As many know, I believe end-of-year predictions have zero value and I prefer to look at important trends that are already unfolding and will impact readers next year. The most important trend right now for people in the social media and gaming spaces, as well as almost anyone in the tech space, is the evolution of analytics. Thomas Davenport, author of the seminal work Competing on Analytics, recently wrote an article in the Harvard Business Review about Analytics 3.0. Just as Analytics 2.0 transformed the gaming space, allowing companies like Zynga, Playfish and Disney to leap over established competitors, Analytics 3.0 can reshape as dramatically the gaming ecosystem. Analytics 3.0 is a new resolve to apply powerful data gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products, services and games that customers buy.
A brief history of analytics
To understand best the impact of Analytics 3.0, it is helpful to understand 1.0 and 2.0 and their impact. Analytics 1.0 ushered in an objective, deep understanding of important business phenomena and gave managers and leaders the fact-based comprehension to go beyond intuition when making decisions. Data about sales, customer interactions, production processes, etc., were recorded, aggregated and analyzed. For the first time, analytics were used to compete by creating greater efficiency: making better decisions on key issues to improve performance.
The era of big data defined Analytics 2.0, and it revolutionized the game industry. In this era, companies like Google, Facebook, Amazon, etc., began to amass and analyze new kinds of information. Although not initially called “Big Data,” it quickly changed the role of data and analytics in those firms.
As analytics entered the 2.0 phase, the need for powerful new tools became apparent. Companies rushed to build new capabilities and acquire customers. Innovative technologies of many kinds had to be created, acquired and mastered. Since a single server was not fast enough or large enough to analyze big data, products like Hadoop came to market to meet the need. Much information was now stored in public or private-cloud computer environments. In the game space, companies with strong internal analytic tools and databases came out of nowhere to reach valuations in the billions of dollars.
The competencies required for Analytics 2.0 were very different from those needed for 1.0. Also, the data scientists were not content to remain in the back office; they wanted to work on new product offerings and help shape the business. In the game space, the “quants” actually moved to the forefront of product design and development.
As Davenport points out, “During 2.0, a sharp-eyed observer could have seen the beginnings of analytics’ next big era.” The leading data-driven companies started investing and focusing on analytics and attracted viewers (or players, in the game space) by the millions with better algorithms, recommendations, targeted feed posts, all driven by analytics rooted in enormous amounts of data.
Analytics 3.0 marks the point at which other large organizations started to follow suit. Every device, shipment and consumer leaves a trail. Companies now have the ability to analyze those sets of data for the benefit off consumers and markets. But it does not have to be traditional companies, as I wrote in November even tech companies like Uber can use this data to take its services and products to the next level. The key principle in Analytics 3.0 is that companies compete in this world not only by relying on analytics in the traditional sense (improved internal decision making, including what features to include in products) but also by creating more-valuable products and services.
Davenport uses Germany’s 127-year-old Bosch Group as an example of a traditional company being drawn into Analytics 3.0. Bosch has embarked on a series of initiatives across business units that make use of data and analytics to provide “intelligent customer offerings.” These include intelligent fleet management, vehicle-charging infrastructures, energy management, security video analysis, etc. To identify and develop these innovative services, Bosch created a Software Innovations Group that focuses heavily on big data and analytics.
What sets Analytics 3.0 apart from the first two phases is the central importance of prescriptive analytics. There are three types of analytics: descriptive (what happened in the past), predictive (modeling what will happen in the future) and prescriptive (the use of models to specify optimal behaviors and actions). Although Analytics 3.0 includes all three types, it emphasizes prescriptive. Prescriptive models involve large-scale testing and optimization and are a means of embedding analytics into key processes and employee behavior.
The importance of Analytics 3.0
The companies—particularly in the game industry—that did not see or intentionally ignored Analytics 2.0 are either not still around or had to spend hundreds of millions of dollars to catch up. Those that embraced Analytics 2.0 generated billions in market cap and became some of the biggest employers. Analytics 3.0 can create similar changes in the tech and gaming space, so being at the forefront can have huge benefits.