Krish Krishnan

Krish Krishnan has been a driving force behind the evolution of data management for nearly three decades. He has worked designing organizational data strategies for one of the largest retailers in the world. Not to mention his roles as an author, educator, and thought leader on topics surrounding modern data architecture. Now he is deeply involved in digital transformation strategies for key Trissential clients. Krish took a moment to answer questions on data analytics consulting in Minneapolis and the necessary shift to data centricity.

Q: What do you see as the biggest advancement in how organizations view and use data?

A: By far, the idea that companies should view their data as a primary, invaluable asset is accelerating digital transformation forward.

Q: Name the biggest obstacle for organizations when it comes to digital transformation?

A: Many organizations believe that having a cloud presence means that digital transformation is complete. But there’s much more to it, and there are really two main issues. First, the organization doesn’t recognize stumbling blocks in the process before becoming major problems. Second, they fail to rely on management consultants and other experts who have been down the road before. It’s so important to understand best practices within the overall umbrella of digital transformation. Particularly, when it comes to processing alignment and opportunities for optimizing data analytics consulting in Minneapolis.

Q: Why is data one of the only remaining competitive differentiators for companies?

A: We’ve arrived at the point in our industry’s maturity where infrastructure needs to be positioned (and viewed) differently within the cloud with the goal of maximizing optimization. Yet to achieve any feasible success when migrating to the cloud, we need to align with our overall digital transformation goals. This requires separating digital, data processing as it flows across, and data transformations applied as independent, yet complementary components. When we do this, we can recognize data as the primary asset and position it correctly within the strategy as such. This is the essence of data centricity – a critical success factor when it comes to digital transformation. Having said that, data centricity and cybersecurity need to work together, coupled with agile transformation for process management. That’s the real recipe for digital transformation success.

Q: What is the primary difference between a data-driven and a data-centric mindset?

A: In a nutshell, a data-driven mindset follows data trails after an event has occurred.

A data-centric mindset establishes the lifecycle around data so you respond to events as they occur.

Search results algorithm and fraud detection are two excellent examples of a more proactive, data-centric approach. But, there are many more.

Q: What do you mean when you say we must learn to “uncouple technology from data?”

A: When we uncouple technology from data we can ensure that the data resides where it needs to, not where the software requires it. The goal here is to ensure the creation of formats like JSON for storing data and moving them across storage layers. We have highly advanced languages like Python and Julia that perform scientific computing on files, databases, and NoSQL platforms seamlessly. And perhaps even effortlessly. Why lock data into the requirements of a specific technology and then struggle to make it fit the needs of your organization? Look at AWS, Azure, and GCP – these are all excellent examples of strategies that deliver decoupled storage layers and databases of multiple formats to perform computations. Treating data as fluid in this way is another key to success. Especially when it comes to digital transformation and the achievement of goals within your specific initiatives and KPI’s.

Q: Why should we view data centricity as a “process” transformation, not a technology re-platform?

A: Data-centric transformation should be designed as three layers: The first layer is the data itself. The second layer includes all the processes and events surrounding the data as it swims across different iterations, while the third is a self-serviced query layer. If we create these three layers intelligently and correctly, innovative technologies like Denodo and Bigquery can provide a decoupled layer that integrates data processes and the data itself, hiding both the technology and the integration from the end consumer or user.

This aligns extremely well with any consumer who wants to go digital and remain secure. The process layer is where all the magic resides. The beauty is that it can be designed and deployed in a distributed fashion, with a micro-services component ready to integrate as needed.

Q: What are the benefits of applying agile as part of a data centric strategy?

A: By applying agile to data-centric strategy, we create a process layer that starts and ends as optimized and distributed architecture. Planning is a key part of agile and process sequencing is an end result of that planning. Once planned, we can continue to iterate until the component is delivered. Many case studies have demonstrated how applying agile has made a huge difference in the entire process.

Q: What organizations are “getting it right” in terms of data centricity? Why?

A: This is a beautiful question to answer. My response has more to do with the maturity of certain industries. For example, eCommerce is succeeding. As retail integrates more completely with eCommerce, we’re seeing advancements in terms of that industry’s maturity as well. Financial services are (for the most part) getting it right from the consumer’s point of view. Although their backend systems often need more maturity and alignment. Insurance is in the middle of the road when it comes to data centricity. And, healthcare is still at the starting gate. Obviously, it’s harder to move forward with strict compliance regulations such as HIPPA, PII, PCI, and Safe Harbor. So naturally, some parts of finance, insurance, and healthcare will take more time.

For more insights follow Krish on Twitter @datagenius.

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