Not so long ago, the user experience of any app was determined by the technology. We all used Outlook because at the time it was the only option available, and users had to either adapt to the interface Microsoft gave them or … well, there was no “or.” Until Gmail showed up and showed us all that there is a better way.
Today, the user is in charge. Features like intuitive design, personalization, and voice control have become standard in consumer-facing technology (think Siri, Alexa, Uber/Lyft.) Users are now expecting similarly tailored experiences from their business applications. And with so many options available in all categories, they aren’t “stuck” with apps that fail to deliver the experience they want.
As data professionals, the question we need to ask ourselves is this: “Are traditional BI apps user-centric enough to meet the demands of today’s decision makers? And if not, can they make the leap?”
Before we dive into that question, let’s take a look at the evolution of business intelligence. The very first BI apps were developed when companies had limited amounts of stored data. Their basic function was to centralize information and deliver “dashboards” to serve as a readable interface between users and raw data.
Eventually, BI evolved into standalone apps that data analysts or BI team members used to perform ad hoc analytics and distribute the results to decision makers. These apps were more sophisticated in terms of the data they delivered, but were still not capable of driving action. In other words, they were still static dashboards, and executives could look at them all day long and still not understand which important changes were taking place … or which actions they needed to take.
Today, enterprises have data coming at them from all sides: big data, unstructured data, data from the “internet of things” (IoT), and data from countless third-party systems. For present-day decision makers, static templates with predetermined sets of questions are no longer enough. They need a contextually aware solution.
Enter the third phase of BI evolution: smart business applications. The ability to gather, store, and report on data is yesterday’s news; now it’s all about making the data work for the user.
Smart business applications represent the integration of BI tools with day-to-day business applications and workflows. They deliver relevant, timely, and actionable insights within the business application where the work is being done. Decision makers no longer have to stop what they’re doing and open another app to check the data. Now the data is right there in front of them the moment they need it.
Smart business apps release data from the exclusive realm of data scientists and BI teams, making it readily available throughout the organization to service the needs of both the ‘data elite’ and business users.
Let’s look at a real-world example.
Say an organization uses a fictional app called “ExpenseHero” for their travel and expense management. Here’s what the workflow within the app looks like:
Now, we all know that managers are busy — really busy. What are the odds that they have the time to thoroughly review every expense request that comes across their desks? Practically nil, and that’s where far too many companies lose far too much money. It’s not about employees committing fraud or acting irresponsibly; it’s about the lack of sufficient time and resources to enforce the guidelines that are in place.
Managers may well have access to dashboards telling them what their spend has been this quarter, how much of their budget is left, and who their top 10 “spenders” are. What the dashboard doesn’t tell them is which actions they need to take and when to take them. It’s a good “pulse check” to look at on a monthly or quarterly basis, but it has little to no use in making day-to-day decisions.
So, how can we make this workflow “smart” in a way that enforces good hygiene in expense reporting without adding to the manager’s workload? Here’s what that might look like:
This little recommendation window may not seem impressive, but it represents a tremendous amount of activity going on behind the scenes. The app probably has to run a machine learning algorithm to create that recommendation, taking into account a wealth of data to create a robust predictive model. For example, if John Smith typically submits expense reports in the range of $4,200 to $5,600, but suddenly he submits a request for $7,200, the app can recommend either rejecting the expense or giving it a closer look.
To deliver a reliable recommendation, apps must often reach outside the company for relevant information such as census data and details on key events. I’ll give you an example from my own experience: I once had to travel to Minnesota on business, and my trip happened to coincide with the Ryder Cup golf tournament. If you know anything about the hotel industry, you know that room rates can go up quite a bit during major events like this one. Sure enough, my room expenses were well outside my normal range, and the finance team rejected them.
Smart business apps can also tap into your CRM data to further refine their recommendations. Getting back to John Smith and his $7,200 request, let’s say that John is your top sales rep and that the size of his close amounts more than justifies the expenses he incurs as part of the sales process. Once the system “learns” this about John Smith, it can automatically adjust the parameters for accepting or rejecting his requests.
It’s an exciting time for business intelligence. With the arrival of smart business applications, we are closing the data loop by helping users turn insights into action. And by embedding those actionable insights directly into the application, we are