Agile data product management | EY

Chief data officers (CDOs) and chief information officers (CIOs) are focused on developing and implementing enterprise data strategies. Early efforts within the financial service sector focused on the defensive nature of data. The goal was to avoid billions of dollars in fines associated with inaccurate data and regulatory reporting. To satisfy regulatory inquiries, companies spent hundreds of millions of dollars on data strategies and the implementation of data capabilities such as data governance, data quality and centralized data hubs. With this significant investment, banks made incremental improvements in their data maturity, but struggled to demonstrate the business value gained from these efforts. This investment without measurable return pushed companies to pivot their strategies to use data to drive company growth. Data organizations began to work with business stakeholders to identify use cases where data could help drive revenue or reduce cost. While banks experienced pockets of success, these efforts were met with mixed results and rarely had a dramatic enterprise impact on a company’s data maturity or the broad adoption of the data capabilities. As the data technology landscape evolved, CIOs began to adopt a platform strategy and introduced concepts of a centralized data platform supported by new technologies such as data lakes, cloud, data mesh, machine learning and artificial intelligence with the intent to alleviate complex data problems and provide businesses with scalable and flexible capabilities. To this day, financial services companies continue to invest in data strategies and platforms hoping for improved data maturity that delivers business value.

As other sectors outside of financial services began to move through their own digital transformation and platform journeys, they realized the importance of having a well-formed data and analytics strategy. The early adopters in consumer product companies and health care considered the lessons learned from financial services. They realized previous attempts to achieve transformational change had three common problems that made success difficult to achieve:

1. Data and analytics efforts were driven by information technology (IT)
2. Business value was difficult to measure
3. Adoption of data platforms and capabilities was limited

What is needed to disrupt previous approaches and transform into a data-driven organization? A product-driven culture where data and analytics squads are focused on addressing stakeholder needs, learning from feedback, and relentlessly prioritizing business value. By applying the concepts of agile product management to data in addition to the reports and analytics that consumes the data, EY clients are achieving measurable results, global adoption of enterprise data platform, and enterprise-wide product cultural alignment. An experienced analytics organization has worked with finance to agree on value measurement of individual analytic products across supply chain, planning and forecasting to achieve a combined $200M of annualized benefits across their portfolio of analytic products. These outcomes lead to growth, agility, and greater investment toward data and analytics.

There are three key aspects that help clients successfully transform into a mature data organization that connects data suppliers and consumers via hundreds of data and analytics products:

1. Relentless focus on measuring value received from the data platform
2. Establishing an organization structure and operating model that partners the enterprise data team with the analytics team
3. Shifting the data and analytics culture to a product management mindset