Mainframes

MythBusters: You Can’t Do Analytics on the Mainframe! Episode I

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320px-Jamie_Hyneman_with_Blue_AngelsI’ve always been a big fan of the American television show MythBusters – where scientific methods are used to prove or bust all sorts of myths and urban legends.  One of the myths that I’d love to see Adam, Jamie and the team tackle is this: “you can’t do analytics on the mainframe.”  While it probably wouldn’t make for a very entertaining show, this is one piece of conventional wisdom in serious need of debunking – so I guess I’m going to have to give it a go myself!

This is the sort of topic that is going to take multiple “episodes” to cover properly, so in this post I’ll begin by simply setting up the business dynamics that are forcing analytics deeply into operational business processes; I’ll save the actual myth busting for my next blog post, where I’ll take on the IT argument that mainframes and business analytics don’t mix.

From my point of view, the most exciting advancement in business technology is the use of analytics techniques to actually optimize the decision making process.  One of my favorite thought leaders in this space is James Taylor, CEO of Decision Management Solutions.  I highly recommend you read James’ book, Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics.

James has identified four broad areas where decision optimization can have a real impact on both top-line growth and bottom-line savings.  Let’s take a quick look:

Managing Risk

Calculating risk is a huge concern in industries that issue credit (what is the likelihood that the applicant will miss a payment in the future?) or insure assets (what is the likelihood of a loss claim on this asset?).  When risk analysis is deeply ingrained in your core business processes, you gain the competitive advantage of being able to differentiate risk at a very granular level and offer your clients more personalized pricing models.

Reducing Fraud

Any entity that processes a payment of any kind must be able to protect against fraudulent requests.  It’s critical that fraud be detected before the payment is made, because recouping bad payments is both costly and difficult.  Pre-payment fraud detection requires sophisticated analytic models that are tightly integrated with payment systems and capable of flagging problems without adversely effecting legitimate payments.

Targeting and Retaining Customers

Every customer interaction is both an opportunity to provide excellent service and a vehicle for improving business results.  Deep knowledge of your customers, coupled with systems capable of optimizing huge numbers of micro-decisions, can lead to greater loyalty, more effective campaigns and increased spend.

Focusing Limited Resources Where They Will be Most Effective

If you have constrained physical and/or human resources at your disposal to solve complex logistical problems, optimizing the deployment of these resources is crucial to achieving business results, and a bad decision can be very costly and create satisfaction issues.  Think of tasks such as managing cash flow (banking, insurance); ensuring public safety (government); managing airport resources and trucking schedules (travel and transportation); optimizing inventory (retail); and the like.  Real-time analytics can help you make the right decisions and react to changing conditions that are outside of your control.

So what does any of this have to do with the mainframe?

Each of these examples represents a class of business optimization opportunity that must be tied directly into the day-to-day execution of standard business processes; this is not the traditional view of performing analytics after the fact to enhance human decision-making.  This is real-time analytics performed against live – not warehoused – data.

Where do you run your business operations, and where do you keep your operational data?  If you are predominantly a mainframe shop, and the decisions that you want to optimize are based on the processes and data that are on the mainframe, doesn’t it make sense that it would be simpler, easier and more effective to bring the analytics to the data?  Why would you not want to do analytics on the mainframe?

Oh yes, your IT people have told you that it’s not possible; that’s why.  In true cliffhanger fashion I’m going to stop the program here, let you think a bit about how real-time, operational analytics can help you improve your business results and leave the actual myth busting to Episode II. Stay tuned!

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