Analytics 3.0 and Beyond: From Ad Hoc Insights to Automated Decisions

Interesting presentation by  Tom Davenport at 2015 Salesforce keynote.

Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Center for Digital Business, and a Senior Advisor to Deloitte Analytics. He teaches analytics and big data in executive programs at Babson, Harvard Business School, MIT Sloan School, and Boston University. He pioneered the concept of “competing on analytics” with his best-selling 2006 Harvard Business Review article (and his 2007 book by the same name). His most recent book is Big Data@Work, from Harvard Business Review Press.

 

He views are consistent to want we do at HIVERY, we are pioneering what we call “Prescriptive Science™” – what he calls “Analytics 4.0”

 

Below I’ve summarized his talk along with few examples:

 

 

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Here is a summary.  He talks about 4 types of analytics:

 

  • Analytics 1.0 was the era of business intelligence –  descriptive, which reports on the past.
  • Analytics 2.0 was about Big Data – which uses models based on past data to predict the future;
  • Analytics 3.0 is the era of data-enriched offerings – which is about being prescriptive. It uses models to specify optimal behaviors and actions.
  • Analytics 4.0  — the idea of automated analytics. These come to full fruition in a new era. Machines talk to machines to carry out decisions within human input.
    • Moving towards “Automated decision from interconnected smart machines”.
    • Connected sensors and the “Analytics of Things”.
    • Things will be “augmented” rather “automated”

 

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Examples of established (old) companies leveraging data-driven approaches to model business innovation

 

 

GE 3.0 a 123 years old company

  • Built a new business – $2b initiative in software, analytics and what they call the “Industrial internet” called Predix.
  • Producing “data-based products and services”
  • Add sensors to their industrial products (e.g. gas turbine, jet engines, and trains etc) to understand how they are preforming.
  • Key objective: to revolution services it offers.
  • Why? 75% of its profit comes from industrial services. If its able to estimate when I jet engine is going to break and service it before it breaks, they can make a lot of money. Ability to sell a unit of “jet thrust” instead of jet engine and produce that “jet thrust” for less, they can make more profits. So GE puts sensors on their jet engines to allow them to service them better (i.e. forecast when likely its going to break down) but also create new business model of charging for jet engines

 

Monsanto 3.0 – 114 years old company

  • Creating “frankenfood,” but Monsanto is not the only company that produces genetically modified organisms
  • If we are going to feed 9 billion people, I think we going to need some innovation in agriculture. Its a agricultural biotechnology company
  • It has “Precision planting” or “prescriptive planning” offering to sold to farmers. That is not just selling seeds and pesticides, but sell “advice” to farmers when to plant, what to plant, how much to water, when to put pesticides, when to harvest this year. These are new “data-products”
  • In 2013, it acquired is a huge agritech startup: Climate Corporation for approximately $1.1 billion. The company uses machine learning to predict the weather and other essential elements for agribusiness.
  • It provides “field-level” highly granular weather data – called “FieldScripts” That is provide insight to when its going to rain and when pest going to start, to prescribe when to apply pesticides to farms.
    – Yield with this advice for farmers, increase by 10% to 20%.

 

Ford 3.0 – 112 years old company

  • Bill Ford “The car is really becoming a rolling group of sensors”
  • Ford Creates New Chief Data, Analytics Officer Position
  • Fords Digital analytics and optimisation team – defined Ford’s digital web analytics strategy & standards for all B2C properties providing Ford a competitive advantage of integrated business intelligence and targeted marketing opportunities.  Example of talent include.
  • Targeted marketing – Digital In-Market Manager is responsible for all activities related to targeting and messaging in-market auto shoppers in order to convert them to Ford customers. This includes establishing an “always on” approach to targeting shoppers online. Position responsibilities will include strategy, creative, production, optimization and media planning for all in-market activities
  • Business intelligence – help dealers become more successful by providing smart inventory system: instead of sending new cars to all dealers, now they can figure out what type of car is most likely to sell in particular dealer lot and increase revenue by $100m

LinkedIn – 14 years old company

  • Has a lot “data-products” including “People You may know”, “Jobs You May be Interested in”. “Groups you May Like”
  • Uses it data to determine who is most likely going to buy Linkedin services

 

View it here https://www.salesforce.com/video/183657/ or http://www.tomdavenport.com/blogs-articles/