5 components of ROI for Data Analytics
Analytics implementation projects have been in fashion for a bit now, job openings are coming up, everyone is learning it, and every IT services provider is offering it. Most companies claim, for the Fear of Missing Out, to be using it too. Many have never seen it, but that doesn’t stop them from providing an expert opinion either.
The truly enlightened organizations are truly transforming their businesses on the basis of Analytics, or are making (or wanting to) their way for a strategic implementation. These companies achieve double digit ROI from their analytics investments. Many organizations are considering how they should determine ROI on an Analytics implementation or when the investment will actually break even. This not-very-visible ROI, prevents leadership approval and progress.
Keeping cognitive computing, or true AI aside for a minute, here are the five major components of ROI for an organization. All of these, you should easily be able to quantify before and after, and the gains in the after situation.
1. Financial Analysis
The ROI calculation, is simple though the components there in might vary for your organisation.
ROI = (Gain from Investment – Cost of Investment) / Cost of Investment
This is first on the list because without the numbers looking right, the project won’t fly. You can choose to create an analytics model, and if you consider it an asset (on your books), you could choose to determine the Capitalization of the revenue that this asset generates or influences.
The following four components roll up to the regular ROI computation.
2. Business Process Proficiency
Foundation level data essentially represents business processes, and then gets aggregated sideways and upwards. It is crucial to fix known business process problems before measuring. Doesn’t help to admire a problem. If the problem is perceived, but root cause isn’t known and there is no measurement happening; that is a different matter.
3. Labour Reduction
Analytics should help through three streams. One is the cyclical efforts around generic MIS, and reporting which should go away. The second is ad-hoc data (or report) requests and gaining insights which should be self-serviced. Both these should cause a drop in people required across departments.
4. Cost Reduction and Revenue Improvement
The third, continuing from above, is business improvements and problem solving, which needs short analytics projects to be initiated, and should lead to direct cost reduction (or avoidance) or revenue uptick. This is the clincher point.
5. Customer Delight
Finally, one can improve cycle times, bring in efficiency in favour of the customer with lesser number of errors, the resulting improvement in customer satisfaction should enable gaining ground in terms of the customer’s share of wallet.
Tracking KPIs is required, but that is just the start. Using KPIs as the indicators, and then Analytics to locate problems or make radical changes is what will bring ROI multi-fold.
ROI essentially puts a number on a value, and evaluating it after implementation helps guide business decisions and optimal use of Analytics.
How far have you reached in your Data Analytics journey? How easy or difficult was it for you to calculate the ROI and keep to it? We would love to hear from you. Or if you are not way down in the journey, we could still help you achieve the required ROI. Visit Us.
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