Improvements in IT data quality and analysis tools have enabled IT management to spend less time looking into the past and more time enabling the dynamic enterprise of the future. This allows them to anticipate business events more accurately, forecast costs and capacity, and identify operational risks before they appear. Empowered by technology-driven insights and technology-enabled prediction ability, IT leaders have secured a long-sought seat at the table with their business counterparts during the strategic planning process. IT management becoming more predictive is good. Right? Perhaps, but there are some risks to consider.
Technology-enabled prediction is only as good as the underlying data, and does a poor job of addressing unknown variables. Human intuition and analysis skills have traditionally been used to fill gaps in available data, interpret meaning and project future events. The predictive abilities of most IT leaders are heavily dependent on the quality of information and technology-enabled processing power at their disposal. Modern machine learning systems have made tremendous strides in analyzing large volumes of data to identify trends and patterns based on past and current observations. Their capability to do so is limited, however, by the quality and dependability of data inputs. “Garbage in-garbage out” has been the rule for many years. Recently, advances in data validation and correlation tools have improved this situation somewhat and enable nuggets of goodness to be derived from what was once considered garbage. By filling integration gaps across data sources, resolving conflicts across data sets and validating data for quality/consistency, technology can now come very close to replicating what humans were able to do previously.
Another caveat is that too much granularity can lead to a false sense of confidence. “The weather app on a smartphone reports there is a 13% chance of rain next Tuesday afternoon, starting at 1 pm.” Accuracy aside, what does that number even mean? Technology enables prediction systems to use complex mathematical algorithms to combine sets of data and assess the probability of various outcomes. The results they generate may have the appearance of a high degree of accuracy, but it is always good to apply the principle, “if it looks too good to be true, it probably is.” A test of common sense and reasonableness should always validate technology-enabled predictions to avoid developing a false sense of confidence.
What if the predictions are wrong? Is accountability still in the right place? Business decisions are usually very important, and the impact of bad decisions on the organization can be catastrophic. Each individual within an organization has his/her own charter, objectives and motives, driving his or her focus and behavior. Business leaders are responsible for guiding successful strategy and operations while (in most organizations) IT leaders have a charter more narrowly focused on managing and stewarding information and technology assets. Having IT leaders contribute their skills and capabilities to strategy is an excellent use of their talents, but it is important to make sure the right business leaders remain accountable if things go awry.
The biggest long-term risk associated with developing a reliance on technology-enabled prediction is that business leaders lose their ability to evaluate the business environment and make decisions without technology’s assistance. Analysis, inference and prediction skills must be continually exercised, or they will atrophy and be lost over time. If this happens, then the organization loses the checks and balances to ensure the information generated from IT is correct.
Learning how to harness the power of technology and information and applying it to create valuable predictive insights for an organizations is definitely good; IT leaders should be commended for bringing new capabilities to the decision-making table. As we all know, however, no information is perfect, and technology has its limitations. Becoming entirely reliant on technology for prediction and losing the ability to apply a human filter is a risky situation for businesses. As with many business decisions, it is important to balance the potential benefits with the acceptable risk profile for your organization.
The application of predictive analytics to IT and OT (Operational Technology) systems has tremendous promise, both for IT and the enterprises they support. A 451 Research-authored report titled, “The Role of DQM in Machine Learning and Predictive Analysis,” sponsored by Blazent presents the current use of advanced analytics in enterprises. Download it here.