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Using Search Analytics To See Into Gartner's $232B Big Data Forecast

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By combining search analytics and the latest Gartner forecast on big data published last week, it’s possible to get a glimpse into this areas’ highest growth industry sectors.

Big data is consistently a leading search term on Gartner.com, which is the basis of the twelve months of data used for the analysis.

In addition, data from Gartner’s latest report, Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016 by Mark A. Beyer, John-David Lovelock, Dan Sommer, and Merv Adrian is also used.  These authors have done a great job of explaining how big data is rapidly emerging as a market force, not just a single market unto itself.  This distinction pervades their analysis and the following table showing Total IT Spending Driven by Big Data reflects the composite market approach.

Use cases from enterprise software spending, storage management, IT services, social media and search forecasts are the basis of the Enterprise Software Spending for Specified Sub-Markets Forecast.  Social Media Analytics are the basis of the Social Media Revenue Worldwide forecast shown below:

Additional Take-Aways

  • Enterprise software spending for specified sub-markets will attain a 16.65% compound annual growth rate (CAGR) in revenue from 2011 to 2016.
  • Attaining a 96.77% CAGR from 2011 through 2016, Social Media Revenue Is one of the primary use case catalysts of this latest forecast.
  • Big Data IT Services Spending will attain a 10.20% CAGR from 2011 to 2016.
  • $29B will be spent on big data throughout 2012 by IT departments.  Of this figure, $5.5B will be for software sales and the balance for IT services.
  • Gartner is projecting a 45% per year average growth rate for social media, social network analysis and content analysis from 2011 to 2016.
  • Gartner projects a 20 times ratio of IT Services to Software in the short term, dropping as this market matures and more expertise is available.
  • By 2020, big data functionality will be part of the baseline of enterprise software, with enterprise vendors enhancing the value of their applications with it.

  • Organizations are already replacing early implementations of big data solutions – and Gartner is projecting this will continue through 2020.
  • By 2016 spending on Application Infrastructure and Middleware becomes one of the most dominant for big data in Enterprise Software-Specified Sub Markets.
  • $232B is projected to be sold in total across all categories in the forecast from 2011 to 2016. From $24.4B in 2011 to $43.7B in 2016, this presents a 12.42% CAGR in total market growth.

Search Analytics and Big Data

Big data is continually one of the top terms search on Gartner.com, and over the last twelve months, this trend has accelerated.  The following time series graph shows the weekly number of inquiries Gartner clients have made, with the red line being the logarithmic trend.

 

Banking (25%), Services (15%) and Manufacturing (15%) are the three most active industries in making inquiries about big data to Gartner over the last twelve months.  The majority of these are large organizations (63%) located in North America (59%) and Europe (19%).

What unifies all of these industries from a big data standpoint is how critical the stability of their customer relationships are to their business models.  Banks have become famous for bad service and according to the American Customer Satisfaction Index (ACSI) have shown anemic growth in customer satisfaction in the latest period measured, 2010 to 2011.  The potential for using big data to becoming more attuned to customer expectations and deliver more effective customer experiences in this and all services industries shows great upside.

Bottom line: Companies struggling with flat or dropping rankings on the ACSI need to consider big data strategies based on structured and unstructured customer data.  In adopting this strategy the potential exists to drastically improve customer satisfaction, loyalty, and ultimately profits.