When we think about big data, we think about breaking old processing models in IT, but there are traditional practices that are pretty useful. One of these practices is asset management, and the reasoning that gets applied when it's time to factor variables into the decision making that precedes budget proposals.
Unlike transactional servers in the data center, big data servers are not good virtualization candidates because they must parallel-process data. Big data servers are always at work, and it is much more difficult to resource share in this environment. If companies also want access to near-real-time analytics from their big data, it isn't very feasible to go to a big data cloud analytics provider either, because of the latency and security issues that potentially come into play.
All of this points to a need to maintain "physical" server resources for big data in the data center. In turn, the acquisition of physical servers invokes traditional budgetary approaches to hardware like asset life cycles and amortization pitted against return on investment (ROI) that the CFO is likely to expect.
What's different about big data in this very familiar process is that you can't expect to develop a ROI in the same way that you have in the past for transaction servers. ROI for transaction servers was often predicated on the speed per transaction, which was then extrapolated into how many more transactions (and resulting revenues) the business was projected to capture. Investment returns on big data don't work this way.
You are better served to estimate the number of actionable analytics outcomes that your processing is likely to deliver, agree on the business cases these results will be applied to, and then measure whether you are enhancing revenue opportunities, product time to market, or other goals you might set. From the standpoint of a big data server, results are likely to be measured in lapse time per job, how close to real time these analytics outputs are, and how many concurrent jobs you can service on the server in a specific period of time.
The other element of big data asset management is making a smart decision when choosing a platform. There are advantages in big data processing speed in a reduced instruction set computing (RISC) versus x86 server, because RISC can process twice as many concurrent threads of data, but now solid state drives are making a real performance difference in x86 class servers.
The question for the IT decision maker becomes: What platform do I invest in for a long-term big data strategy that will protect my investment? To answer the question, the IT leader must meet with vendors to see where the vendor is headed long-term. He or she should also make it a point to have very direct discussions with vendors to see where the vendors are planning to invest and how willing they are to offer investment protection alternatives if they change direction.
It might also be time to consider cloud-sourcing as a big data strategy. Cloud-based big data offerings are maturing, and if your need is not for immediate real-time analytics, you could be well served by a cloud-based big data provider that takes on all of the asset management. By going to the cloud, you also save on energy costs and infrastructure investment in your data center, and you lower your long-term risk from facility and asset standpoints because you are with the cloud provider purely on a subscription basis, with no hardware to buy. At the end of this process, the IT decision maker (and the CFO heading the budget process) is looking at several asset scenarios for big data.
- Do you invest in the data center, or do you go to the cloud?
- If you go to the data center, which computing platform do you run your big data on?
- What are the financial, security, operational, and strategic risks?
For questions in asset management and acquisition, when to outsource and when to insource, and even how to develop a workable ROI, the traditional playbook on IT asset management continues to deliver value for a new technology.
Mary E. Shacklett is president of Transworld Data, a technology research and market development firm. Prior to founding the company, Mary was Senior Vice President of Marketing and Technology at TCCU, Inc., a financial services firm; Vice President of Product Research and Software Development for Summit Information Systems, a computer software company; and Vice President of Strategic Planning and Technology at FSI International, a multinational manufacturing company in the semiconductor industry. Mary is a keynote speaker and has more than 1,000 articles, research studies, and technology publications in print.