Big data was big news in 2012 and probably in 2013 too. The Harvard Business Review talks about it as The Management Revolution. The Wall Street Journal says Meet the New Boss: Big Data, and Big Data is on the Rise, Bringing Big Questions.
Big data was big news in 2012 and probably in 2013 too. The Harvard Business Review talks about it as The Management Revolution. The Wall Street Journal says Meet the New Boss: Big Data, and Big Data is on the Rise, Bringing Big Questions. Given big data’s popularity in the press, you might think that the technology market is only about big data and how companies use the vast and growing amount of data now available to organizations. While this technology can provide a significant opportunity, the reality is that just having big data does not provide an organization with the intelligence to be more efficient or grow market share. It can provide the foundation on which organizations can assemble technologies and applications that can help realize these opportunities, but organizations need to focus on the big picture, which encompasses additional layers of technology that work together with big data. Our recent benchmark research on business technology innovation found that big data is not the top priority for business or IT; analytics, collaboration, mobile and cloud computing are all more important. Organizations do believe that big data is very important (25%), but if they were pushed to prioritize technologies, it would not top the list.
The majority of the big data hyperbole focuses on the velocity, volume and variety of big data, which are important technology attributes for IT to deal with but deliver little to help business gain any opportunity for improvement. My colleague Tony Cosentino articulated this well in his blog (see Transforming Three Vs of Big Data into Three Ws of Business Analytics), which placed the pivotal value on the So What, Now What and Then What aspects of what business expects in time-to-value (TTV) aspects of big data. These factors are what business cares about in terms of analytic and information value from big data. Business is not concerned with the criteria IT uses to evaluate or determine which big data approach it is taking. Technology evaluations have fixated around the Vs of big data with no context of the Ws and no involvement from analysts who have to apply analytics or ensure the right information is made available in their business processes. That means IT organizations may be wasting their businesses’ time and resources. Our research into business technology innovation finds that lack of resources is the largest barrier (51%), and having IT expend significant quantities of time and resources on big data without a strong business context is a recipe for failure. Thankfully for many organizations, planning approaches for technology such as specialized DBMS (45%), in-memory databases (40%), data warehouse appliances (37%) and Hadoop (36%) requires a solid business case to move to full evaluation and deployment mode. If you hear the V pitch on big data, just ask about the W’s to get the conversation back to the business value.
When it comes to getting value from analyzing big data, our research found the three primary benefits organizations want were access and retention of data for analytics (29%), reducing the time required for analyzing data (13%) and increasing revenue (12%), which are more specific than benefits such as better communication, better management and tracking of initiatives and better organizational alignment. Those latter benefits are important, but organizations could also derive them from using business analytics. Ensuring you get the analytics value from big data also ensures you can mine or analyze big data for predictive analytics, forecasting and discovery, which, along with supporting taking action based on analytics, are the most critical business analytics needs in organizations today. With only a little more than half of organizations satisfied with their analytic processes, and 44 percent of organizations indicating the most time-consuming part of the analytics process is data-related tasks, IT must guarantee that business priorities of analysts who are held accountable for the information and metrics are included from the beginning.
Organizations must make sure to get business and IT together to determine whether they are getting the most value from the existing data stored in the organization. If you scale up the amount of data, is your organization prepared to take advantage of it and deliver business value in reduced time periods? Our research finds the most important change agents for selecting technology are a strong business improvement initiative (60%), drive to improve the quality of business process (57%) and operational efficiency and cost savings initiative (39%). Nothing should be different for big data, except that you should ensure you can use your IT resources efficiently and not build new silos of proprietary technology that require specialized resources that might not be aligned to the business value you expect to gain from the technology.
Big data can deliver big value if properly assessed and strategically applied, but like the data warehouse hype from almost 20 years ago, it will take time to ensure it can be properly applied for business and not just serve as a new technology initiative. For my final thoughts on the hyperbole of the Harvard Business Review and Wall Street Journal, they should use research and facts on what business and IT are doing today and what they need for working together to find value from big data since that is really the big deal.
CEO & Chief Research Officer
Filed under: Big Data, Business Analytics, Business Collaboration, Business Intelligence (BI), Business Performance Management (BPM), Cloud Computing, Customer Performance Management (CPM), Financial Performance Management (FPM), Information Management (IM), IT Performance Management (ITPM), Operational Performance Management (OPM), Social Media Tagged: Big Data, Business Analytics, CIO, Data Warehouse, Hadoop, Harvard Business Review, Predictive Analytics, Technology Innovation, Wall Street Journal