Data-led digital innovation: What you need to know

Building a modern data estate isn’t easy. In many cases, modernization requires a complete overhaul of data and business strategy. Due to the huge workload involved, more and more companies are turning to expert partners for guidance on how to make data a core component of their cloud journey. But what does it mean to take a data-led approach–and how does it differ from an application-led approach?

“Data-led” vs. “application-led”

“Application led” innovation. . .

With a “data-first” mindset, modernization involves continuous and iterative design that enables an organization to remain agile, able to quickly and smoothly pivot when needed, market shifts, and customer demand. The data-first model is also much easier to replicate and scale, making it ideal for companies building a growth strategy.

journey to digital transformation

Building a Modern Data Estate

A Modern Data Estate enables organizations to manage and consume their data in real-time. First, it’s critical to establish an enterprise-wide data hub that includes a data warehouse for structured data and operational analytics and a data lake (or lakes) for semi-structured and unstructured data.

Next, integrate relational data sources with other unstructured datasets using Big Data processing–use semantic modeling and visualization tools for simpler data analysis. The image below will give you an idea of how this might look.

The pivot to becoming a data-driven organization represents an enormous opportunity for solution providers to become catalysts for digital innovation. To fully capture this opportunity, we must re-imagine how solution providers engage customers on their cloud journey by shifting from application-only to application- and data-led approaches.

This opportunity requires a shift to conducting early discovery efforts and assessing the data estate as a whole–apart from any singular application–to ensure repeatability and scalability across current and future customer use cases. Partners looking to adopt a data-led approach can be early-to-market with this model that’s not yet offered at scale.

Things to be aware of include:

  1. Design considerations – A data-led approach should be designed around two best practices:
    • Continuous and iterative design to prove value quickly
    • Repeatability and scalability to catalyze value realization for customers and increase profitability for solution providers
  2. Business outcomes – When defining success with the customer, always refer back to the Modern Data Estate litmus test (does the investment deliver on reduced costs, improved performance, reduced risk, and advanced analytics?)
  3. Managed services – To maximize customer value while maintaining healthy margins, attach managed service offerings across Foundational Services, Data Availability Services, Intelligence Services, and Experience Services.