From Tactical to Strategic Action: Operational Decision Management

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How do companies stand out in today’s commoditized markets? What routes do companies take to become or remain more competitive? Companies now must not only perform better with their go-to-market strategies to sustain growth and profitability, they have to tap into operational processes to improve performance and find new ways to innovate. Operational functions cut across the enterprise; enterprises must now proactively integrate operational functions, business processes and data with other groups, to break down silos that impede positive performance.

As with LOB strategies, the operations strategy must map into the overall corporate strategy and support desired outcomes and goals. Corporate strategy should point the way, but operational strategy is all about how corporate strategy is enacted every day. The operational function enhances its value to corporate strategy as daily transactions and decisions accumulate over time. Data is highly apparent in operational processes, as it is both used by and created by these processes. Most enterprises make numerous operational decisions every day – the volumes of data from many sources rapidly accrue and become an invaluable asset.

The context of daily operational decisions and continuously generated operational data combine as a one-two punch that drives massive impact on the enterprise for short term and long term outlooks. This one-two punch also contributes to fine-tuning and transforming strategies at all levels. A variety of business intelligence and analytics processes can be put into play to benefit daily decisions as well as bring richer and timely insight for high level corporate decisions.

For daily operational decision-making, advanced analytics can be used to improve accuracy, timeliness and relevance. Drivers for improvement can target business process efficiency, risk management, clear customer focus, competitive activities, and finding new opportunities. And now real-time analytics are transforming operational functions and staff into real-time decision makers.

For taking advantage of predictive analytics, operational processes provide a perfect storm of cumulative data, decisions made and actions taken that feed into analytical models. To build useful analytical models requires data over time, where various patterns are derived to bring better insight to decision-making. The value of operational decisions over time generates a sum that is far greater than the daily or one-off decision-making parts.

It’s obvious how predictive analytics that draw on data volumes generated by daily operations can be quite valuable for high level corporate decision-making. But predictive analytics for daily operations itself becomes a matter of “predicting the present”, which sounds contradictory. The predictive value for daily operations is not about the future, but about similar patterns of behavior or event occurrences. What can be revealed is a customer’s predisposition to Action B, because Action A was taken – based on predictive modeling.

One significant change that comes about from using predictive analytics for “predicting the present” is that analytical processes and tools can now sit where the data is. Predictive analytics can become part of daily recurring business processes for direct impact on how the business functions. The repercussions for real-time intelligence in terms of speed and relevancy are obvious. Commoditizing analytics also enables the running of a greater variety of scenarios and analyses to improve the quality of intelligence derived.

But – real-time decision-making also has to be vetted with domain knowledge, human experience and common sense, to validate the viability of analytics results. Decisions make a positive difference for the enterprise only if they are based on accurate intelligence. While many things are possible with predictive analytics, there is always the danger of trying to force ‘reality’ to fit the model. This can be deadly to real-time operational decision-making.

About Julie Hunt

Julie is an accomplished consultant and analyst for B2B software solutions, providing services to vendors to improve strategies for customers, target markets, solutions, vendor landscape, and future direction. For buyers of software, she helps companies make purchase decisions for software by working from a business-technology strategy. Julie has the unique perspective of a software industry “hybrid”: extensive experience in the technology, business, and customer-oriented aspects of creating, marketing and selling software. She has worked in the B2B software industry on the vendor side for more than 25 years in roles from the very technical (developer, SE, solutions consultant) to advisory roles for developing strategies for products, markets and customers, and go-to-market initiatives. Julie is an accomplished consultant and analyst for B2B software solutions, providing services to vendors to improve strategies for customers, target markets, solutions, vendor landscape, and future direction. For buyers of software, she helps companies make purchase decisions for software by working from a business-technology strategy. Julie has the unique perspective of a software industry “hybrid”: extensive experience in the technology, business, and customer-oriented aspects of creating, marketing and selling software. She has worked in the B2B software industry on the vendor side for more than 25 years in roles from the very technical (developer, SE, solutions consultant) to advisory roles for developing strategies for products, markets and customers, and go-to-market initiatives.

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