Frequently Asked Questions About Analytics 3.0

What are the characteristics of Analytics 3.0?

Tom Davenport, IIA’s Co-Founder and Research Director, describes the traits of Analytics 3.0 as follows:

  • Organizations are combining large and small volumes of data, internal and external sources, and structured and unstructured formats to yield new insights in predictive and prescriptive models
  • Analytics are supporting both internal decisions and data-based products and services for customers
  • The Hadoopalooza continues, but often as a way to provide fast and cheap warehousing or persistence and structuring of data before analysis—we’re entering a post-warehousing world
  • Faster technologies such as in-database and in-memory analytics are being coupled with “agile” analytical methods and machine learning techniques that produce insights at a much faster rate
  • Many analytical models are being embedded into operational and decision processes, dramatically increasing their speed and impact
  • Data scientists, who excel at extracting and structuring data, are working with conventional quantitative analysts who excel at modeling it—the combined teams are doing whatever is necessary to get the analytical job done
  • Companies are beginning to create “Chief Analytics Officer” roles or equivalent titles to oversee the building of analytical capabilities
  • Tools that support particular decisions are being pushed to the point of decision-making in highly targeted and mobile “analytical apps”
  • Analytics are now central to many organizations’ strategies; a Deloitte survey found that 44% of executives feel that analytics are strongly supporting or driving their companies’ strategies.

What defined Analytics 1.0?

Analytics 1.0 represents an era marked by enterprises assembling business intelligence systems and expertise to drive reporting and descriptive analytics. During this phase, very few enterprises view their systems as capable of generating predictive or prescriptive analytics. Enterprises focus on the internal, structured data that they generate without giving much thought to other types or sources of data. In this stage, most organizations do not view their data as a valuable “asset” that might sit on the balance sheet like physical equipment or inventory. For companies that went through Analytics 1.0 many years ago, the concept that competitive battles might be won or lost based on data assets an enterprise controlled was foreign at the beginning.

What are the characteristics of Analytics 1.0?

In Tom Davenport’s February 20, 2013 CIO Journal blog post, he describes Analytics 1.0 as being characterized by the following attributes:

  • Data sources were relatively small and structured, and came from internal sources
  • Data had to be stored in enterprise warehouses or marts before analysis
  • The great majority of analytical activity was descriptive analytics, or reporting
  • Creating analytical models was a “batch” process often requiring several months
  • Quantitative analysts were segregated from business people and decisions in “back rooms”
  • Very few organizations “competed on analytics”— for most, analytics were marginal to their strategy.

What defined Analytics 2.0?

In 2010, many companies entered the beginning of Analytics 2.0, when they became aware of “big data”. The primary differentiator between Analytics 2.0 and Analytics 1.0 is the emergence of fast moving, external, large, and unstructured data coming from various new and interesting sources. As such it has to be stored and processed rapidly, often with parallel servers running technologies like Hadoop. The overall speed of analytics increased and visual analytics (a form of descriptive analytics) gained prominence however predictive and prescriptive techniques were still not the main use of analytics. In this stage a new community of “data scientists” emerges that fosters experimentation, hacking and data mashups. Regardless of industry, most enterprises are discussing new data product business opportunities that may lie ahead of them. Big data is still very popular and, for many organizations remains a challenge they are struggling to overcome.

Where are most businesses performing in the evolution of Analytics today?

The majority of companies today are still operating within Analytics 1.0 and 2.0. However, industry leaders are entering the Analytics 3.0 world. To capture competitive advantage, firms must prepare for and embrace Analytics 3.0.

Who are some of the companies leading the Analytics 3.0 charge?

Proctor & Gamble is a prime example of an Analytics 3.0 enterprise. P&G CEO Bob McDonald and CIO Filippo Passerini share an enthusiasm for sophisticated analytics and have created a culture that supports moving business intelligence from the periphery of operations to the center of how business gets done. Together, they have put people and solutions in place that reflect this focus, including P&G’s well-received “Business Sphere” rooms for executive review and decision-making. There are firms in other industries such as Williams Sonoma in retail and Bank of America in banking that are also entering the 3.0 world.

Are there specific tools required for Analytics 3.0?

Companies need to have the technical infrastructure to manage the volume of data. This includes technologies like Hadoop, in-memory and in- database analytics and enough computing power to handle the complex calculations. In addition, companies need appropriate tools to effectively support decision making such as mobile and self-serve “analytical apps”.

Which industries will be most impacted by Analytics 3.0? Who will benefit the most?

All industries will be impacted by Analytics 3.0. The benefits from effectively leveraging big data, embedding data into decision making and truly becoming an analytical competitor will apply to any firm in any industry. However, the speed with which companies are able to enter this world will depend on the analytical maturity of the industry. Manufacturing, retail and banking are industries characterized as being highly analytical. As such, there are firms already entering the era of Analytics 3.0. By contrast, there are other industries still in the Analytics 1.0 era.

What role will Data Scientists play in this new era/evolution?

Data scientists are a critical element of the shift to an Analytics 3.0 world. They have the skills to extract and structure the complex, high volume data sets in use by organizations. However, they need to work closely with IT and traditional quantitative analysts to develop insights for the business. Equally critical is the “translator” function that insures that the business and analytics teams are collaborating effectively when working to quickly provide insights into critical business problems.

Is Analytics 3.0 a future vision or a trend that is happening today?

There is considerable evidence that organizations are already entering the Analytics 3.0 world. It’s an environment that combines the best of 1.0 and 2.0—a blend of big data and traditional analytics that yields insights and offerings with speed and impact. It is in its early days, but IIA expects leaders to rapidly enter the world of Analytics 3.0 over the next few years.

How can companies prepare for Analytics 3.0?

Companies can prepare for Analytics 3.0 in several ways:

  • Develop the analytics culture necessary to enter the Analytics 3.0 world. This means a company must align analytics with strategic priorities, insure leadership is championing analytics and leading by example, and creating a Chief Analytics Officer (or equivalent role) to oversee the strategic deployment of analytics.
  • Invest in the technology needed to manage big data and provide insights quickly.
  • Recruit, retain and effectively leverage analytical talent. Insure that analytics groups are aligned with the business and focusing on critical business questions.

Who is Tom Davenport?

IIA co-founder Tom Davenport is an analytics industry pioneer. As such, Tom is leading the charge in articulating the new framework of Analytics 3.0 and continues to study this new era and the way it will shape business going forward. Voted the third leading business-strategy analyst (just behind Peter Drucker and Tom Friedman) in Optimize Magazine, Tom Davenport is a world-renowned thought-leader who has helped hundreds of companies revitalize their management practices. He combines his interests in business, research, and academia as the Visiting Professor at Harvard Business School. Tom’s Competing on Analytics idea was recently named by Harvard Business Review as one of the twelve most important management ideas of the past decade and the related article was named one of the ten must read articles in HBR’s 75 year history. Published in February 2010, Tom’s related book, Analytics at Work: Smarter Decisions, Better Results, was named one of the top fifteen must reads for 2010 by CIO Insight.

What is the International Institute for Analytics (IIA)?

IIA is an independent research firm established for analytical competitors committed to maturing their analytics programs. IIA’s research is designed to guide organizations in leveraging data via the power of analytics as a valuable and predictive strategic asset that enables them to make better decisions and identify new opportunities. IIA was founded on the premise that knowledge and community are key components to becoming analytically competitive and believes that those organizations that compete on analytics will be the leaders of their industries. Unlike other research firms, IIA’s insights are generated from the collective wisdom and practical experience of its unique community of practitioners, faculty and industry experts. Connecting these key constituents and facilitating meaningful dialogue results in research that defines the path to analytics excellence, inspires emerging leadership, and supports on-going decision making within enterprises.

Where can I learn more?