IT investment decisions are easy. Right? If you’re projections show that you’ll get back more than you spend, in either cost savings or increased revenue, you do it. Sounds easy. It usually is not. But, it needs to be.

Let’s say you’re exploring a big data or a master data project. You know you should do it. You know that it makes business sense. But, the finance guys want hard numbers, and that’s often not easy to get.

CFOs often demand to see some pretty complicated numbers such as ROI. NPV. IRR. Payback period. So, business folks go to great lengths to come up with those calculations.

But, I’ve found, in the end, to get the sale, you’re analysis and presentation need to be brain-dead simple and a no-brainer. If you aren’t comfortable with the numbers, if you don’t fully understand them and can explain them really easily and convincingly, don’t even bother to go to the top to ask for the budget approval. The answer should slap you in face. “Yes, of course, we have to do it,” must be the obvious conclusion. And remember, it all has to be measurable.

The most important thing I learned in business school was how to do analysis on the back of a napkin. Literally, you should be able to outline the ROI for a business project on a napkin. I’ve done it before. I once helped convince the management team of a startup to sell the company and lock in a good return, rather than continue to invest for another three years in the hopes of a higher return, by scribbling a few numbers on my coffee stained napkin (I drink a lot of coffee) in a staff meeting.

A. Bird-in-hand return now: $10/share offered by a potential acquirer.


B. Potential return in three years. = ($15/share)

Revenue would be 70% higher (20% per year increase target)
Stock price of 4X revenue. (Typical for a company growing 20%)
Stock dilution of 25% because we’d need to raise $10 million

=$10*(1+(0.7*.75))=$15.00 per share (potentially)

The simple result was a modest potential upside. I did not bother to risk adjust anything or do NPV with a fancy calculation. My colleagues knew the incredibly high market risks in their minds. We were in a very competitive market and needed to make significant product enhancements to remain competitive.

The decision was a no-brainer. We took the deal.

Yes, we put the whole thing in a fancy spreadsheet later, but that was really all a formality. The real decision had been made in that conference room on that napkin.

You should apply a similar approach when you’re trying to get buy-in for a Big Data Analytics or Master Data Management or other strategic data project.

Let’s look at two manufacturing companies. Both make, or have made, acquisitions fairly regularly. Both IT departments knew they needed to handle their master data better. They had all the usual problems – data silos, incomplete data, quality problems, imperfect customer service, etc. Both had lots of inefficiencies because various groups didn’t know what other groups were doing. Both companies had the idea to integrate big data across their various divisions so they cold run more analytics to optimize their businesses.

While their challenges were similar, each company took a different approach to justifying the project. One tried to justify the project via increased sales. The other through reduced costs.

1. Industrial materials manufacturer – ROI would come from increased sales – better cross-selling and thus higher productivity for the telesales staff.

2. Air conditioner manufacturer – ROI would come from the cost savings derived from reducing the cost of maintaining master data across multiple systems and divisions. E.g. Much easier to enter new customers or modify customer information enterprise-wide.

One was way easier to calculate and measure than the other. Guess which one got funded much faster.

Company 1 stated that increasing telesales productivity by 15% would way more than pay for the project. It got funded right away. They also projected a variety of cost savings. But, the obvious advantage of the increased sales was the most convincing number. The rest was gravy. The project is implemented and the results are exceeding their expectations.

Company 2 collected a lot of data and wrote a 10-page report and 15-slide presentation basing their justification on reduced data maintenance costs of IT and LOB personnel. They calculated that they spent tens of thousands of IT man-hours per year in master data related activities, and significantly more with LOB personnel in the business units. By making those processes and those employees more efficient, they estimated $5 million in annual savings, far more than the cost of the project. They calculated an NPV of the savings of $10 million and IRR of 170%. But, it took 10 pages and 30 minutes to explain.

Working with outside consultants deeply knowledgeable and experienced in master data and data quality projects, they came up with twelve ways to save money across a variety of groups and processes, totaling many hundreds of employees. For each of the twelve different processes and types of personnel, they estimated different productivity improvement coefficients, ranging from 5 to 25%. They calculated that they’d save millions from reducing both master data maintenance and data errors. They built a big spreadsheet to calculate the savings. They transposed the spreadsheet into a few PowerPoint slides, each with about 40 or 50 numbers on them.

Great analysis.

Bad presentation.

They are still working towards getting approval. They need to simplify their approach. They also need to make sure the results are clearly measurable. It’s hard to track man-year savings across many divisions and job functions. Perhaps, they should concentrate on one major group and apply the average of all the productivity coefficients and come up with a few simple measures that justify the project and can be measured. All the detail is great, but present it in a highly simplified way.

I have a background in statistics and math. I’m somewhat of a geek. I like numbers. But, first and foremost, I’m a businessman. I have a steadfast belief that when you are making business decisions, throwing more math, and especially throwing higher-level math, at decision-making can easily result in diminishing returns. If you can’t very easily and quickly explain the numbers to your bosses with full confidence, then don’t even bother. Simplify it all first.