What Makes Insights-Driven Organizations Different?

If you can’t stand the heat

Summer is finally upon us. After an eternal winter and classic spring indecisiveness, temperatures have reached what some would consider “warm” here in the midwest. And that means it’s time to crank up that A/C.

One of the first things you learn as a kid is that leaving the door wide open when it’s hot outside is a big no-no. Letting all of the cold air out is a good way to lose that day’s popsicle privileges.

Even today, with smart thermostats and energy efficient units, kids still hear their middle names when they can’t make up their minds between the backyard and the basement. You’d think that between all of this innovation and parental wrath that we would have solved this age-old dilemma of trapping the cold air in even when doors and windows offer an escape path. But even the most state-of-the-art air conditioner can’t cool a house effectively and efficiently without one crucial ingredient: a closed loop.

Calm cool collection

More data has been produced in the last 2 years alone than in all of human history combined.¹ There is no way to overstate the way in which digital is fundamentally changing our lives all around us. Businesses have begun to collect this data in massive quantities, and this often times has required architecting new data stores and overhauling systems that have been in place for years.

And this is where we have seen a divergence in organizations. Some organizations take a step back, look at their newly minted data warehouse/data lake/cloud migration, and feel like they’ve done it – they’re set up for the future and can now ward off digital disruption. And then there are organizations that understand that this is just where the transformation begins.

Truly insights-driven businesses understand that data is the means to an end, but it’s definitely not the end. It’s like installing an air conditioner but then never turning it on.

Cultural climate

Analytics is all about turning data into insights. Those new infrastructures can set organizations up for success when they also adjust to establish a culture of innovation by leveraging a cross-departmental combination of individuals with a unique combination of skills.

Executive leadership brings the direction of what should be accomplished through proper utilization of the data. Business users bring complex understanding of business processes, problems, and insider intuition of what data could lead to helpful insights to accomplish the goal. Developers and engineers understand the systems in which the data lives and how to securely and effectively grant access to the users who need it. And analysts and data scientists know how to apply statistics and complex techniques to model the data in accordance to what they learn from the business users and the goal they’re driving towards.

Working collaboratively, with a clear goal in mind, and with the right technology supporting you, will lead to insights. You’ll be amazed at how data science projects will reveal opportunities for improvement, surprising correlations, and unsuspected bright spots.

Yet here is where we see another divergence in organizations. At this stage, some organizations yet again take a step back and say, “I’ve done it. I’ve uncovered unique and differentiating insights about my organization,” and think they are now an insights-driven business. Truly insights-driven organizations understand that these insights are only as impactful as their ability to deliver an action at a crucial point in a journey. Uncovering impactful insights and letting them sit in a report or in the heads of your analysts is like leaving every window and door open as your A/C pumps cool and comfortable air into your house – you might as well turn it off and save your money.

Shut the front door!

There are two important aspects to closing the loop on analytics initiatives: operationalizing insights, and measuring their impact.

The insights you uncover through your analytics exploration normally started with a specific business question or use case. Operationalizing those insights means you are now embedding that insight directly into a decision point within one of your operational systems or processes. This might include incorporating a machine learning model on incoming claims data to determine the priority for review. Or maybe it’s automatically routing negative reviews via surveys or social media to designated service representatives for prompt and personal responses. In both of these situations, you can see the timeliness of your action is critical – which reiterates the need for the insights to be applied at the point of that decision (and the need for that multi-skilled group of individuals discussed earlier). It’s like making sure you open the vents in the specific rooms you want to cool.

Just as with any good business process model, periodic review of the process effectiveness is one of the most important steps. Digital business has required organizations to become more nimble and agile – successful organizations are able to adapt when things aren’t working and double down on what is. Analytic insights are no different. As operationalized actions are taken, the outcomes of those actions must be collected as data to fully close the loop. If you’re still not meeting service requirements on high priority claims, how can you adjust your classification methods to no longer include claims that, after review, were deemed less critical? If it’s realized that many negative reviews contain a similar complaint, how do you adjust your process to incorporate the operations staff that could fix the root cause of the complaint? If it’s too hot in your house, do you just deal with it? No, you sneakily adjust the thermostat when your spouse isn’t looking.

Truly insights-driven organizations understand that their data has to produce insights, those insights have to drive actions, they have to measure the outcomes of those actions, and they have to be ready and willing to adjust so future actions continue to become more impactful and profitable. Forrester predicts that by 2021, insights-driven businesses will earn $1.8 trillion and grow 30% annually. Most of that growth will be diverted from their lagging competitors.² As the competitive landscape continues to heat up, they’ll be sitting at a cool 68 degrees.

We’re Zirous

We help organizations choose the insights-driven path when they get to those crucial points of divergence, and navigate the roadblocks they may encounter along the way. Our strategy and execution services begin with your goals as the foundation. With that understanding of where you need to go, we’ll lay out and execute a plan to incorporate the people, processes, and technology to get you there.

How do we start?

1 SINTEF. (2013, May 22). Big Data, for better or worse: 90% of world’s data generated over last two years. ScienceDaily. Retrieved May 21, 2019 from www.sciencedaily.com/releases/2013/05/130522085217.htm

2 Forrester Research, Inc. Insights-Driven Businesses Set The Pace For Global Growth. October 2018. https://www.forrester.com/report/InsightsDriven+Businesses+Set+The+Pace+For+Global+Growth/-/E-RES130848#