S3 | Ep 5 | Why Data Literacy Won’t be the Cornerstone to Success with Malcolm Hawker, Head of Data Strategy at Profisee
Dec 6, 2022
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Malcolm Hawker, Head of Data Strategy at Profisee, discusses why data literacy isn't the key to success. Topics covered include the challenges of data analytics initiatives, implementing recommended data practices, rethinking data literacy and data-driven culture, exploring decentralization vs. centralization in data adoption, and the importance and benefits of data sharing.
Data literacy is a loaded term that turns the table of accountability, so data leaders should focus on treating data as a product and integrating product management disciplines.
Finding a balance between centralization and decentralization is crucial to overcoming cultural barriers and fostering a data-driven culture.
Data sharing can lead to cost reduction, efficient data management, and standardized data governance, but it requires a shift in mindset and a willingness to collaborate.
Deep dives
The importance of data literacy in organizations
Data literacy is often seen as a solution to the challenges faced in data analytics projects, but it can be a loaded and confrontational term. Instead of blaming business leaders for not being data literate, data leaders should focus on solving the problem from a product perspective. By treating data as a product and integrating product management disciplines, organizations can better understand customer needs and design data products that are easier to adopt and use.
Culture and the centralization-decentralization dilemma
Cultural barriers often arise in organizations when it comes to data management and governance. The centralization versus decentralization debate plays a significant role in this. Finding a balance between the two is essential, as both views are accurate and valid. Adaptive governance that addresses the challenges of decentralization and centralization is key to overcoming cultural issues and fostering a data-driven culture.
Data sharing as a solution
Data sharing can offer significant benefits in terms of cost reduction, efficient data management, and standardized data governance. By sharing certain types of data that are managed in similar ways across organizations, economies of scale can be achieved. This approach requires a shift in mindset and a willingness to collaborate, but it can lead to improved data quality, better insights, and reduced duplicated efforts.
The future of data analytics
Looking ahead, data analytics will continue to evolve, and concepts like blockchain-enabled data management may revolutionize the field. Embracing new technologies and approaches, such as integrating product management disciplines, adaptive governance, and data sharing, will be crucial for organizations to drive value from their data and analytics initiatives.
Challenges in data analytics projects
Data analytics projects often fail to deliver the expected value due to various factors, including a lack of clear business outcomes, disconnect between data initiatives and desired outcomes, inadequate measurement of data impact, and resistance to change. Overcoming these challenges requires a focus on quantifying the benefits of data investments, aligning data initiatives with business goals, and creating incentives that tie data-driven outcomes to executive pay.
In Episode 5 of Season 3, of Driven By Data: The Podcast, Kyle Winterbottom is joined by Malcolm Hawker, Head of Data Strategy at Profisee, where they discuss why Data Literacy isn't the key to success, which includes;
The benefits of starting his career in Product
His time at Gartner; the 1500+ conversations he had with CDO’s and why he left
Why there are so few successful use cases of Data & Analytics initiatives
Why so few Data Leaders do what they’d advise others to do
Why we talk a good game about failing fast but no organisation incentivises that to happen
Why it’s up to the Data Leader to instigate the required change required to improve the current dysfunction
Why most CDO’s are walking into Lions dens
How it is absolutely possible to quantify the commercial benefit of our data initiatives despite some claiming it’s not
How models can help us to quantify the value we create
Why we need to stop debating semantics
Why organisations continue to (rightfully) invest in Data & Analytics despite not seeing great success
Why the data community can come across as “preachy” and “finger wavey” in communicating best practices
Why data literacy is a loaded term that turns the table of accountability
Why it’s a product problem and not a user problem
Why it always come down to centralisation V decentralisation and why you need to do both!
The future of data sharing and the economies of scale it can create
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