BI Trends

The Rise of the Genius App

By Charles Caldwell
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Apps have come a long way. Not just in user experience and design, but in what they actually allow us to do. When I look back at the 90’s and 00’s, applications were basically aimed at doing things faster. It was all about process efficiency, and technology was primarily being used to create those efficiencies.

A lot of this was based on our ability to communicate more effectively – paper goes away and we can communicate over long differences. It was also about creating digital artifacts. So rather than trying to track down that one piece of information you need on that piece of paper you can’t find, you can now take digital notes and create digital records – all of which are easily accessible.

While there are still a number of areas that we can apply this type of technology to, I would largely say that the efficiency problem has been solved.

Today, doing things “better” is not just about process efficiency, it’s about making better decisions in that process. It’s about being more globally aware of the context you are operating in, and applying that context to the very specific micro-decision you are trying to make in each moment.

Where once we would be okay with trying to make business decisions based on last’s month data, now we expect up-to-date information every time we need to make a decision. Moreover, the types of users making these decisions have changed. Data-driven decisions used to be made solely by executives, but today everyone needs to make these decisions, from those operating the supply chain, to those interacting with customers to,  in many instances, our customers.

What’s crazy to think, is that where we see this happening most frequently is in not in our enterprise, but in our consumer applications.

People love their smartphone apps, because they give users the information they need in the moment they want to make a decision, and it’s contextualized to the task. Take the Zillow app for example, which uses your location to give you information on the houses for sale near you, in your price range that are having an open house today.

Uber is another good example. When I open my app, I can see the cars circling, how long it’s going to take for a car to arrive, and the star rating of the driver. Immediately, I can make a decision of whether or not I want to take an UberX, a black car, or, if I’m feeling fancy, an SUV, or whether I need to hail a cab because the Uber will take too long.

The same thing applies to Amazon. When you are looking to make a purchase, you look at the reviews and apply that to your decision process. But let’s say you table that purchase and come back just a week later – your decision may change based on new information (pricing, availability, reviews, products) that Amazon is providing you. You revise the decision you were planning to make, based on this new information.  In the past, you would have never updated all that information by yourself – it would have been too difficult and time-consuming. But now, not only do the real-time data updates happen without you realizing it, you also don’t realize you’ve just reanalyzed your decision.

These are what we call “genius apps.” Genius apps focus on better decisions, not just faster process. Genius apps embed analytics into the fabric of the application experience. And genius apps drive strong user loyalty by making users better at the task at hand.

Meanwhile, too many times in our business lives that is not the case. We are making decisions based on operational and performance data from last week (if you’re lucky), last month, or even worse, last quarter.

That is not a genius app – that doesn’t help me decide whether I should approve a project budget or focus resources on one initiative over another.

As we increasingly demand our employees use data to make day-to-day business decisions, we also have to offer them genius apps that give them the information they need in the moment they need it to make a better decision.

To put it simply – genius apps help people do things better.

Genius apps (like any genius) understand exactly what problem they are solving, and they solve it well – in the context of a user’s workflow. It says, “Here is the most efficient process,” and combines it with the insight to say, “And when you are doing that, here is what you should think about.”  When done well, the analytics disappears in the fabric of the application, and users take on the best practices without even realizing it.

In fact, research has shown that if you ask an expert to make a list of what they consider when making a decision, and you give that list to non-experts, the non-experts do almost as well as the experts.

Consider the Amazon example again.  I am not a Bluetooth headset product expert. And yet, when I purchase one via Amazon, I get insights from the experts, from other novice customers, product comparisons, etc., etc.  What the above research says is basically the difference between me and the expert is that the expert know the process and knows what matters.  After that, how they weigh the information is the same as how I would weigh it.  (Repeat with Yelp, Uber, etc., etc.)

At the end of the day, the difference between a genius app and one that rarely gets used by your employees is analytics delivered in context of the decision they wish to make.

Business apps have come a long way, and now genius apps have arrived. And now it’s on you to create these applications for your business.


Originally published August 9, 2016; updated on August 9th, 2017

About the Author

Charles Caldwell is the Vice President of Product Management at Logi Analytics. Charles came to Logi Analytics with a decade of experience in data warehousing and business intelligence (BI). He has built data warehouses and reporting systems for Fortune 500 organizations, and has also developed high-quality technical teams in the BI space throughout his career. He completed his MBA at George Washington with a focus on the decision sciences and has spoken at industry conferences on topics including advanced analytics and agile BI.