From a Hunch to a Punch: Winning with Analytics

Published by: Srini Chari

I am writing this after recently attending three conferences focused on Big Data Analytics in New York:

  1. HPC on Wall Street
  2. Strata Hadoop
  3. IBM’s Analytics for All.
The technical sessions and the end-user discussions in particular were superb and enlightening. Beyond, typical discussions on technology (Hadoop, Spark, Infrastructure, etc.), the end-user panels repeatedly emphasized that organizational and cultural changes were critical to implement and integrate Analytics into core business process and drive value and competitive advantage.

Analytics is a Journey of Progressive Value Creation and Competitive Advantage

Analytics is not just about implementing a set of technologies or solutions, although, this is a necessary foundation for end-users to get derive business value. Analytics is actually a journey from a hunch to the punch where data is iteratively and progressively analyzed by the user and the system to produce powerful insights and punchy actions to outflank competitors and improve profitability.

Progressing From Data to Information to Insights to Action to Wisdom


The prior figure depicts how clients can progressively get time critical insights and higher rewards. But the risks are also higher. The Analytics journey starts with Data and advances to Descriptive, Predictive and Prescriptive Analytics.

Data is fundamental in any Analytics initiative. A data warehouse is typically built to capture, store, secure, retrieve and manage the raw and processed data. Today, data warehousing is widely used by clients and traditional implementations are usually low risk activities. Modern warehouses with newer technologies such as Hadoop and Apache Spark are riskier to implement. But unless data is converted to insights, there is little reward.
Descriptive analytics is dominant today and with low to medium risk and reward. It condenses data into nuggets of insights summarizing what happened. Social media analytics is one prominent descriptive analytics example.

Predictive analytics (medium risk and reward) uses a combination of several statistical, modeling, data mining, and machine learning techniques to analyze data to make probabilistic time-critical forecasts about the future. Weather prediction and customer sentiment analysis are some noteworthy predictive analytics examples.
Prescriptive analytics goes beyond descriptive and predictive analytics. It recommends one or more courses of action and the likely outcome of each action, including the usually time-critical “next best action”. IBM’s Predictive Customer Intelligence solution is one example.
Cognitive computing systems continuously build knowledge over time by processing natural language and data. These systems learn a domain by experience just as humans do and can discover and suggest the “best course of action”; providing highly time-critical valuable guidance to humans. Cognitive computing spans Descriptive, Predictive and Prescriptive Analytics. IBM’s Watson is the premier cognitive system in the market today.
Judicious investments in progressive Analytics competencies give clients unprecedented capabilities to not only validate their hunch, but also deliver a punch through fact-based Cognitive Analytics. These prudent investments, using Financing or Cloud subscription models based on achieving milestones, provide an effective way to mitigate risks while maximizing rewards; sparking widespread adoption of Analytics.

Delivering the Analytics Punch with Cognitive Computing

After the dramatic win at the Jeopardy contest, IBM Watson received considerable buzz in the industry as a supercomputer with unparalleled intelligence. This unfortunately gave the impression that implementing IBM Watson is exotic and complex.

But with the recent IBM Watson Analytics initiative with Expert Storybooks unveiled at the Analytics for All event in New York, a large portion of the software technologies underpinning the IBM Watson supercomputer is now accessible to all and easy to implement. In fact, IBM claims to have over 500,000 (small and medium business and enterprise) users for Watson Analytics just in the last several months. This could be the one of the fastest adoption rates of Analytics software in history, mirroring Apache Spark which is a critical Analytics software infrastructure for implementing deep learning and cognitive computing.
Armed with critical and unique insights and recommendations from Analytics, get ready to punch your competition!

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