What’s the Return on Investment for Analytics? Ah, this is a classic question that a lot of companies ask, especially when investigating analytics for the first time.
It’s also a little bit of a trap.
That’s because the return on Analytics is never as clear as, say, a new bottling machine. That’s not to say there isn’t one. There definitely is and it can be measured. But the factors that make up that ROI depends on your stage of growth.
This article talks about the chasms you must leap as you grow, but in terms of expected ROI on your data. We won’t give you any hard numbers here because it really depends on the state of your business. So have a read and if you’d like to work through an exercise to determine what that ROI percentage is for your business, then give us a call.
We really like to use Dell’s Data Maturity Model because it so cleanly articulates each stage of a company’s data maturity. Here’s that diagram with the chasms highlighted again:
We’ll go into far more detail on jumping these chasms in our upcoming e-Book. For now, let’s focus on what ROI you should expect from each leaping each chasm.
You’re moving from highly manual reporting in spreadsheets to something far more automated. To use the Donald Rumsfeld model of knowns vs. unknowns, automated reporting is used to measure your “known knowns.”
Use cases. Develop and track industry-specific KPIs. Create scorecards/dashboards. Build parameterized, refreshable reports.
Why make the leap? Joining, Cleaning, Reporting & presenting are all done in Excel. It’s a very onerous and manual process. Basic KPI reporting is a nightmare. And while the business needs data on a daily basis, the best you can provide is weekly.
How to measure ROI. The ROI here is highly predictable and fairly easy to measure. You’re essentially looking at headcount hours saved through automation.
Continuing on with the Rumsfeld model of knowns vs. unknowns, when making this leap you’re trying to get a better handle on your “known unknowns.”
Use cases. Measure a new LTO (limited time offer). Evaluate the effectiveness of different advertising channels (eg., online, billboards, radio, TV, etc.). Cross-functional analysis like Ad Spend by Channels vs Store Sales by Region.
Why make this leap? Governance, consensus and reliability are your biggest sticking points just prior to making this leap. Where before you didn’t have enough access to information, here there’s too much. Too many dashboards. Too many black box analyses. And, ironically, too many people have their hands on the data. You’ll keep asking the same two questions, “Why don’t the numbers match?” and “Whose numbers are right?”
How to measure ROI. The ROI becomes more difficult to calculate because it now depends on:
The ROI of making this leap is more “indirect” since it depends on leadership taking action on the information presented to them. Companies almost always find efficiencies and opportunities to increase revenue or decrease cost at this stage, but the big issue just prior to this leap is the amount of conflicting information that leadership receives, cluttering the decision and making it hard to know what to do next. The big contributor to ROI of this leap is seeing a drop in “black box” analyses and boardroom debates on “Whose numbers are right.” The fewer conflicts, the faster the pivots, and hopefully the higher return.
This final leap is about elucidating “Unknown unknowns.” There are two parts to this – using AI to identify patterns, and creating a tight feedback loop between reporting and planning.
Use cases. AI and Machine Learning are useful as a starting point in your forecasting and planning. It can identify patterns in your data that the human eye cannot identify. AI can also help develop a forecast model that your business tweaks to budget and plan for upcoming seasons. A major challenge is creating a tight-feedback loop between the forecasting/planning piece and the business intelligence reporting for the rest of the business.
Why make this leap? In many ways this leap is exactly the same as the first one. A lot of your work is manual, done in Excel, and requires stitching together a lot of people’s work. But where the first leap is all about reporting, this one is all about planning.
How to measure ROI. The return on AI and Machine Learning can be tricky – it depends on the tools you’ve purchased, the people you’ve hired, and the decisions you make from the results. Rather than focus on squeezing every percentage point out of AI and ML, instead focus on measuring how closely your Actuals are tracking against Plan. The tighter that gap, the less waste and fewer missed opportunities you’ll experience.
While you may have been nodding along more with one chasm than another, the tricky thing about the Dell Data Maturity Model is that it makes it look like you’re jumping each chasm in sequence. There’s an intuitive dependency on time. But as we’ve discussed in The Surprising Link between Analytics and Growth, you’ll start to face these hurdles as a function of growth, not simply the passage of time. And although we love the DDMM, this is the one place where it breaks down.
It’s entirely possible you could be facing all three of these hurdles simultaneously. One massive chasm. This can happen if you’ve had unfettered growth over the past several years and have let each of these problems pile up. It’s a lot like lower back pain. It’s chronic. You can live with it. You can ignore it. But eventually you’ll throw your back out, and you won’t be able to think of anything else. It’s best not to let it come to that point, but regardless if you’re leaping three chasms or one massive one, TypeSift can help you bridge the gap.
Are you contemplating a leap in your analytics strategy, but want some help determining the expected ROI? ThenWe’ll take you through our standard ROI calculator and break down the options available to you. We can probably find some opportunities to save money and time in your implementation.
TypeSift is a Data Engineering & Design Minimalism Firm. Our expertise is decluttering information and solving problems in your data that are holding back your growth. We build software that corrals data and invokes ingenuity with the fewest moving parts.