CEO Perspective: Future Trends in BI and Analytics
Is your data accessible? Is it coming too fast? Are your tackling AI and machine learning now or do you expect them in your future? Upside spoke with Sisense CEO Amir Orad to explore what’s hot now, what BI and analytics trends are on the horizon, and what tech offers a bigger opportunity to your enterprise than you may know.
Upside: What technology or methodology must be part of an enterprise’s data strategy if it wants to be competitive today? Why?
Amir Orad: To be competitive, you need to make data accessible to as many decision makers and operators in the organization as possible. Too many organizations have data locked down underground in data silos, making it inaccessible and difficult to use. That frustrates people, reduces innovation, and affects the ability to make decisions.
Second, the velocity of data today is such that any data strategy needs to assume that your data inputs, outputs, and requirements are going to change every month. As a result, you need to build a very agile system for data analysis. A waterfall method that takes time and effort to make small adjustments just won’t work. A typical approach is to balance the two extremes and have some data technologies that are more rigid and planned and some that enable more ad hoc and agile development. But you cannot function with just the former.
What one emerging technology are you most excited about and think has the greatest potential? What’s so special about this technology?
First, as much as it’s overhyped, machine learning and AI are also under appreciated. Today, models are able to detect everything from cancer to your insurance risk to the chance you have a car accident better than any human-built algorithm. ML and AI are going to transform our lives and put any organization that’s not leveraging them at a total disadvantage to the point of irrelevance.
ML and AI are also enabling a whole new industry of robotic process automation. We won’t be replacing artists or songwriters tomorrow, but we can replace a lot of other mundane tasks and free up people and businesses to spend time on things that just matter more. For example, I’m proud to say, through our work with GE Healthcare, we have seen them build over a dozen analytics applications taking advantage of modern machine learning and analytics in Sisense to better predict and automate processes that had been manual and less accurate before.
What is the single biggest challenge enterprises face today? How do most enterprises respond (and is it working)?
There’s two areas where enterprises used to have clear strengths that have now become weaknesses. First, most enterprises are proud, robust organizations with very strong culture, but in a majority of them, dynamic deployment of modern technology is not a part of the culture. Specifically, when it comes to analytics and data-driven decision making, many of these organizations don’t have the right DNA to take advantage of the latest technologies.
Second, these organizations have had the luxury of building gigantic IT systems, the strongest and the fastest on the planet. However, these IT systems were built historically with rigid controls and very limited access to data, so when you want to quickly and nimbly deploy analytics and data products in an enterprise, you’ll find that legacy to be limiting, not enabling. Those two things were advantages and are now becoming disadvantages and slowing enterprises down.
I view our role at Sisense as providing these organizations a bridge to let them leverage their amazing people and assets by getting them more quickly and easily into AI, ML, and other technologies and bypassing some of those historical limitations.
Is there a technology in data and analytics that creates a bigger opportunity for enterprises than they realize?
Logical data models, or what we call our semantic data layer, which is somewhat similar in concept to what was once thought of as logical data warehouse. That’s something we strongly believe in and we think there is an amazing opportunity to decouple the physics of data with the logical representation to business people. By decoupling it, you accelerate your ability to access and use data and ignore historical limitations.
What initiative is your organization spending the most time/resources on today? In other words, what internal projects is your enterprise focused on related so that you (not your customers) benefit from your own data or business analytics?