Modern Data Leadership

The Guest

With over 25 years of experience in data and analytics, Malcolm Hawker is Chief Data Officer at Profisee and a recognized thought leader and advisor in the field of Master Data Management (MDM) and Data Governance. Having helped some of the world's largest businesses to improve their enterprise information management strategies, Malcolm is constantly sharing practical and actionable insights on how to leverage data as a strategic asset, and he’s here today to talk about modern data leadership.

The Genesis of Modern Data Leadership

Malcolm Hawker began his journey into data leadership during his tenure at Gartner, where he interacted with over 1,500 Chief Data Officers (CDOs), CIOs, and data analysts. This unique experience allowed him to understand the common challenges data leaders face, especially around adopting best practices in data management.

"Data leaders are following best practices, pushing these boulders uphill, yet self-assessment surveys consistently show that only 44% of CDOs say they're delivering meaningful value to their businesses," Malcolm notes. This alarming statistic reveals a gap between effort and effectiveness, leading Malcolm to explore why data initiatives often fall short.

The Problem: Traditional Mindsets in Data Leadership

In his e-book, "The Playbook for Modern Data Leadership," Malcolm argues that the real issue in data management isn't just a lack of technology or skills—it’s a matter of mindset. He introduces the concept of "negative mindsets" versus "growth mindsets" in data leadership. Drawing from Carol Dweck’s psychological theory, Malcolm stresses the importance of a growth mindset: an openness to learning, embracing feedback, and willingness to break away from the status quo.

"We suffer from what could best be described as negative mindsets," he says. "We need to shake free from the addiction to the status quo if we want to realize the true transformative value of data."

The Common Pitfalls: Blaming Culture and External Factors

Many data leaders point fingers at external factors when data initiatives fail, citing a "lack of data culture" or "poor data literacy" within their organizations. This, Malcolm explains, is a classic example of having an "external locus of control," a mindset where one blames others for setbacks rather than taking responsibility.

“There’s a lot of finger-pointing,” Malcolm remarks. “Data leaders often say, 'You don’t embrace data, so I can't help you.' This negative mindset traps us in the status quo.”

Redefining the Role of Data Leadership

A key aspect of modern data leadership is adopting a customer-centric, product management approach. Malcolm suggests data leaders should think of their roles like running a business. He poses the thought-provoking question: "Could you run your data function as a profit-and-loss operation? Could you put a price tag on every dashboard and ask, 'Would someone pay for this?'"

This shift in thinking from simply providing data services to running a customer-focused, product-driven data organization can help data teams align their efforts with tangible business outcomes.

Data Quality: An Opportunity, Not a Burden

Data quality often gets a bad reputation in data management. However, Malcolm reframes this challenge as an opportunity. "Data quality is not a burden," he states. "It’s an opportunity to improve our products and services."

Malcolm emphasizes the importance of understanding the context in which data is used. "Every one of those 800 instances of 7-Eleven in a database may be fit for its operational use. When it comes to analytics, we might label it as ‘bad data,’ but that ignores the valid reasons it looks the way it does."

The key takeaway is to stop viewing data as strictly "good" or "bad." Instead, Malcolm encourages data leaders to understand the context and trade-offs made during data entry and processing.

Navigating the Probabilistic Nature of AI

As organizations look to leverage artificial intelligence (AI) and machine learning, Malcolm underscores the need for a mindset shift. Traditional data management is highly deterministic, viewing data as either correct or incorrect. However, AI operates on a probabilistic basis, requiring leaders to embrace uncertainty.

"AI requires us to jettison our deterministic, binary, either-or thought patterns," Malcolm asserts. "We must rethink our approach to data governance and management to support AI’s probabilistic nature."

Malcolm points out that 95% of companies have yet to fully operationalize AI. To harness AI’s potential, data leaders need to let go of perfection and embrace new ways of thinking.

The Future of Data Leadership: Embracing Change and Driving Value

The conversation with Malcolm Hawker reveals a critical need for modern data leadership, driven by a growth mindset, customer-centricity, and a willingness to embrace probabilistic thinking. In Malcolm's words, "We need to start thinking differently about our roles, our customers, and the data we work with. Only then can we become modern data leaders."

For data analysts, marketers, and leaders alike, this approach challenges them to redefine their relationship with data, not as a burden but as an asset that, when managed correctly, can unlock strategic business value.

Key Takeaways:

1. Mindset Matters: The biggest obstacle in data management isn't technology; it's the mindset of data leaders. Shifting from a "negative mindset" to a "growth mindset" is crucial.
2. Customer-Centric Approach: Run your data function like a business, focusing on serving internal customers and adding tangible value.
3. Data Quality as an Opportunity: Stop viewing data strictly as "good" or "bad." Understand its operational context and the trade-offs that shape it.
4. Embrace Probabilistic Thinking: AI operates on probabilities, not certainties. Data leaders must adapt their governance and analytics strategies accordingly.

The Links

Malcolm on LinkedIn
The Playbook for Modern Data Leadership

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