In the ever-evolving technology landscape, data analytics and data strategy continue to play a larger role in economics and business models. Director of the Center for Applied Artificial Intelligence at the University of St. Thomas, Dr. Manjeet Rege, co-hosts the "All Things Data" podcast with adjunct professor and Innovation Fellow Dan Yarmoluk. The podcast provides insight into the significance of data science as it relates to business models, business economics and delivery systems. Through informative conversation with leading data scientists, business model experts, technologists and futurists, Rege and Yarmoluk discuss how to utilize, harness, and deploy data science, data-driven strategies, and enable digital transformations.
Rege and Yarmoluk spoke with Jote Taddese on big data in larger organizations. Jote brings over 20-Years of proven experience in Enterprise Architecture, Data Analytics and Information Management strategy development and execution in large enterprises. As an IT Strategist, Jote has extensive experience advising Business and IT executive leaders on data & analytics driven digital transformation strategies. As Non-Profit board leader and a social entrepreneur, Jote has worked on innovative initiatives that are brining vital educational and healthcare services for communities across Africa.
Here are some highlights from their conversation.
Q. In your case you were thrown into Target and medical field where big data is available. How have your experiences shaped your understanding of big data?
A. First of all, the terminology big data, depending on who you talk to, I find it to be somewhat loosely understood. Especially for organizations who have traditionally been in the data management space. Because the process and the discipline and even the technologies that go behind enabling big data technologies is different. And there is a considerate amount of upscaling or rescaling of resources that are required to harness value out of big data. Also, Dan you mentioned data volume. Yes, data volume definitely is a critical component of the big data system, but also there is also a lot to be said about data accuracy and data completeness as well. Because that drives the quality of the analytical outcomes, the business insights, the predictability insights, that data scientists would provide to the businesses. Even the data engineers and the data wranglers, they sometimes need raw and unmanipulated data, but the accuracy, completeness, and cleanliness of the data really matters. Because that is what drives productivity when it comes to data driven business capabilities.
Q. So can you tell us about your journey with large organizations in respect to the cloud? What is your perspective on whether the future of the cloud?
A. There is no question that when it comes to realizing business value out of big data opportunities, cloud technologies, cloud providers, have really been charting the path. But I also think that when it comes to cloud adaption, it is a journey. There is a level of maturity that organizations have to go through, and organizations find themselves at various spots on that maturity curve. It also depends on what business those organizations are really in. Because the decision criteria that let's say a retail company would take to accelerate their cloud adaption versus the decision criteria that a mettech company would take are somewhat different. They are governed in various industry requirements and business demands. But one thing that I have seen in my experience is that the need to embrace and fully leverage the value that cloud technologies provide is no longer a question of if, it’s a question of when. And I've seen for example in the major corporations that I have worked in, in the retail space there was actually a more accelerated and rapid move toward adapting the cloud technologies. But also balancing it with cost when it comes to making that jump.
Listen to the full conversation below:
