Best practices

Analytics Without Behavior Change Is Just Expensive Artwork

Analytics can have a transformative effect on business, but it requires highly-intentional design around the user experience. That’s because the ultimate goal of analytics is much deeper than just showing people how they are performing – it’s about driving behavior change. 

After 20+ years building data and analytics tools at companies including Oracle and Microsoft – and now Incorta – I’m amazed by how many teams still struggle to grasp this simple, yet powerful truth at the heart of analytics. 

So how do you build analytics that actually work? The best approach is thoughtfully designed analytics experiences that encourage and coach users in accomplishing their goals and objectives. If you believe people do their best when they are empowered and supported, then your analytics should do the same thing. Dashboards that function as basic scorecards don’t work. What’s needed is an experience that lights a fire within users – not under them. 

For example, let’s say you run a customer call center and productivity and customer satisfaction are your two leading KPIs. Following the traditional scorecard model, you shame or guilt people into productivity, and before you know it, they stop using the analytics altogether. Resist this temptation – unhappy representatives provide poor customer satisfaction, defeating the point entirely. Instead, you want to focus on answering the following question, “How can analytics help the team make better decisions?”

Where do you start? The first step is understanding how data and analytics are being used in your organization today. That is, figuring out who is using what types of data, in what ways, and how frequently – as well as the impact existing use cases are having on your business. These insights are critical because they provide a baseline for your efforts. Once you have a clear view into current usage and behavior, you can begin to set goals and strategies for driving even better and more productive use of data and analytics. 

Generally speaking, there are five key questions you need to answer:

  • How many reports have we generated in the past year?
  • How many are run on a weekly, monthly, or quarterly basis? 
  • Which teams (or individuals) are generating the most reports?
  • What are the overhead costs of running these reports?
  • What is the net impact on top and bottom-line growth?

If you already have an analytics solution up and running, finding the answers to most of these questions should be easy – just ask the software provider. If they don’t have the ability to share these insights, ask if they will consider adding it to the product roadmap. That shouldn’t be an issue since basic reporting like this doesn’t require advanced new technology on their part.

For those who can’t get these sort of insights from their analytics provider – or who are just getting started and don’t have a sophisticated system in place – building a custom solution is another option. Again, this isn’t rocket science. There are tons of creative options for capturing these insights manually: database triggers that log prespecified events or application hooks connecting to Google Analytics, for instance, are a couple lightweight options. 

Let’s be clear: If people in your organization are using data to make important business decisions, then there is no time to waste. There are huge risks in not knowing what data people are using and how – and it only gets worse over time. What’s more, when you consider the broad shift to metered, self-service cloud services, along with the low rates of data literacy in the workforce today, it’s clear the need for better cost controls is growing – fast.

As large IT workloads move to the cloud, there are two important consequences for data and analytics: First, cloud-based services are almost all metered – the more data you process, the more you pay. Second, modern cloud-based data and analytics systems are largely self-service. Both have advantages, of course, but they can also lead to trouble when left unchecked. If teams inside your organization are running large volumes of data reports that have little practical business value, then you are literally throwing money out the door. Without visibility into how data is being used, there’s a good chance you won’t even notice the hemorrhaging until it’s already cost you far too much.

Looking ahead, the need for better cost controls is growing – fast. As workers become increasingly data literate, demand for more and better data and analytical insights will rise exponentially. If you’ve ever spent time with data, you understand why perfectly well: data is highly addictive. Once you start, it’s hard to stop. And that’s great – so long as you have proper control in place to ensure they aren’t wasting resources by generating reports that have little material impact on the bottom line. 

I see this go wrong all the time. You wouldn’t believe how many executives I’ve met who tell me about how their company is data driven and has already generated 20,000+ reports. The trouble is, they have no idea which ones are valuable. Only later, after going through the motions of answering the above questions, do they realize only a small fraction (usually a few hundred) are actually creating value for the business. The other 17,000 reports they’ve been running? That’s money down the drain. 

Finally, the most important reason to take stock of current data usage across your organization is that it sets the stage for creating a better analytics system – one that is not only more efficient in terms of resources but also more effective at driving better business outcomes through behavior change. 

Take a minute to learn more—schedule a time to speak with us or read the new eBook, “Three Reasons to Think Beyond the Dashboard: Why Visualization Does Not Equal Analytics; Transaction-Level Intelligence is the Way to Full Data Trust.”