The Data and Culture Transformation Imperative

In 2022, every IT leader Lopez Research speaks with has embraced digital business concepts for at least part of their workloads. Today, these leaders understand that being digital isn't enough. We're moving beyond digital transformation to an environment where companies need to accelerate their ability put data its data into action. Companies also need insights to help them proactively support the business. I call this the digital acceleration era.

Technology was the gating factor in delivering insight in the past. We have a wide range of solutions to support data analytics challenges today. What's missing for many are the culture and processes that treat data as a product and strategic asset that's available to everyone throughout the organization. Experts from Discovery Health and Cloudera shared insights on creating data & culture transformation in an event hosted by TechCrunch.

The Chief Technology Officer of Cloudera, Ram Venkatesh, shared his thoughts on the technical requirements to create data culture transformation. Meanwhile, Shirley Collie, the Chief Health Analytics Actuary of Discovery Health, shared how the company used machine learning. She also shared how the company was integrating data from multiple systems to create new approaches for mitigating the risk of hospitalization with COVID-19, reducing patient readmissions, and ranking various health facilities.

During the event, I had the opportunity to share my thoughts on creating internal and externally connected, collaborative data ecosystems. If you read various reports from prominent consultants, you'll discover that each consulting and analyst firm categorizes data ecosystems into 4-6 types. Still, the two I've mentioned provide a good starting point for simplicity. The broadest definition of a data ecosystem is a collection of infrastructure, analytics, processes, and applications used to capture, store, analyze and act on data. 

When most consultants and industry analysts speak about data ecosystems, they discuss the collaborative ecosystem that connects to companies outside your organization. I prefer to start within your organization because most companies still need to build the culture, infrastructure, and processes to have accurate, connected data flows within a company. These require a robust data governance method to track data lineage and changes. Once you have this, you can participate in a collaborative ecosystem that expands beyond your environment to connect to partner and third-party data resources. 

Data ecosystems also require methods to process and act on the information at the edge, closest to where the data is created. Thriving data ecosystems provide new insight into applications so every group within an organization can make data-driven decisions.

I learned several important things about creating a data culture in the panel session that I moderated. First, Collie shared that people and process transformation is the real challenge and opportunity in today's landscape. She said the company had to create a culture of continuous learning and data sharing to be successful. I agree wholeheartedly with this point. Many organizations have limited visibility of all the available data sources. She also noted that "one needs to have a strong sense of the value of such data. There's an opportunity cost (when) low-value data sources are prioritized over other potentially more meaningful datasets. The entire organization must understand the importance of data, and then accurately (have) data available for analysis to empower future decision making."

Venkatesh shared his thoughts on how cloud computing and SaaS services would allow companies to minimize the time it takes to achieve value from the data. He shared how open protocols, open data standards, and open APIs are more relevant for data ecosystems. From the organizational side, he spoke of small, nimble teams that focus on specific business use cases to drive immediate value from data. On the technology side, he shared the need for improved data governance with "a distinction between policy versus the mechanism, especially when it comes to authorization. He added that the underlying authorization mechanisms must be able to scale to support increasingly large datasets. Venkatesh shared that predictable classification and data profiling will help companies avoid failures.

 Overall, the takeaway from the event is that data is a team sport. Different parts of the organization will have various skills, but it requires combining these skills to move the organization forward. You can view the whole session on the TechCrunch site here.