4 core learnings from successful CDOs and how to apply them

A recent IBM IBV CDO study explored the pressures CDOs are under to deliver results despite unclear expectations for the role. The study identified an elite 8% of chief data officers whose organizations allocate proportionally less of their revenue to data yet generate equal or greater business value. These surveyed “Data Value Creators” were found to have four core focus areas:

1. A clear line of sight from data to value
2. Data investments that accelerate business growth
3. Making data a central element of business model innovation
4. A network of engaged ecosystem partners and internal stakeholders

The first two learnings from the study come across as what should be done, but the next two steps really speak to the how. It can be easier to get a clear line of sight from data to value with engaged ecosystem partners and internal stakeholders. And when data is a central element of business model innovation, it can be easier to come up with data investments that accelerate business growth. In this blog, we’ll dive deeper into findings from the study and what these Data Value Creators are doing differently.

Help deliver a clear line of sight from data to value with engaged external partners and internal stakeholders

The Data Value Creators studied indicated that they have partners that are 100% engaged in their data strateg­­y­­.¹ These relationships can be crucial to creating a clear line of value.

I think about my network of partners in three parts:

1. Horizontal: This is the peer-to-peer element, where the CDO connects with internal and external stakeholders such as business heads, the functional heads and key suppliers.
2. Top down: In this element of the ecosystem, the C-suite pushes for critical alignment with business strategy, so data strategy can be designed to support business goals.
3. Bottom up: The people at the company who will execute the plans comprise this element of the network. This is where the culture of the company comes to life.

The exercise we did at IBM showcases this end-to-end connection of partnerships over time. To get from data to value, you should understand what drives the business, connecting your data and analytics strategy to business objectives even though the focus of your line of sight will shift as the business strategy shifts. IBM’s business strategy is to sell cloud and AI to enterprises. When I first joined IBM my goal as Chief Data Officer was to increase revenue and be client zero for IBM. Over time, my goal as CDO evolved to focus on creating AI-driven efficiencies that mitigate risks around GDPR by threading privacy and security concepts into the data landscape.

Today, our skills are used in support of the business strategy to drive the top line of the company using data. Because we were always aware of the business strategy and kept adapting to support it, we were able to focus on revenue to grow as a company. The main thing is that we kept adapting to the business goals to align to what was considered valuable by the organization. Our data strategy evolves as the business strategy evolves, which empowers us to help achieve and ultimately shape business goals through digital transformation.

When we talk about the adoption of capabilities that take data all the way up to value, that is the bottom-up element of the network. By transforming the culture of the company and empowering the team, people can execute on these shifts and we could deliver results side by side with business teams. All three parts contribute to our ability to realize value because everyone was involved and united behind the future data landscape.

Make data central to business model innovation with smart data investments 

Almost 9 out of 10 of the surveyed Data Value Creators are using data investments to pursue new sources of value to fuel innovation within their organizations. Choosing a data architecture that supports those choices can drive success and provide the guardrails for teams to innovate.

A modern data architecture like a data fabric architecture can be a means to addressing two major issues in the data landscape that can impede innovation:

1. The ever-changing regulatory landscape

The regulatory landscape is changing, with constraints to moving data as well as restrictions based on country of origin, with different regulations applying rules to different bodies of data. Only 58% of all surveyed CDOs agree their organizations are fully compliant with data legislation and standards, and only 61% agree their customers’ data is secure and protected.¹

A data fabric architecture helps you to manage this complexity, adding and adapting use cases for data over time as your organization’s needs evolve. It can act as a pane of glass that allows you to govern data from different locations in a way that is consistent with all the policies. This architecture is flexible enough to be customized by organizations to help address their regulatory needs and data security concerns. Ultimately, a data fabric architecture addresses the distributed regulatory problems associated with this multimodal governance through components that enable you to make progress to achieve value.

2. The proliferation of workload environments:

The old perspective that a single public cloud could address all complicated enterprise workload options is no longer widely held. Hybrid cloud has become the leading architecture for enterprises because it offers more value than relying on a single, public cloud.² Data fabric can simplify data management across these environments.

In this modern landscape, a data fabric can help lo address the regulatory landscape and the proliferation of workload environments. If data is geographically distributed, its value may only be realized once you aggregate certain of its elements and apply analytics.

With the right data architecture, data can be central to your organization, which can lead to more innovation and a data-driven culture.

How can you apply these learnings in your organization?

Beyond the architecture, data management tools and business analytics tools can help make sense of data and allow you to apply it for your organization’s business goals. To apply the four core learnings from the surveyed Data Value Creators, data leaders should realize that data is more than a technological foundation, it can play a critical role in helping the business use data and technology for digital transformation. This requires a mix of technology, culture, skill sets and capabilities. Long-lasting CDO should live each day with the belief that data is the foundation for business model innovation and foster it in others. This foundation allows you to not only articulate where you’re going, it enables the vision and insight that allow you to pivot as business strategy changes.