When data from many situations has common trends that end up leading to similar outcomes, the consistent pattern provides strong evidence for future results under similar conditions and trends.Never have we heard more about decision-making “being guided by the science”. That is, not only should guesses be made about the likelihood of future outcomes based on present trends, but also those predictions should be verified by actual examples from similar situations in the past-as much as possible, at least.
The practice of predictive analytics should also be subjected to the discipline of evidence-based principles. This work of projecting future trends is known as predictive analytics, and although it still obviously remains only a best guess about the future, it is grounded in objective facts and trends and can provide a greater degree of likelihood as a result. What if an understanding of past events and trends could be used to predict the most likely outcomes of future data sets and events? If current trends are identified and projected to continue in the future, decision makers will have access to rich insight that can aid their cause. There is another step beyond the basic analytical goal of explaining what the data reveals, though. However, the same technological power that has made the collecting of such data sets possible has also been harnessed to aid in analyzing that data. Turning almost endless matrixes of numerical data into sensible patterns of interrelated and explainable trends can be a daunting task. Though this sounds simple enough, the vastness of the data sets makes it rather a tall order to accomplish. The analyst attempts to explain what the data reveals about the events that have occurred, the relationships between different events and market forces, and why the numbers are what they are. The focus of this type of analytics is simply to understand and describe what has taken place as revealed by data sets. The most basic type of data analytics is known as descriptive analytics. Today’s massive sets of data are commonly referred to as “big data.” By relying on actual evidence of this sort, much of the uncertainty about treatment practices can be removed. The question becomes that of which treatment has been shown to be most effective in actual practice. Given the same set of conditions, one doctor might prescribe one treatment whereas another doctor might prefer another. Medical professionals work with much scientific and objective data about the health conditions of their patients, but many professionals believe that many medical practices have too long been subjective in nature. The medical field provides an example of an area where evidence-based decision making is clearly valuable. There is today an increased focus on scientific experimentation-or at least as close to scientific as circumstances will allow-to test theories and provide evidence about the effectiveness of different approaches to problems and different business strategies. This calls for a different type of data collection and analysis. An evidence-based approach asks a key question: has such a course of action been proven to be effective for others in similar situations? The decision maker believes the course of action should resolve a particular problem and lead to a desirable outcome. Suppose an analysis of data and trends leads a decision maker to propose a potential course of action. The emphasis of evidence-based thinking is relying on actual experimentation to demonstrate that a plan does indeed provide a likelihood of success.
This is where the idea of “evidence-based” decision making becomes central. Even if the data itself is reliable, how that data is used remains a key consideration. Having looked at objective data, it is still far too easy and common to posit unproven theories to explain the data, identify causes, and predict future outcomes. However, there is a way to do this that still leaves far too much room for error. Using objective facts as the supporting basis for decisions seems like a sensible approach. Explain the uses of descriptive and predictive analytics.Explain evidence-based decision making.
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