Organizations have invested in various technologies and processes to become more data-driven. Historically, those investments have focused on using data only to enhance siloed decision-making. The evolutions in cloud, analytics, and AI are now pushing organizations to look beyond operations to infuse data into everything the organization does to become truly data-centric.
The vision of data everywhere becomes possible only when combining a strong data foundation with AI and GenAI. This blend enables both top-line growth and bottom-line value in numerous ways:
- Development of new products and services through a comprehensive understanding of customer behavior and patterns;
- Enhancements to customer lifetime value through refined experiences; and
- Minimizing operational costs with automation driven by embedded data and insightful analytics.
Achieving this means going beyond being ‘data-driven’. Enterprise data efforts have focused on point-in-time decisions delivered through self-service, personalization, and hyper-personalization. These fall short of data-centricity since they treat individuals as part of a group, with data and insights based on the least common denominator for a given domain and set of roles.
AI and GenAI can apply data in a much more refined way. With natural language interfaces, organizations can shape and deliver an individualized experience based on the unique needs of the specific job and the person executing that role. Data can be seamlessly embedded into every process, system, and product, becoming the common connective tissue that turns the entire organization into a virtuous improvement cycle engine.
Organizations must change how they approach data and evolve their operating models to enable wide-scale AI and GenAI success. Thankfully, there is a proven and applicable set of methods from the product world. We call it the Data as a Product approach.
The Data as a Product approach is a unique framework that blends product management discipline, design thinking practices, and agile principles to bring to life various transformations: use cases become user journeys, user interfaces evolve into user experiences, data projects become data products, and IT business alignment shifts into measurable business operating models.
Applying these models and frameworks, organizations move beyond point-in-time decisions to becoming data-centric, where everything the organization does is informed and enhanced with data. With data-centric solutions, individuals experience an intuitive and natural way of leveraging data in their roles, while data, AI and GenAI solutions work seamlessly in the background.
In the product world, designers think about user personas, user journeys, jobs to be done, and user experiences tied to what is valuable. By applying a Data as a Product approach, the data world can truly transform an organization, realizing the full potential of AI and GenAI while shifting from being data-driven to being fully data-centric.
