The board of a fictional mid-sized organization decides it’s time to double down on advanced analytics and AI and commits to bringing in an ambitious leader: Anne. Even before she started as CDO, during the interview process, Anne was made aware of the organization's current outstanding technical issues: siloed datasets, broken pipelines, and legacy code.

This state of affairs does not scare her because she has dealt with similar situations. However, she does not expect what will come next: a smaller, nimbler competitor enters the space, and at the same time, her budget is slashed in half due to unforeseen global market performance affecting company operations.

This pressure forms the backdrop of Anne’s first 100 days. The board sets two targets for her:

Steady the ship and set it up for long-term success. To achieve the first goal, she must instill confidence in her team with quick wins: visible products clients and management can get excited about.
The second goal requires her attention to topics her superiors often ignore, such as data quality and governance. Based on previous experience, she knows that even the best pilot products will eventually fail without a solid foundation, hampering the organization’s ability to compete.

Anne’s story might appear overly dramatic, but many CDOs have experienced this tricky balancing act (DalleMulle and Davenport recognized the dichotomy of strategies). Short-termism under pressure is a common mistake, resulting in an unbalanced strategy. How can we, as data leaders, successfully navigate such a scenario?

“Working under pressure and with limited trust from senior management can force first-time CDOs to commit to an unbalanced strategy, focusing on short-term, highly visible projects – and ignore the essential foundation.”

Anne knew she was not the first leader to face such a situation, so she looked at what others had done. For example, David Castro-Gavino, VP of Data at HelloFresh, started by identifying concrete enablers for his organization. Data quality, governance, platform, and culture were already a part of their data strategy, but he needed to dig deeper to discover concrete work packages.

At the time, HelloFresh’s customer base expanded rapidly – the COVID-19 pandemic forced more people to order food from home. This exponential growth brought a considerable challenge in the time taken to access actionable insights. Moreover, the company’s public status provided an additional regulatory incentive to invest in data quality and granularity.

A second major work package he committed to was also focused on the longer term. He realized that to enable new advanced analytics products at the next stage of the data strategy, he needed to increase the number of purple people (those who possess a mix of business and technology skills) in the organization. For this, he spearheaded a data culture work package called “HelloData YOUniversity” — an upskilling program for business users.

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