A featured contribution from Leadership Perspectives, a curated forum for enterprise technology leaders, nominated by our subscribers and vetted by the CIOApplications Editorial Board.
The HEINEKEN Company
Unravelling Data's Role in the Digital Evolution
Central to enabling digital transformation and its associated business value is data. However, the full extent of necessary data transformation is frequently underestimated, even though the outcomes of digital transformation depend on it.
Drawing from over a decade of cross-sectoral digital transformation, what insights have I gained about data's role?
Starting with the Customer: Anchoring Digital Transformation on Customer Needs
During my tenure at Barclays, a prominent UK financial institution, customer and client data guided our digital transformation efforts. We assessed what data was pertinent to customers for better financial management and investment decision-making. Behavioural data modelling played a pivotal role in prioritizing service features that catered to customer needs. Forecasted adoption rates and adjustments in digital transformation priorities were guided by these insights.
Establishing a Solid Data Foundation and Fostering a Data Culture
In my current role at Heineken, a global brewing company, I focus on building the necessary data foundation for digital transformation. We examine the standardization of data required for automating crucial manufacturing processes. Identifying global datasets necessary for critical reports and scaling advanced analytics is also a priority. Strengthening data quality, harmonization efforts and data collection to support the circular economy are key considerations. A robust data foundation facilitates interoperability across systems and the scalability of analytics use cases. Data is a team sport: it requires collective participation. Fostering a data culture integral to company values is crucial. Demystifying data topics and making them accessible encourages imaginative data use.
Prioritizing Quality Over Quantity: The Efficacy of Relevant Data
Through years of experience, certain principles remain consistent across sectors and nations:
Few individuals within an organization have the time or inclination to extensively interpret data. The question ‘What has changed and why?’ is of paramount importance. Automation serves as a solution for most other scenarios. Simplifying the data experience, achieved through automation and concise insights, demands substantial groundwork in establishing the data foundation.
Looking Forward: Balancing AI and Human Decision Making
While AI utilization is racing forward, conscious design within businesses for AI-driven decisions is often lacking unless mandated by regulations. Determining where AI is best suited for precise decision-making and whether data quality supports high-calibre models poses questions. Advancements in AI call for robust governance, akin to ingredient quality and certifications, for distinguishing 'good' from 'bad' AI products and ensuring transparency in AI deployment.
The book "Noise" by Kahnemann, Sibony, and Sunstain exposes the biases in human decision-making, highlighting algorithms' reliability. As AI advances, Kevin Kelly proposes AI's role as a 'co-pilot' alongside humans.
Collectively, crafting organizations that integrate digital co-workers and employees from all tiers is crucial. Additionally, a transformative shift in IT is on the horizon, reshaping data flows, developer roles, and platform designs. The value derived from digital transformation hinges on our imaginative utilization of data and AI. As we forge ahead, the possibilities are vast, only limited by the extent of our creativity in leveraging these tools effectively.
