It’s been predicted that every human being on the planet will be generating 1.7 megabytes of data every second of the day by the year 2020. Yes, you read that correctly! That’s humongous, to say the least.
So you can expect our digital universe to grow from 4.4 zettabytes to 44 zettabytes (or 44 trillion gigabytes). These numbers might be hard to get your head around, but it definitely shows the importance of Big Data.
With these kinds of figures, you would think that all the leading organizations were already taking advantage of data analytics to drive their business forward. But unfortunately, this isn’t the case at all. The sad reality is that less than 0.5% of global data is being used to influence business decisions.
The only thing that will probably change this reality is to look at the return on investment (ROI). At the end of the day, management teams love ROI metrics, so considering the ROI of data analytics will enhance your chances of getting your investment approved.
The advantage of ROI is that it helps you measure the expected rates of return on the capital invested in a specific solution. Further, it can be compared to other investments and compared to the business’ required rates of return.
Earlier this year, I wrote about how you should go about proving the value of big data investments and ROI. Let’s expand on that and look at the different variables that you have to consider when calculating the ROI from your data analytics project.
How are you going to calculate ROI?
Before you embark on any big data project, you have to first consider how you’re going to calculate the ROI for the investment.
The ROI from data analytics can be calculated based on a couple gain/cost parameters following the formula below:
ROI = Gain from Investment – Cost of Investment
The first gain from investment parameter will consider the following variables:
1. Revenue Optimization
If you spend a certain amount of money on big data analytics, how much revenue can you generate from this solution?
This can be calculated as follows:
Existing Revenue Stream: If you can generate innovative price plans or bundles to enhance your existing revenue streams, then that percentage can be applied to your annual revenue as an additional gain from investment.
New Revenue Stream: If the data analytics solution can be sold as a new service for clients, the sales budget can provide the necessary revenue information as gain from investment.
2. Cost Optimization
Human Capital Gain: For most enterprises, employees make up the most important resource within an organization. As a result, they will form an essential part of the gain value that’s related to the investment in analytics.
If your staff can save time by taking advantage of big data analytics, it will significantly increase productivity. To understand the impact on staff productivity, you will have to figure out the average cost per employee per hour and then calculate the hours of increased productivity.
To measure this, you can assign a certain number of hours per activity. Then you multiply the average cost per hour.
Human Capital Gain = Employee Average Cost per Hour * Total Number of Increased Productivity hours
3. Existing Solution Gain
If you’re thinking of phasing out a current big data analytics solution, you have to first estimate the cost involved in maintaining it at the current value. This will enable you to measure the cost saving when you replace it with a new big data analytics solution.
Existing Solution Gain = Running Costs + Value of Minimized Revenue Risk
The second gain from investment parameter will consider the following variables:
Cost of Investment
The costs that are related to investing in big data analytics can be split into two main categories:
- CAPEX: Non-recurring cost
- OPEX: Ongoing cost
- CAPEX: Non-Recurring Cost
Finding the right big data solution that perfectly matches your business needs will take time and it will also require a long roll-out phase before it’s up and running.
Even though this cost will only apply once, it can constitute a major part of the overall investment. As a result, you should include it when you measure the ROI.
- OPEX: Ongoing Cost
Maintaining and running a big data analytics project will have a lot of ongoing costs associated with it. These expenses can be related to the following:
- Client support
- Network and security
- Software upgrades
- Data storage
So based on the information above, we can calculate the ROI with the following formula:
ROI = (Revenue Optimization = Cost Optimization) – (CAPEX + OPEX)
The whole point of conducting an ROI analysis is to help understand the costs and gains associated with making that leap into big data analytics. By focusing on ROI, you can make decisions based on an informed approach instead of assumptions.
And have you ever calculated ROI from your Big Data development project? Please share your experience in Comments below!
This post is based on "Return of Investment (ROI) Guide to Big Data Analytics Solutions" by Casper Tribler.