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SmartData Collective > Data Management > Best Practices > NPV Considerations for Open Source Big Data Technologies
Best PracticesBig DataCommentaryExclusiveITOpen Source

NPV Considerations for Open Source Big Data Technologies

paulbarsch
paulbarsch
6 Min Read
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 Mention the words “open source” and all kinds ideas probably come to mind such as “free”, “agility”, and “speed”. However, with any IT project, it is important to look at business benefits vs. costs in a manner that goes beyond generalizations. One method for benefit-cost analysis for open source big data projects is Net Present Value (NPV).

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 Mention the words “open source” and all kinds ideas probably come to mind such as “free”, “agility”, and “speed”. However, with any IT project, it is important to look at business benefits vs. costs in a manner that goes beyond generalizations. One method for benefit-cost analysis for open source big data projects is Net Present Value (NPV).

It’s not unusual to find the IT community excited about the possibilities of open source. And with good reason as adoption of open source big data technologies may provide companies flexibility in charting their own path, ability to innovate faster and move at the speed of business. And yet, it is sage advice to temper some of the frenzy in adopting open source with a financial analysis.

There are plenty of methods to justify an IT project. They range from simple payback to the more complex such as calculating the present value of cash flows. Financial managers use NPV to see if a project will ultimately bring in more money than it costs. NPV is also used to rank projects in priority order when there are competing alternatives.

What exactly is NPV? Investopedia’s definition follows: “Net Present Value (NPV) is the difference between the present value of cash inflows and the present value of cash outflows.” To start an NPV analysis, you will need to determine three things:

  • Initial outlay
  • Incremental cash flows (for the useful life of the project)
  • Cost of capital

The initial outlay is probably not too difficult to find. It’s essentially money needed to get the project started. The corporate cost of capital can be ascertained by asking your corporate finance professional for his/her best estimate (I don’t recommend trying to calculate your company’s cost of capital unless you’re a glutton for punishment—that’s better left to the finance folks).

Now to the tricky part; the incremental cash flows, or “cash” that’s going to come into the business from your big data open source project.

Here’s the process: determine your best estimate for incoming revenues from the proposed project and then subtract out incremental costs including potentially higher utilities as a result of your big data project, additional training, additional overhead and/or salaries, ongoing application maintenance, enterprise support fees, and depreciation (if you’re buying an appliance or commodity hardware). Then subtract out taxes, add depreciation back in, and add in any salvage value of computer equipment if purchased specifically for your project.

Let’s talk about areas where incremental cash might show up. For example, when moving to open source, sometimes an ETL offload project into Hadoop is identified as a place for cost savings. In this instance, first determine your current spend with ETL licenses, development and ongoing support and then figure out what it will “cost” to replace that process with open source support and development. Keep in mind; depending on the maturity of the open source project, there may be hidden costs such as additional training requirements, added security, higher than expected development costs, and increased charges to upkeep a high availability environment.

The exercise of determining incremental cash flows as a result of your open source big data project is educational in its own right, as it’s entirely possible to discover the savings you thought would result from open source might not show up. However, for the sake of discussion, let’s assume there are indeed incremental net cash flows for the project at hand.

Now it’s time to calculate. Excel has a NPV function (figure 1) and a financial business calculator works just as well to plug in your numbers.

Figure 1. Image courtesy of mysmp.com
Figure 1. Source: mySMP.com

At the end of your calculations you should have a dollar amount, positive or negative which shows the NPV for your particular open source big data project. Anything positive in terms of NPV should be a project “accept” and a negative NPV means the project should be declined.

NPV might not be your sole criteria for making a business decision. And of course there are reasons other than financial to adopt open source big data technologies (we will look at these in subsequent articles). However, NPV offers a compelling look at whether the present value of cash inflows expected from your proposed open source big data project exceed what’s potentially going out in terms of cash. NPV can also help indicate which big data project should go first if you have competing projects and limited capital.

Overall, NPV is a great way to test your financial assumptions and justify—in your mind—and your CEOs, whether a particular open source project is right for your company.  

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