in the data field. Leveraging an ecosystem of partners with complimentary technologies can bring solutions to clients faster.

Additionally, the massive, heterogeneous, and innovative environment at IBM allows our GCDO to focus on solutions as part of a living lab. Acting as our own power user, we can test our solutions at scale to consistently provide a roadmap of insights back into our own products and partnerships. Our current partnership with Palantir showcases how we do this at scale.

When data strategy is prioritized, data can govern processes as well as augment the leadership experience. As a CDO whose role is that of change agent in the enterprise, I will continue to shape strategic conversations with leadership. And as we move further into 2022, our data strategy investments will continue to evolve alongside our offerings.

If you connected to these ideas, I invite you to learn more about data fabric solutions and how IBM Cloud Pak for Data can help you leverage the value of data across your enterprise.