Any time a business unit is asking for funds to make an investment, whether it’s for production equipment or an infrastructure investment, the very first question always centers on respected ROI. That’s the question which we can understand, if for no other reason it ensures that the requesting group has spent necessary time justifying the costs associated with investment. And, that the person who requests has a firm grasp on the potential benefits.

However, as Daniel Newman points out in this Forbes post, calculating the ROI associated with big data prior to making the investment can be quite challenging. That’s true in part because a properly structured big data implementation has wide sweeping implications.  Newman suggests big data can build communities, encourage culture through customer and employee retention as well as driving revenue.

Big data is (or should be) almost every company’s focus these days. Now executives of almost all industries are looking for ways to explore the potential it promises. However, many executives struggle to justify the expense of a big data project that doesn’t offer a clear ROI.

The results of a recent global marketing survey by Teradata provide a number of insights and issues that deserve our attention when researching a potential big data implementation. Most issues are focused on how to implement big data solutions within a company, and after implementation, how to strategically employ these solutions.

The study indicates that marketers are the most advanced adopters of big data tech. In fact, 71% look forward to putting big data solutions in play within the next two years. This should come as no surprise – innovative marketers are looking to be the first to use big data effectively.

However, adopting big data tech and knowing how to use it are two different things. The survey also describes that of these marketers, 75% do not know how to calculate ROI on these investments.

According to the study, “Most marketers trying to calculate ROI report having problems because their systems do not allow them to manage and consolidate the data they need. This makes it tough to connect earnings to market activities.” This is problematic because, as with any new technology or capability, marketers required to prove to management that the investment in big data solutions will benefit the company and drive improved marketing capabilities.

ZDnet’s article on the study shows that, “The irony here is striking: marketers are having difficulty measuring and evaluating data driven marketing, the very basic of which is measurement and evaluation.”

The quote above from the Teradata study suggests that structural issues and a lack of data integration could be impeding marketers’ ability to successfully calculate the ROI of big data solutions. And if marketers cannot consolidate the huge amounts of data at their disposal, how will they get a clear picture of how their investments have paid off?

CIOs know that no matter how great the promise of any new technology, that technology has to be “sold” at the budget table to pave the way for implementation. The process of selling is highly complex. It begins with the cost of hardware and software in dollars and cents, and then adding to this the staff resources or special skills that must be brought on the board. From this point, the discussion gets into the ultimate benefits of the technology for the business-whether they are time to market, better revenue opportunities or saved costs.

This is the backdrop for IT decision-makers when they read reports from top-level research companies like McKinsey, which state that big data “will become a key basis of competition, grounding new waves of productivity growth, innovation, and consumer excess.” MacKinsey notes that the retailers who are using Big Data could increase its operating margin by more than 60 percent; that US healthcare can drive efficiencies and quality with big data analysis to the tune of $300 billion in value every year, cutting expenditure by about eight percent; and that government administrators in developed European economies could save more than $149 billion in operational efficiencies by using Big Data.

The question is, will this be enough for CIOs and business leaders to bring big data technology through the door?

The CFO will likely be the first person to ask for a projected return on investment. The real problem the CIO faces is that he may not have any existing ROI models in IT that he can draw from! This is because traditional IT ROI models are based on elements like speed per transaction (Big Data doesn’t work on a speed per transaction basis), reducing data center equipment footprints and increase in energy savings (Big Data does not run on virtualized machines, which are pivotal to reducing data center footprints and increasing energy savings). To mix up the situation, Big Data for applications like automobile performance simulations or modeling new drug formulations can sometimes take hours to run. These applications in reality can’t usually qualify for the more compact and inexpensive processing options of simple business analytics computing.

This begins to materialize multi-million-dollar images of supercomputers, certain to make every CFO’s and CIO’s hair turn gray.

Fortunately, there are a growing number of scalable, clustered with very high performance computing solutions out there that make Big Data analysis a reasonable option for the enterprises. These solutions provide capability to start with small and then be expanded as enterprise big data analysis requirements grow. This can also be scaled to the level of supercomputers if they ever need to be. This will be enough to pass the CFO’s litmus test on cost of acquisition.

Of course, there is still more to do. The team offering big data technology must show how the technology is going to bring value to the enterprise as well and how long it will take the company to recoup its technology investment.

Value, as McKinley states, will come in the form of faster times to market from Big Data analysis that give the enterprise a competitive edge, or better ways to evaluate and answer to consumer buying patterns that enable the company to capture more revenue. In some cases, Big Data analysis (think healthcare) can provide insights that allow organizations to revamp operations for wasting less, and so reducing costs. These kinds of savings or earnings projections are usually penciled out by line of business managers at the budget table.

That generates ROI from the data center, which can be a challenging for CIOs. Always Remember: high performance computing for Big Data is not virtualized, so it’s not likely to generate any return on investment in saved data center floor space or energy savings.

Efficient resource utilization is the area of Big Data ROI that the CIO needs to bring to the discussion of budget. If this argument of resource utilization is combined with the time to market, operational efficiencies and revenue generation arguments from the end business, the CFO will feel very comfortable about the investment.

Here are some prime examples of Big Data examples in which ROI was clearly generated:

1) T-Mobile

Lately T-Mobile has been shaking up the cell phone carrier industry with its “un-carrier” strategy. That’s not all they’ve been up to, though; thanks to an implementation of Hadoop clusters, T-Mobile has realized an ROI not only from reduction of storage hardware, but also from having more accessible data. Their Hadoop solution allows them to identify and resolve network issues twice as fast as before, creating an overall better customer experience while creating savings to the tune of $2 million in hardware.

2) GE

GE is well-known as a Big Data “giant” – with on on-going $2 billion investment in their latest software an analytics division, they clearly mean business. On a smaller scale, they are using Big Data to improve efficiency on their gas turbines. By monitoring data from thousands of gas turbines across the globe, they hope to improve the capacity and efficiency of these machines, which could easily translate millions of dollars in savings annually.

3) Macy’s

This retail giant carries a whopping 73 million items, yet only recently invested in a Big Data solution. Their analytics now offer a comprehensive analysis of online transactions, store transactions, and social media activity. This data accessibility lead to a 10% increase in store sales, and allows Macy’s to provide near real-time pricing based on inventory and demand.

4) IRI

IRI is a data analytics company servicing fortune 500 consumer packaged goods and retail industries. Using the MapR platform (which Aptude is a partnered with), they reduced annual costs by $1.5 million by addressing mainframe load and support issues. The Hadoop solution they implemented was an interface on top of their existing mainframes, which decreased processing time and lessened the load on the mainframes.

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