Big data, like every new technology, needs to be sold to the management in order to be implemented and, like every other technology, needs to show what the return of the investment will be. Many organisations believe that a Big Data strategy requires a big investment with unknown results. Although McKinsey stated that companies using Big Data can increase their operating margin by 60% and can reduce expenditure by 8%, a lot of CFOs are reluctant to go ahead with Big Data because of the uncertainties.
Big data is like any other strategy; it will affect the course of the organisation one way or another and it will cost money to implement, while the returns are yet uncertain. How can an organisation know what the return is and therefore how much budget can be allocated to Big Data? If we look at the marketing budgets, we see that a staggering 57% base their marketing budgets on historical budgets and not on the ROI of the marketing efforts. Results from the past are no guarantee for the future, so why would you use historical budgets to determine the investment for next year? Besides, historical budgets do not exist for Big Data.
The question is therefore, how do you determine the ROI of a Big Data strategy? Big Data may look expensive, if you believe that you need supercomputers to do the Big Data computations. Truth is however, more and more open source tools are available that are free to use and that work on commodity hardware, thereby saving a lot of money.
Storing data is also not the problem, as it is nothing new; companies have been doing this for a long time already. Storage is even becoming cheaper every day and more often innovative cloud data warehouse solutions offer a good value for money nowadays. Cloud storage can save you a lot of money, as no new data centre has to be built. New technologies, such as Hadoop, allow parallel processing of large data sets across low-cost commodity hardware that can scale easily. This dramatically reduces the cost of storing petabytes of data. Traditional database approaches dont scale or write data fast enough to keep up with the speed of creation and therefore require high costs. Companies who want to use Big Data will therefore have to look at the new technologies to make Big Data worth doing.
The main cost involved in Big Data is the operation and overall management or integration of Big Data within the organisation. A good Big Data scientist is expensive, as they are rare, and managing 1000s of nodes within a data grid requires good management. Luckily there are Big Data startups that have developed efficient algorithms that can help and combined with using the right open source tools it can give an insight in the costs involved.
However, determining a Return on Investment will remain difficult. Especially because there are no IT ROI models that can be used. Traditional IT Return on Investment models are based on elements such as speed per transaction, gaining energy saving from data centres or shrinking data centre equipment. Big Data does not work on a speed-per-transaction basis and does not run on virtualized machines, making traditional models useless.
In order to develop a Big Data Return on Investment, companies will have to start with the following steps:
- Understand why you want to use Big Data and for what. Set the Big Data objective you want to achieve. For example, a better understanding of your customer will allow you to give your customer a better experience and 86% of the people are willing to pay more for a great customer experience with a brand. Selecting the right objective can therefore help determining the ROI.
- Select the tools to meet your objectives. Different Big Data startups offer different solutions for different prices. Open source tools are free to use, but most of the time offer a commercial support plan to help you implement the tool. Depending on the tool selected, commodity hardware or a cloud date storage solution needs to be bought.
- Start small, with a pilot project that will achieve your objectives at a smaller scale. The investments needed for a pilot project are quite often less problematic for CFOs to make available in the budget. The costs and returns involved can give you a far more valuable insight into the return on investment than benchmarks or historical figures. With the pilot project, the team that proposes the Big Data technology can show how it will bring value and how long it will take before a return is made.
- Extrapolate the results of the pilot project to the entire organisation and get the entire organisation involved.
It is for sure that Big Data will bring value to the organisation. Value can be in the form of faster time to market because of knowing exactly what your customers want and what their customer buying patterns are (perhaps before they even know themselves). Big data can also help to get to know what your competition is doing or give a better understanding of where the market is heading. Big Data can also offer efficient resource utilization, for example due to parallel processing. Actually, there are many use cases around Big Data. The fact is, if implemented well, Big Data will have a lot of advantages.
How much the ROI will be, depends on the objectives set, the size of the organisation, the (open source) tools selected and the hardware chosen. There are a lot of variables that will affect Big Data ROI. If chosen wisely, Big Data will for sure give a positive ROI and setting up a pilot project can give you the valuable insights needed. With the results from a pilot project, a CFO will be much more willing to go ahead with implementing a Big Data strategy company wide. So, why wait? Start today with your Big Data pilot project and learn what your Return on Investment will be.
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