Harvard Business Review, Article #1, Analytics 3.0

HBR  Analytics 3.0
by Thomas H. Davenport Dec 2013
http://hbr.org/2013/12/analytics-30/ar/1

The Evolution of Analytics
Briefly, it is a new resolve to apply:

• Powerful data-gathering 
• Analysis methods 

not just to a company’s operations but also to its offerings to embed data smartness into the products and services customers buy.


History 

• The use of data to make decisions is not a new idea.
• It is as old as decision making itself. 
• But the field of business analytics was born in the mid-1950s

Analytics 1.0 the era of “business intelligence

• For the first time
• data about production processes 
• sales
• customer interactions 
• and more 

         were 

• recorded 
• aggregated
• analyzed
• This was the era of the enterprise data warehouse

• used to capture information 

•  business intelligence software

• used to query and report it

• Readying a data set for inclusion in a warehouse was difficult

• Analysts spent much of their time preparing data for analysis and relatively little time on the analysis itself

Analytics 2.0 the era of big data

• At the mid 2000s, when internet-based and social network firms primarily in Silicon Valley (Google, eBay, ...) began to amass and analyze new kinds of information
• Big data also came to be distinguished from small data because it was not generated purely by a firm’s internal transaction systems
• Big data couldn’t fit or be analyzed fast enough on a single server
•  So it was processed with Hadoop, an open source software framework for fast batch data processing across parallel servers
• To deal with relatively unstructured data companies turned to a new class of databases known as NoSQL
• Machine-learning methods (semi automated model development and testing) were used to rapidly generate models from the fast-moving data

Analytics 3.0 the era of data-enriched offerings

• The pioneering big data firms in Silicon Valley began investing in analytics to support customer-facing products, services, and features
• The common thread in these companies management to compete on analytics 
• not only in the traditional sense (by improving internal business decisions) 
• but also by creating more-valuable products and services
• Any company, in any industry, can develop valuable products and services from their aggregated data
• This strategic change in focus means a new role for analytics within organizations

Ten Requirements for Capitalizing on Analytics 3.0

1. Multiple types of data, often combine
2. A new set of data management options
1. In the 1.0 era, firms used data warehouses as the basis for analysis. 
2. In the 2.0 era, they focused on Hadoop clusters and NoSQL databases. 
3. Today the technology answer is “all of the above”
3. Faster technologies and methods of analysis
4. Embedded analytics
1. Consistent with the increased speed of data processing and analysis, models in Analytics 3.0 are often embedded into operational and decision processes
2. Some firms are embedding analytics into fully automated systems through 
1. Scoring algorithms 
2. Analytics-based rules.
5. Data discovery
6. Cross-disciplinary data teams
1. Companies now employ data hackers, who excel at extracting and structuring information, to work with analysts, who excel at modeling it
7. Chief analytics officers
8. Prescriptive analytics
1. There have always been three types of analytics:
1. Descriptive, which reports on the past
2. Predictive, which uses models based on past data to predict the future 
3. Prescriptive, which uses models to specify optimal behaviors and actions
2. Analytics 3.0 includes all three types, 
3. Analytics 3.0  emphasizes the last
9. Analytics on an industrial scale 
1. Analytics 3.0 provides an opportunity to scale those processes to industrial strength
2. Creating many more models through machine learning can let an organization become much more granular and precise in its predictions
10. New ways of deciding and managing 
1. Some of the changes prompted by the widespread availability of big data will not yield much certainty
2. Big data flows continuously consider the analysis of brand sentiment derived from social media sources and so metrics will inevitably rise and fall over time 
3. Additional uncertainty arises from the nature of big data relationships. Unless they are derived from formal testing, the results from big data generally involve correlation, not causation

Creating Value in the Data Economy

• Analytics 3.0 is the direction of change and the new model for competing on analytics.

Thomas H. Davenport is the President’s Distinguished Professor of IT and Management at Babson College, a fellow of the MIT Center for Digital Business, a senior adviser to Deloitte Analytics, and a cofounder of the International Institute for Analytics (for which the ideas in this article were generated). He is a coauthor of Keeping Up with the Quants (Harvard Business Review Press, 2013) and the author of Big Data at Work (forthcoming from Harvard Business Review Press).