From strategic to operational decision making: Decisions at scale

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Man in suit overlooking city landscapeLuckily, or perhaps better said, hopefully, we only need to make the big life decisions every now and then. What school to go to? Who to marry? What job to take? Where to live? There’s no penultimate answer to these decisions, but we all take them to the best of our knowledge, feeling and ability.

Likewise in organizations, we don’t make big strategic decisions every day. Which customers to focus on? Which products? Which regions? Strategy revolves around crunching the numbers, evaluating the situation, incorporating past experience, and choosing a direction. Since emerging on the organizational agenda, analytics have always played a role in informing strategic decisions.

In life, the follow up on big life decisions requires hundreds, even thousands of daily micro-decisions. Do I study now or do I study later? At this moment, should I look at work mails or talk to my wife? Should I engage in the neighborhood committee? No single decision is make or break, but if the big decision was a good one, then the little decisions should be consistent with it.

This is just as crucial in organizations. A strategic decision without operational consistency and follow-through is hollow. Hundreds of thousands of daily operational decisions should be informed by relevant data and fully consistent with organizational strategy. This requires a change in approach and most definitely a change in scale. Enter "Decisions at scale."

Decisions at scale means generating the thousands of relevant, data-driven analytic models, encasing them in relevant business logic and seamlessly deploying them as flexible, intelligent decision engines throughout the organization.

SAS provides a complete offering enabling Decisions at Scale with the following components:

  • Data prepration for analytics – accessing and preparing the right data at the right time.
  • SAS Factory Miner – More models, more quickly and allowing Data Scientists to focus on innovation through modelling by exception.
  • Enterprise Miner – exhaustive deep modeling of challenge areas, including the range of open source analytics offerings.
  • Decision Manager – a combination of Business Rules Manager and Model Manager, enables business users to design, test and deploy decision packages and learn through a feedback loop

By embracing decisions at scale, organizations will reap far more return on their current analytics investment and enable their data scientists to better satisfy the organization's growing appetite for analytics.

As for those personal life decisions, my advice is to trust yourself, you've made it this far. Finally, I'll be so bold as to offer this incontrovertible nugget from Baz Luhrmann, "If there is extreme sunshine, then use sunscreen."

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About Author

Andrew Pease

Principal Business Solutions Manager

After 14 years in various roles at SAS, Andrew is currently responsible for advanced analytics in the Center of Excellence. Andrew helps financial institutions, major retailers, pharmaceuticals, manufacturers, utilities and public sector to understand and use powerful analytic techniques such as decision management, predictive modelling, time-series forecasting, optimization, and text mining.

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