What do cars, snacks, 401ks, and spies have in common?
by Adam Ferrari
Adam Ferrari
I’ve had a couple of busy weeks of customer interactions, and although the travel is not something that my family is particularly happy about, listening to technology leaders on the front lines is amazingly energizing on a professional level. In this recent series of meetings I saw a large auto maker in Europe, a leading food and beverage maker, a large financial services company, and a US intelligence agency, among others.
The particular aspect of these discussions that I found so striking: the people I talked to came from an amazing breadth of organizations, yet they faced the same fundamental business challenge and underlying technology challenge. Each owned the goal of delivering information to his organization to increase its competitive advantage. And this meant that each faced the technical challenge of enabling end users to understand information too complex and messy to fit into relational BI schemas, via analytics and visualizations that are outside the scope of what search engines provide. They needed a hybrid of search and BI, a convergence that I’ve written about before.
One example came from the European auto maker; they were trying to get more comprehensive views into their supply chain, with data coming from a wide variety of sources that are changing and evolving all of the time. Meanwhile, at the food and beverage maker, they were trying to get more timely and comprehensive views into their sales and distribution channels across a variety of divisions, products, and geographies. The very next day I met with the financial services company, where they wanted to provide interactive reporting on 401k participation to the plan sponsors that they serve. And finally, people in the intelligence community have ever greater urgency to make use of their wide variety of intelligence assets to keep citizens safe.
What I find amazing about this breadth of use cases is that they all reduce to very similar application requirements:
- Most fundamentally, they all need to analyze complex, heterogeneous data – data that is much easier to represent in a semi-structured form than it is to model as a relational schema, especially as it changes and evolves over time.
- Secondly, they all need the ability to flexibly search and filter the information set. But more importantly, they all require the ability summarize and analyze the data using numeric OLAP-style analytics, and all at interactive speeds.
- Thirdly, as enterprise analytic applications, they all live in an environment where custom application coding is highly undesirable; rather, out-of-the-box configurable analytics UIs are the expectation.
As was the case with the first generation of BI, a wide range of business problems across many industries actually reduce to common set of technical requirements.
I find that insight amazingly energizing. I got into software to build tools that lots of people would use and love, and derive great value from. It’s gratifying to see firsthand evidence that very broad and important unmet needs are still out there in the market where you can do exactly this.
