You will notice some common themes in the nine IBM journeys described in this book.
Relationships inferred from data today may not be present in data collected tomorrow. The relationships that you infer from data about the past do not necessarily hold in data that you collect tomorrow. You cannot analyze data once and then make decisions forever based on old analysis. It’s important to continually analyze data to verify that previously detected relationships are still valid and to discover new ones. Fortunately, major discontinuities with data do not happen very often, so change generally happens gradually. Social media sentiment, however, has a much shorter half-life than most data. Using relationships derived from past data has been repeatedly demonstrated to work better than assuming that no relationships exist. The relationships that have been detected are likely correlation rather than causality. However, these relationships, if detected and acted upon quickly, may provide at least a temporary business advantage.
You don’t have to understand analytics technology to derive value from it. For a long time, many business leaders expressed the opinion that mathematics should be used by only those who understood the details of the computations. However, in recent years this view has been changing, and analytics is being treated like other technologies. You must learn how to use it effectively, but it is not necessary to understand the inner workings in order to apply analytics to business decisions. You have to apply analytics methods in the context of the problem that is being solved and make the results accessible to the end user. But just as the user of a car navigation system does not need to understand the details of the routing algorithm, the end user of analytics does not have to understand the details of the math. Typically, making the results accessible to the end user involves wrapping the math in the language and the process of the end user. Also, the analytics can be embedded deep inside things so that the user does not see it, like in supply chain operations. Analytics should be usable by anyone, not just those with PhDs in statistics or operations research. Some users will want to understand the algorithms and inner workings of an analytics model in order to trust the results prior to adoption, but they are the exception. Chapter 4, “Anticipating the Financial Future,” illustrates such a case.
Fast, cheap processors and cheap storage make analysis on big data possible. Moore’s law has resulted in vast increases in computing power and vast decreases in the cost of storing and accessing data. With readily available and inexpensive computing, we can do what-if calculations often and test a number of variables in big data for correlation.
Doing things fast is almost always better than doing things perfectly. Often inexact but fast approaches produce enormous gains because they result in better choices than humans would have made without the use of analytics. Over time, the approximate analytics methods can be refined and improved to achieve additional gains. However, for many business processes, there is eventually a point of diminishing returns: The calculations may become more detailed and precise, but the end results are no more accurate or valuable.
Using analytics leads to better auditability and accountability. With the use of analytics, the decision-making process becomes more structured and repeatable, and a decision becomes less dependent on the individual making the decision. When you change which people are in various positions, things still happen in the same way. You can often go back and find out what analysis was used and why a decision was made.