Data is a rich asset. As we push the boundaries of available data beyond Zettabytes and into Bronobytes and Geobytes, how will we achieve accurate data intelligence? According to Forbes, 90% of the world's data was generated in the last two years with 2.5 quintillion bytes data being created each day.

Is all data equal, and how much data do we need to address our questions?

In talking with Kamal Distell, from Toyota Motor Corporation, "there are similarities in how we think about data governance, collection, labeling, architecture, etc., across all industries." These foundational elements remain static, regardless of which industry or company you work for.

"It is the context that is different across each industry. Yet, the context of the data can be learned." Kamal adds, "As we increase how we gather more information, there will be a hypothesis, and the data will either refute or accept."

In the 1990s, the term Data Mining became more mainstream in the database communities. It was one of the foundational concepts of my Master's degree in Management Information Systems, focusing on Data Management. I was more vocal about the declining expectations as data increased in ways we could no longer imagine; i.e. how could we store and report on meaningful data. As we continue to collect and store beyond terabytes of data (this was in 1996), the relational architecture would no longer be sufficient. What was working, in theory, would not work in the real world.

Moving forward to 2022, there are many more levels above a Terabyte just so we can quantify the amount of data accessible today. We are having the same conversations we started in 1996. How do we wrap our arms around the right balance of relevant data to support the business challenges, to create an effective decision at a time when that decision has the most impact?

Kamall and I discuss the concept of minimal viable data, which will be a critical component to the future success of data intelligence. Additional topics in under 30 minutes include: - Value of working with data across industries - Are we able to solve any business challenge with data alone? - Future of Data Trends - Minimal Viable Data - 80/20 rule from the past. Is this still relevant today? - Using data differently - Lessons learned