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A Look Back on My Information and Analytics Strategy Research from 2014

by Doug Laney  |  December 2, 2014  |  Submit a Comment

As we at Gartner plan our research agendas for 2015 (and as you set your 2015 and beyond information and analytics strategies) it’s good to reflect on what we thought and wrote over the past year. So without further ado, here it is:

In Agenda Overview for Information Innovation and Governance, 2014 I shared out that our research was going to feature what happens as Big Data becomes a mainstream concept and how strategies and organizations will need to evolve. First and foremost, information needs to be treated as an actual business asset. The key issues for IT and business leaders that this research agenda we focused on during the year were:

  • What are the keys to improved information leadership and vision?
  • How should organizations strategize, plan and govern traditional and new forms of information such as big data?
  • What are the range of sources and uses of information that should be considered by businesses?
  • How can organizations use established economic principles to measure and improve the value of their information assets and the investments in them?

In How Organizations Can Monetize Customer Data Olive Huang and I posited that customer data has discernible monetary value and suggested several business models and approaches to monetizing it, both directly and indirectly. And in the companion piece, Improving the Value of Customer Data Through Applied Infonomics, we showed how to apply our seven principles of infonomics to manage and measure the value of information as if it were a balance sheet asset, leading to its improved realized value.

With Frank Buytendijk in Information 2020: Beyond Big Data we highlighted research and ideas on how to address coming organizational conflicts, grab hold of the exciting promise of information, and fortify against the real fears of information misuse. In this piece we include a table that shows nine distinct ways information management is already, or will be, changing in your organization, particularly due to Big Data becoming commonplace.

In Customer Analytics and the Art of the Possible With Big Data Jenny Sussin and I continued to pound home the notion that information of almost any variety can be a boon for sales, marketing and customer service functions, and therefore should be treated as an essential corporate asset. And we shared that in 2014 business and IT professionals now deem new product and service innovation a better use of Big Data than even marketing and sales growth. Still, we included several real-world stories about how organizations have radically transformed sales and marketing related business functions through applied information and advanced analytics.

Our crowd favorite, Cool Vendors in Information Innovation, 2014, discussed how there are now millions of available online open data sets available as a fuel additive for business performance and innovation, and featured a few vendors taking a leadership role in innovating with open data, including:

  • HG Data that pulls data from the web to discover what kinds of IT products a particular organization is using
  • Prevedere that mashes an organization’s own data with up to one million exogenous data sets to discover predictive indicators
  • ProgrammableWeb that aggregates available APIs and online data sources into a searchable directory
  • SkyFoundry that helps organizations manage and generate value from internet-of-things (IoT) data

Of course data science is seen as the primary means to go beyond basic BI to achieve diagnostic, predictive and prescriptive analytics. But skills are in terribly short supply and will continue to be so into the foreseeable future. So Alexander Linden and I promoted a none-to-radical idea: crowdsourcing. In Four Steps to Effective Crowdsourcing of Data Science Projects we describe how to leverage the power of community and the Internet effectively for targeted advanced analytic needs.

Andrew White and I decided to explore economic theory and practice in the context of information assets. In our piece Increase the Return on Your Information Investments With the Information Yield Curve we adapted the traditional yield curve concept to the discipline of enterprise information management (EIM). We showed how and why the information rate of return (IRR) accelerates then flattens as an organization evolves from immature to mature to optimized EIM, at which point the yield curve becomes asymptotic to both the current state-of-the-art technology and the universe of available data.  We also identified dozens of forces pushing downward and upward on the curve, and how to discourage and encourage them respectively.

In a work of extreme collaboration, many of our top analytics and information management & strategy analysts came together to produce, Answering Big Data’s 10 Biggest Vision and Strategy Questions, in which we addressed the top questions we receive on the topic of Big Data from our clients, including:

  • How to communicate the value and economics of Big Data projects
  • Understanding the many uses and sources of Big Data, internal and external
  • Reconsidering information leadership, organization and skills
  • Identifying key Big Data strategy, planning and governance
  • Leveraging social, mobile and cloud as and when appropriate

And finally, in our 2015 Predicts pieces related to information and analytics (i.e. Predicts 2015: Big Data Challenges Move From Technology to the Organization;; Predicts 2015: Information Governance and MDM Will Be Foundational to Improving Digital CulturePredicts 2015: The Intersection of Information Innovation and Business DigitalizationPredicts 2015: Power Shift in Business Intelligence and Analytics Will Fuel Disruption) I personally offered an analysis of and recommendations for the following strategic planning assumptions:

  • Through 2017, fewer than half of lagging organizations will have made cultural or business model adjustments sufficient to benefit from big data.
  • Through 2016, less than 10% of self-service BI initiatives will be governed sufficiently to prevent inconsistencies that adversely affect the business.
  • By 2017, 50% of information governance initiatives will have incorporated the concept of information advocacy, to ensure they are value-driven.
  • By 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.

During the year I also researched and advised clients on using and publishing open data, and the emerging role of the chief data officer (CDO) that I presented at Gartner Symposium, our BI/Analytics and Information Management Summits, CIO Summits. Additionally, I continued compiling our growing library of hundreds of real-world examples how organizations around the world and in every industry are using information and analytics in high-value, transformative ways.

Finally, I published infonomics-related pieces in Forbes (The Hidden Tax Advantage of Monetizing Your Data and The Hidden Shareholder Boost From Information Assets) and in this blog (Twitter’s Secret Nest Egg is in Plain Sight and To Twitter, You’re Worth $101.70).

As always, thanks for reading my research, articles, blog and tweets! I hope you find them interesting, informative, and especially, inspirational.

Follow Doug on Twitter: @Doug_Laney

Category: 

Tags: analytics  big-data  business-intelligence  cdo  chief-data-officer  crm  customer-data  data-governance  data-science  infonomics  information  information-governance  information-innovation  information-strategy  innovation  internet-of-things  iot  predictions  

Doug Laney
VP and Distinguished Analyst, Chief Data Officer Research
10 years at Gartner
30 years in IT industry

Doug Laney is a research vice president and distinguished analyst with Gartner. He advises clients on data and analytics strategy, information innovation, and infonomics (measuring, managing and monetizing information as an actual corporate asset). Follow Doug on Twitter @Doug_Laney...Read Full Bio




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