Digital transformation reimagines business models by integrating digital capabilities with people and processes. Over the past few years, you’ve made material investments in your digital culture, governance, and architecture. The industry winners are beginning to exploit their analytics capabilities through faster insights, data-driven actions, and automation. Of course, with great success comes even greater demand.
The new year offers us an opportunity to pause and reflect on what we have learned and what we envision for the next three years. One thing is for sure, we have learned how to streamline new cloud services; just count your new data lakes and warehouses. As we look ahead, we are facing a growing challenge. How are we going to streamline business access to the existing and future data sources? Let’s be clear on the problem: This is not about granting someone access to a database table or field. Rather, in a digitally transformed organization, this is about providing business consumers with access to aggregated data products that they understand and use in a self-service manner.
Three New Considerations for Digital Transformation
We don’t have the time or resources to redesign our data architectures. Our business teams can’t afford another disruptive enterprise migration exercise. To maintain a competitive pace in the digital race, we need to leverage the architectural investments that we already have in place. Before you hit the reset button, consider these three points:
#1. Only a small percentage of your data sources will require migration to a single lake/warehouse environment. Identify the use cases that demonstrate a clear ROI. The tangible value will help your business teams accept the longer term costs and potential short-term disruption. By sharpening your scope, you can accelerate delivery in the areas that matter the most.
#2. Most of your data is already in a suitable data store, there is no case for migration. Replace that multi-year data centralization program with an agile Data Mesh strategy. Starburst can be quickly onboarded (i.e. in weeks) to connect to and run fast analytics against your existing data sources. The business case demonstrates a low cost of entry with immediate return. As you add new data sources, the cost is consumption-based and the onboarding of any source follows a repeatable standard model.
#3. All of your data sources should be integrated within a consistent Data Mesh. The data that you selected for centralization and the data across the other sources will benefit from the streamlined Data Mesh access to contextual data products. As you move forward, data sources will be added and subtracted. The mesh provides you with a consistent framework for managing the integration demands for today and tomorrow.
You don’t need to reset your strategy, you can’t afford losing competitive positioning. You do need to prepare for continued change as digital capabilities are even further enhanced with 5G and AI. It is time to focus on the business as digital transformation programs must be dominated by business solutions, not data architecture.
Abstracting the architecture complexity from the data consumer is a key principle of the Data Mesh. Architects should have the flexibility to make design decisions to optimize cost and performance on the data back end, without creating disruption for the business teams on the front end. If you can deliver valuable data products to the business teams, the focus shifts to insight and results, rather than lakes and warehouses.
The digital transformation journey is a race. With the right Data Mesh strategy and Starburst you can leverage your existing investments to accelerate your business outcomes.
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