Big Data ROI in Year 1? Fuggedaboutit

Depending on the size and scope of your forthcoming big data project, in most instances it’s unfair to expect positive cash flows in year one. That’s because in addition to a potentially steep implementation and learning curve, it takes time to master and adopt open source big data technologies. Thus, for year one, it’s important to adjust expectations and look at other forms of “return” while you wait for substantial financial results.

A seed planted in good soil and supplied with water and sunshine eventually blooms. In the same way, it is likely to take more time than anticipated for a big data investment—especially in open source—to pay financial dividends. Why?

First, big data skills aren’t plentiful. This article suggests the big data skills gap is only widening, even as universities churn out newly minted analytics graduates. With the explosion of big data technologies and heavy demand from companies to implement and use them, any slack in the skills market is quickly absorbed. So, even if you get the green light to move ahead with your big data project, finding the right personnel to implement and run it—at least right away—isn’t guaranteed.

Second, it takes experience to implement big data open source technologies with a level of quality and consistency. It may seem obvious but many of the sixty big data open source projects don’t come with the same level of documentation and established best practices as traditional off-the-shelf software. This means there’s a lot of tribal knowledge for implementing big data technologies that’s locked away in various consulting vaults, or in the minds of big data experts. Too much trial and error with big data technologies could potentially put you behind the 8-ball in terms of gaining financial results quickly.

Here are additional implications to consider for your financial business case:

Modeling Cash Flows Might Be Tricky

Despite the financial rigor associated with modeling cash flows, it’s really all about assumptions. As in, you’ll need to ask yourself: “What incremental net cash will come into the business as a result of my big data project?” If you assume that skills and experience for various open source big data projects are scarce, you’ll have to come to grips with the possibility of negative net cash flows for the program in year one. Negative net cash flows won’t affect the ability to perform Net Present Value (NPV) calculations, but they certainly make IRR calculations challenging.

Whether or not you have negative cash flows for year one will depend on whether your project scope is ambitious, the availability of skills and experience, employee adoption (got change management?) and more.

Assess Project Risk

The open source community doesn’t stand still. There is always something new coming out and occasionally there’s back and forth bickering among competing projects. For open source initiatives, additional project risk should be accounted for in business value calculations.

For example, if your corporate cost of capital is 8%, it might be prudent to adjust it to 10% or even 12% for high risk projects. This adjustment will invariably affect your NPV calculations and provide feedback as to whether to do the project—or not. And if you have limited capital, NPV analysis has the added benefit of helping rank competing projects based on value contribution.

Understand Your Timeline for Business Value

For some companies, net negative cash flows for year one might be unacceptable. However, there are scenarios in which you could see value in a shorter timeline for open source big data projects. For example, if a pilot project looks promising, it might be possible to start seeing positive net cash flows at the end of year one if you go-ahead with a larger project. Can’t wait that long for project profitability to accrue? This video (around the 30 minute mark) describes KPIs to look at instead of ROI for the first year of your big data project.

Whatever open source big data project you choose, it should bring in net new (incremental) cash at some point. The key factors you’ll need to decide are whether it’s acceptable to have negative net cash flows year one (or not), and your risk tolerance for projects using open source. Negative big data project cash flows might be acceptable for a while, but unless your initiative is a strategic one, you won’t be increasing corporate wealth without earning a good return for stakeholders.

Originally published on Smartdatacollective.com, June 2, 2016

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