Are you ready for the Chief Data Officer challenge?


The primary obstacle to becoming a data-driven business is that data is not readily available, leaving valuable insights unused in data silos. To overcome this hurdle, today’s companies are creating a new role: Chief Data Officers (CDO). Responsible for unlocking insights hidden in data silos, the CDO is tasked with improving data management, analytics and decision making. Duties range from organizational changes, to process and technology improvements, all with the goal of treating data as a corporate asset and gaining additional business value from data.

To achieve this, the Chief Data Officer drives change and implements a new culture of using and managing data. This corporate culture change requires the CDO to pull many strings in a gentle and coordinated way without disrupting the business. In fact, orchestrating the diverse activities of education, overcoming resistance, tearing down technical barriers, and implementing new data processes and technology puts the CDO into a data decathlon – the “CDO Challenge.”

It requires strong leadership, vision and a solid understanding of the market, the business goals and the organization's capabilities to be successful. Focusing on communication and education is key to changing the mindset across the whole organization. Only a clear vision and a common perception of this vision sets the organization up for a successful transition into the data economy. But before any transition can be kicked off, the CDO will need a clear picture of the company’s strategy, the data available and the company’s capabilities.

Let’s take a closer look at the three main things a CDO needs to know to successfully handle the CDO challenge.

1. Business goals.

The first task for the CDO is a full understanding of the business goals. For example, digitalization is not an end in itself, but is key to achieving many organizational goals. What are these business goals? And how does data, available internally and externally, support these goals? All data governance activities should be based on business needs and be in support of the overall business goals to avoid implementing features for features’ sake. It is crucial for the CDO to prove business value for every activity initiated, and it’s easier if activities are aligned with the company’s goals.

2. Data available, internally and externally.

Next up for the CDO is assessing the available data. The task of compiling the data elements available and creating a catalog to describe all data elements and its usage, helps the company, and the CDO, unlock hidden insights. The goal of this assessment is not to find anomalies within the data, but to fully understand what data can be used to help accomplish the goals of the business. Data is the fuel of modern business processes, therefore knowing the data will aid in designing better business processes. At the same time, knowing the deficits of the data helps the CDO to take appropriate actions to cure or acquire additional data.

3. Organizational capabilities.

The analytical lifecycle is the process that takes data and turns it into decisions or actions. But even with plenty of data availability, the organization might not be able to refine it into insights and actions. That’s why the CDO has to also assess the company’s ability to turn data into decisions. A clear picture of the organization’s analytical maturity allows the CDO to initiate improvements or changes to the analytical lifecycle when needed.  This can affect people, technology and processes across business and IT departments. The end-to-end process from data to decision is not owned solely by IT, and it’s also not up to the business to accomplish the process on its own.

Bringing the many players from across the organization together to collaboratively form the analytical lifecycle needs to be a key part of the cultural change introduced by the CDO. It’s crucial for this transformational change to align all forces and activities, create a common understanding and work towards a common goal to be successful. But technology plays a vital role in enabling the analytical lifecycle, as it allows the players to access, discover and analyze data to generate insight and make decisions collaboratively. On the other hand, not using an enterprise analytics suite limits the company’s abilities to turn data into actions.

Being data-driven is about giving decision makers the power to explore data and make predictions. Help your business stakeholders with tips from this collection.

How should the CDO be equipped for the challenge?

Every Chief Data Officer charged with the task to improve the data to decision process will have to make investments in people, processes and technology. Having the brightest talent is not sufficient if they’re not working toward a common goal or don’t have the right data at the right time.

A modern analytical platform makes a CDO’s life easier, and is needed throughout the transitional journey. Data management and analytical capabilities are beneficial during the initial assessment, and for the long haul. Using a single enterprisewide analytics platform helps improve efficiencies and speeds up the decision making process. Having all participants of the analytical lifecycle work on a common platform, sharing the same technologies and views, is highly beneficial as well.

A modern analytics platform provides the building blocks for a data-driven business and facilitates the complete data to decision making process. SAS can support CDOs for the challenge, and in all phases of the analytical lifecycle by providing the required technology building blocks to support the analytical lifecycle with the capabilities needed to run a data-driven business. Learn more on how CDOs and organizations can benefit from the SAS Platform.


About Author

Helmut Plinke

Principal Business Solutions Manager

Helmut Plinke acts as Principal Business Solutions Manager for SAS, focusing on data quality and data governance technologies. Helmut is an enthusiast of data quality technologies to improve fitness of data and thereby help businesses to improve efficiency and gain competitive advantages. In his current role Helmut supports customers in designing enterprise data management solutions based on SAS technology. He is a specialist in data quality and data integration technologies for a long time now and has been part of some of the major SAS data quality and data governance projects in DACH and the Netherlands recently. With over 15 years of experience across multiple industries Helmut has also gained a wealth of knowledge and experience in technologies like business intelligence, content management and enterprise application integration from his past roles with other companies. Helmut has published in IS Report and speaks about the topic of data management at SAS and public conferences sharing his project experience and knowledge.

Comments are closed.

Back to Top