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16
Nov

Predictive Analytics – A Project or a Program?


Our AMEX credit card was recently compromised.  Someone got hold of the card information and Petro Canada charges started to rack up.   Amex spotted this suspicious pattern and immediately initiated a fraud alert thru multiple touch points.

What does your credit card company know about you?  A lot…maybe more than your spouse. A study of how customers of Canadian Tire were using the company’s credit cards found that 2200 of 100,000 cardholders who used their card at drinking places missed four payments within the next 12 months. By contrast, only 530 of the cardholders who used their card at the dentist missed four payments within the next 12 months. So drinking is a predictor of credit risk.

Predictive analytics is not a fad. It’s not a trend.  In a real-time world, Analytics is a  core business requirement/capability.  However, many organizations flounder in their efforts not because they lack analytics capability but because they lack clear objectives. So the first question is, What do you want to achieve?

Analytics so far has largely been a departmental ad hoc activity.   Even at the most sophisticated corporations, data analytics is  a cumbersome affair. Information accumulates in “data warehouses,” and if a user had a question about some trend, they request “data priests/analysts” to tease the answers out of their costly, fragile systems.  This resulted in a situation where the analytics are done looking in the rearview mirror, hypothesis testing to find out what happened six months ago.

Today it’s possible to gather huge volumes of data and analyze it in near real-time speed. A retailer such as Macy’s  that once pored over last season’s sales information could shift to looking instantly at how an e-mail coupon impacts sales in different regions.  Moving to a realtime model and also building an enterprise level “shared services” model is going to be the next big wave of activity.

Read more »

11
Nov

Big Data Infographic and Gartner 2012 Top 10 Strategic Tech Trends


Data, data and more data…data is everywhere…data is important… By 2015, nearly 3 billion people will be online, pushing the data created and shared to nearly 8 zettabytes.  Centurylink created this cool infographic to highlight the data deluge and big data issues.  Gartner 2012 Top 10 Tech Trends illustrated some examples of this.

  • 30 billion pieces of content were added to Facebook this past month by 600 million plus users.
  • Zynga processes 1 petabyte of content for players every day, a volume of data that is unmatched in the social game industry.
  • More than 2 billion videos were watched on YouTube … yesterday.
  • The average teenager sends 4,762 text messages per month.
  • 32 billion searches were performed last month … on Twitter.
  • Worldwide IP traffic will quadruple by 2015 (Cloud is a big driver for this; most corporations are racing to upgrade networks and connectivity)

Time for a strategy…. I have visited several large corporations in the past month that are beginning to  build strategies and tangible plans.   This may be the difference between reacting and prospering in the world of Big Data and predictive analytics. Read more »

6
Nov

What is a “Hadoop”? Explaining Big Data to the C-Suite


Keep hearing about Big Data and Hadoop? Having a hard time explaining what is behind the curtain?

The term “big data” comes from computational sciences to describe scenarios where the volume of the data outstrips the tools to store it or process it.

Three reasons why we are generating data faster than ever: (1) Processes are increasingly automated; (2) Systems are increasingly interconnected; (3) People are increasingly “living” online.

DataEvolutionAs huge data sets invaded the corporate world there are new tools to help process big data. Corporations have to run analysis on massive data sets to separate the signal from the noisy data.  Hadoop is an emerging  framework for Web 2.0 and enterprise businesses who are dealing with data deluge challenges – store, process, index,  and analyze large amounts of data as part of their business requirements.

So what’s the big deal? The first phase of e-commerce was primarily about cost and enabling transactions.  So everyone got really good at this. Then we saw differentiation around convenience… fulfillment excellence (e.g., Amazon Prime) , or relevant recommendations (if you bought this and then you may like this – next best offer).

Then the game shifted as new data mashups became possible based on… seeing who is talking to who in your social network, seeing who you are transacting with via credit-card data, looking at what you are visiting via clickstreams, influenced by ad clickthru, ability to leverage where you are standing via mobile GPS location data and so on.

The differentiation is shifting to turning volumes of data into useful insights to sell more effectively. For instance, E-bay apparently has 9 petabytes of data in their Hadoop and Teradata cluster. With 97 million active buyers and sellers they have 2 Billion page view and 75 billion database calls each day.  E-bay like others is racing to put in the analytics infrastructure to (1) collect real-time data; (2) process data as it flows; (3) explore and visualize. Read more »

2
Nov

IBM CIO Study: BI and Analytics are #1 Priority for 2012/2013


“Running a company is an endless quest to find out things you don’t know“

– Jeff Immelt, CEO GE

What will 2012 bring?  Recently, I attended the CIO Executive Leadership Summit in Greenwich, Connecticut. I was particularly intrigued by the presentation by the new CIO of IBM, Jeanette Horan where she presented the projects she was tackling and how IBM is thinking about business analytics.

IBM is making a bet that “true leaders” will develop the capabilities required for making good and timely decisions in unpredictable and stressful environments.

IBM is adapting to this new data analytics reality by a rapid-fire acquisition strategy:  Cognos,  Netezza, SPSS, ILog, CoreMetrics, Algorithmics, OpenPages, Clarity Systems, Emptoris, DemandTec (for retail).  IBM also has other information management assets like Watson, DB2 etc.  They are building a formidable capability around the value chain: “Raw Data -> Aggregate Data -> Intelligence ->Insight -> Decisions” . They see this as a $20Bln opportunity. Read more »

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