Tony Houghton Business
Much is being written today about big data. Big data has been defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, vali- dation, storage, search, sharing, analysis, and visualization. What is needed is to shift the discussion from big data to big value. Business analytics and its amplifier, predictive business analytics, serve as a means to an end, and that end is faster, smarter decisions. Many may assume that this implies executive decisions, but the higher value for and benefit from applying analytics is arguably for daily operational decisions. There are several reasons that operational decisions are arguably most important for embracing analytics. First, executing the executive team’s strategy is not accomplished solely with strategy maps and their resulting key performance indicators (KPIs) in a performance score- card and dashboards. The daily decisions are what actually move the dials. Next, although much is now written about enterprise risk man- agement, the reality is that an organization’s exposure to risk does not come in big chunks. Enterprise risk management deals more with reporting. Risk is incurred one event or transaction at a time. In the sales and marketing functions, operational decisions maxi- mize customer value much more than do policies. For example, what should a frontline customer-facing worker Much is being written today about big data. Big data has been defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, vali- dation, storage, search, sharing, analysis, and visualization. What is needed is to shift the discussion from big data to big value. Business analytics and its amplifier, predictive business analytics, serve as a means to an end, and that end is faster, smarter decisions. Many may assume that this implies executive decisions, but the higher value for and benefit from applying analytics is arguably for daily operational decisions. There are several reasons that operational decisions are arguably most important for embracing analytics. First, executing the executive team’s strategy is not accomplished solely with strategy maps and their resulting key performance indicators (KPIs) in a performance score- card and dashboards. The daily decisions are what actually move the dials. Next, although much is now written about enterprise risk man- agement, the reality is that an organization’s exposure to risk does not come in big chunks. Enterprise risk management deals more with reporting. Risk is incurred one event or transaction at a time. In the sales and marketing functions, operational decisions maxi- mize customer value much more than do policies. For example, what should a frontline customer-facing worker do or say to a customer to gain profit lift? Operational decisions scale from the bottom up, and in the aggregate they can collectively exceed the impact of a few strategic decisions. Today many businesspeople do not really know what predictive modeling, forecasting, design of experiments, and mathematical opti- mization mean or do, but over the next 10 years, use of these powerful techniques will become mainstream, just as financial analysis and computers have, if businesses want to thrive in a highly competi- tive and regulated marketplace. Executives, managers, and employee teams who do not understand, interpret, and leverage these assets will be challenged to survive. When we look at what kids are learning in school, then that is certainly true. We were all taught mean, mode, range, and probability theory in our first-year university statistical analytics course. Today children have already learned these in the third grade! They are taught these methods in a very practical way. If you had x dimes, y quarters, and z nickels in your pocket, what is the chance of you pulling a dime from your pocket? Learning about range, mode, median, interpola- tion, and extrapolation follow in short succession. We are already see- ing the impact of this with Gen Y/Echo Boomers who are getting ready to enter the workforce—they are used to having easy access to infor- mation and are highly self-sufficient in understanding its utility. The next generation after that will not have any fear of analytics or look toward an expert to do the math. There is always risk when decisions are made based on intui- tion, gut feel, flawed and misleading data, or politics.do or say to a customer to gain profit lift? Operational decisions scale from the bottom up, and in the aggregate they can collectively exceed the impact of a few strategic decisions. Today many businesspeople do not really know what predictive modeling, forecasting, design of experiments, and mathematical opti- mization mean or do, but over the next 10 years, use of these powerful techniques will become mainstream, just as financial analysis and computers have, if businesses want to thrive in a highly competi- tive and regulated marketplace. Executives, managers, and employee teams who do not understand, interpret, and leverage these assets will be challenged to survive. When we look at what kids are learning in school, then that is certainly true. We were all taught mean, mode, range, and probability theory in our first-year university statistical analytics course. Today children have already learned these in the third grade! They are taught these methods in a very practical way. If you had x dimes, y quarters, and z nickels in your pocket, what is the chance of you pulling a dime from your pocket? Learning about range, mode, median, interpola- tion, and extrapolation follow in short succession. We are already see- ing the impact of this with Gen Y/Echo Boomers who are getting ready to enter the workforce—they are used to having easy access to infor- mation and are highly self-sufficient in understanding its utility. The next generation after that will not have any fear of analytics or look toward an expert to do the math. There is always risk when decisions are made based on intui- tion, gut feel, flawed and misleading data, or politics.
