Episode 26: Unleashing Your Full Digital Potential Through Data Democratization & The Right Tech Stack

Q&A

Chris and Doug, you both interact with data leaders all globally. What is top of mind for data leaders and, and what are the problems that they are focused on solving right now? 

Chris: In my experience, the problems I have seen among data leaders generally falls under two broad categories: 

1. Organizations are on-prem and are predominantly moving to the cloud. The question becomes, what is the sort of path to get there, how do they ensure that the data is protected in the cloud is particularly important for regulated businesses? 
2. The second group is, and sometimes these happen simultaneously, is many of the organizations are already in the cloud and they're trying to understand what they need to do differently from the traditional ways of processing data that they were used to in the past to move forward?  
   

And so, when it comes to the data estate, it is essential to make decisions that would suit their needs, like am I going to choose a vendor solution to work with data or a series of vendor solutions? Am I going to go with cloud native services and what are the pros and cons? 

Doug:

1. Fully leveraging and integrating technologies continues to be a significant challenge, such as integrating analytic output directly into operational systems and strategic processes. 
2. Keeping up with advances in data management, analytics, and AI is particularly challenging right now. But I think some of the bigger challenges are a bit softer. They relate more to data literacy and organizational change. What data do we have? How can we use it? What is the art of the possible? How can we position our organizations or reconfigure our organization to get people on the same page when it comes to how we're going to become data-driven? 
3. I also think innovation is a challenge. Too many organizations are stuck doing hindsight-oriented reporting and creating dashboards and really not thinking about or planning how to use data to diagnose, predict or prescribe or automate things.

Chris, when you were just talking about the shift from moving from on-prem to cloud or being in a multi-cloud environment, what are some of the challenges that organizations face during these shifts? 

Something we talk about Databricks a lot is people process and the platform for us, and ultimately how data relates to all of it. Organizations need to put together a well-defined strategy. Where I see a lot of the friction occurring is how do we turn that strategy into reality? How do we actually get the data into the systems that need to generate the insights and need to enable people to do their jobs more efficiently and enjoy it more? The data is vital to the success of the organization along with its people. How do you make it so that the data is really accessible, but secure? And what do you need to do to ensure that employees enjoy their jobs as much as they possibly can when they're at work? 

Let’s talk about generative AI, the topic of the moment. What are your thoughts on generative AI?  

Chris: We at Databricks certainly subscribe to the importance of GenAI. I think it's going to show up in two meaningful ways in the near term. Personally, I think it's going to show up in a lot of vendor products that are content generating products. So, take your Google apps, Microsoft office, and PowerPoint presentations can at least be notionally created for you and save you a bunch of work there. I think in terms of enterprises, you're going to be able to leverage it for more customer service and customer support type work. 

Doug: I’m particularly enthused by it, especially because this is the kind of vision we’ve all had in the AU industry since I was working at it in the late 80s and early 90s. Now finally we have the horsepower, content, and the ability to process data in unique ways. I'm initially very enthused about the ability for generative AI, as Chris mentioned, to generate content and code, but also perform analytics. I just had an analytics dialogue with one of the large language models to analyze some survey data that I generated, and that half hour conversation with it was like having a conversation with a data scientist. But I think beyond this, I'm much more excited about the possibilities of automating and integrating processes 

How do you think about tool selection or how do you advise clients on what tool(s) is/are needed to make data actionable? In your opinion, how many tech providers should companies use? 

Doug: I think looking for a technology provider that can be a true partner who can, as we say at West Monroe, meet you where you are and help you move forward is important. Too many technologies require too much of a do-over, starting from scratch or somewhere near that. We also recommend selecting technologies based on intended needs and uses, not a comparison of features and functions and which one has the most features and the longer feature list. I also think, as I mentioned earlier, we need to be aware of available skills. If you don't have those skills internally, ensure that they're readily available to support implementing whatever technology it is that you're going to choose.  

Chris: To me, it’s not about the quantity but about quality. It is really the partnership and feeling like the vendor understands where you are today and where you are headed. Another critical piece is enablement. This shows up in the form of training and/or professional services, or with a strong partner network with consultancies that can come in and are familiar with the individual tool or platform.