What’s the Return on Investment (ROI) 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 bit of a trap.
That’s because the ROI 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 compromise that ROI depend 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 for a free 20-minute discovery call.. We can help you measure your ROI using data analytics.
We find the Dell Data Maturity Model very effective, as it cleanly articulates each stage of a company’s data maturity, which greatly helps with effective data driven decision-making. Here’s the model, with the chasms highlighted:
We have a downloadable e-book in the works that goes into far greater detail about chasm-jumping. Sign up for our mailing list to be the first to know about its release. But for now, let’s focus on what ROI you should expect from leaping each chasm.
In this stage, you’re moving from highly manual reporting in spreadsheets to a far more automated system. To use the Donald Rumsfeld model of knowns vs. unknowns, automated reporting is used when making this data chasm leap to measure your “known knowns”.
Use cases. Develop and track industry-specific key performance indicators (KPI). Create scorecards/dashboards. Build parameterized, refreshable reports.
Why make this leap? In this leap, joining, cleaning, reporting, and presenting are all done in Excel. It’s a very onerous and manual process, and basic KPI reporting is a nightmare. 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 limited time offer (LTO). Evaluate the effectiveness of various advertising channels (e.g., online, billboards, radio, TV, etc.). Conduct 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. Whereas before you didn’t have access to enough information, here there’s information overload: too many dashboards, too many black box analyses, and, ironically, too many people with their hands on the data. You’ll know you’re ready to make this leap when you find yourself repeatedly 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 data 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. Conflicting information causes inefficiencies by cluttering the decision-making process and clouding your next steps. This leap’s biggest contributor to ROI is seeing a drop in black box analyses and boardroom debates over “Whose numbers are right?” The fewer the conflicts, the faster the pivots, and ideally 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 (ML) are useful as starting points in your forecasting and planning, as they can identify patterns in your data that the human eye cannot identify. AI can also help develop a forecast model that your business then tweaks to budget and plans for upcoming seasons. A major challenge is creating a tight feedback loop between forecasting/planning 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 a lot of stitching together of many 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’ve made based on the results. Rather than focus on squeezing every percentage point out of AI and ML, focus on measuring how closely your actuals are tracking against your plan. The tighter that gap, the less waste and fewer missed opportunities you’ll experience.
While you may have been identifying with one chasm more than another, the tricky thing about the Dell Data Maturity Model is that it makes jumping data chasms appear sequential. There’s an intuitive dependency on time. But as we’ve discussed in The Surprising Link between Analytics and Growth, you’ll start to consider these hurdles as a function of growth, not simply the passage of time. And although we love the DDMM, this is one of it’s few limitations..
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, with that being said, no matter if you’re leaping three chasms or one massive one, TypeSift can help you bridge the gap.
Are you contemplating a leap in your data analytics strategy, but want some help determining the expected ROI? Then talk to us. We’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 as well.
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.
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Chasms you must leap as you grow, but in terms of expected ROI on your data.