The tech community is faced with the truth that Big Data is now shaping IT economics. The recent Wikibon Peer Incite Research Meeting on Big Data came to the conclusion that data warehousing isn’t a thing in the past and may actually coexist with Big Data. However, enterprises will often look at a long-term effect. And, this is where Big Data solutions maintain an edge.

Dave Vellante hoisted vital challenges directed to the CIOs. He suggested  that “while many unknowns exist around so-called big data, CIOs need to position their organizations to take advantage of the new data reality by developing a strategy around big data. CIOs in data-rich industries should organize an elite team consisting of data scientists, programmers, and business professionals that can monetize data. This team should be tasked with developing a comprehensive data strategy and identifying an industry-specific ecosystem that can evolve with both internal and external partners.”

He also brought up some important considerations for managers regarding what they can do to extract the potential value of Big Data to their respective organizations, and what other possible opportunities there are to monetize their new data models outside of the usual process of data warehousing. The need to identify specific skills set to get things done accordingly and what partnerships to explore also are very interesting starting lines for the purists.

Vellante’s thoughts were further supported by David Floyer’s analysis of the financial entailment of Big Data versus data warehouse solutions. He went on to conclude, “The bottom line is that for big data projects, the traditional data warehouse approach is more expensive in IT resources, takes much longer to do, and provides a less attractive return-on-investment. However, big data projects are using new and less mature technologies, and are less likely to succeed. Big data technologies are unlikely to be suitable for traditional data projects and vice versa – as is so often the case, it is a question of horses for courses.”

The study that Wikibon Senior Analyst David Floyer conducted has produced some practical thoughts on two approaches. He made a comparison on the cumulative cash flows for a project for evaluating customer experience. The outcome leans in favor of Big Data approaches. Why? The enormous volume of data that needs to be extracted will not allow pre-analysis centralization and the quality and availability are both unknown and has to undergo iterations before selection of the correct data commences. The figures revealed more startling conclusions: cumulative cash flow in 3-year period for Big Data reached $152 million, while traditional data warehouse posted a little over 1/3 of the competition, or $53 million.

Another relevant point, this time involving evaluation of 3-year IT costs for customer experience project through MPP (a Big Data solution), Appliance RYO (conventional data warehouse solutions) and Single SKU Appliance solution. The results again heavily favor Big Data solutions, with the least cost and just about 40% of the next best single SKU appliance solution.

Analytics is the bread and butter of all existing enterprises in the world. And data is the microfiber that constitutes analytics. This only tells us that Big Data solution is and will be a flourishing business. IBM’s City Forward for example, handed Big Data analysis out to urban spaces to improve quality of lifestyle.