Predictive analytics (PA) uses historical data to predict future outcomes and how accurate and relevant that historical data will determine the validity of the outcome. It is safe to assume every business is technology driven now, and even if you don’t DIY, you have experts to rely on. With the help of these experts, predictive analytics has truly become an integral part of business decisions. While relying heavily on statistics sounds like a smart decision, it too can have its limitations.
The outcome will be as good as one’s input; if the input data is incomplete, irrelevant and unorganized, the outcome will be as good as none. Your data should be accurate and comprehensive; either use a larger set of data or subsets of different data sources. Your data sources also need not be equally accurate. For example, surveys will be less accurate as it is a form of self-reporting. Data sets filled by different agencies will also vary, and in order to model, you need to be consistent, which may skew your results.
Risk of decisions
Deciding to model isn’t the only decision a business has to make. Decisions like what variables to include, what data sets to include, to target which objective, to include extreme values or not, how to treat an irregularity or missing data, etc., are constantly arising in the modeling process. It can feel overwhelming even to an enthusiast, which is why most prefer to rely on a team of experts with knowledge in data science and an understanding of the industry.
While these risks exist, the benefits of predictive analytics are too significant to ignore. They:
- Drive down costs
- Identify new opportunities
- Improve operational efficiency
- Reduces customer churn
- Minimizes process interruptions
Every business faces uncertainty, and as we have recently witnessed, there are no foolproof plans. You can do everything correctly, but if the entire world is in lockdown, how do you adjust to it? A business constantly needs to adapt, learn new tricks and deploy them with efficacy in order to survive. This is why PA remains popular, and it reduces the risk of uncertainty.
Executives can try to minimize the limitations of data by
- Clearly defining parameters of the analysis
- Choosing a data set that accurately represents the entire population
- Estimate the scope of this data and include data from more than one source
- Make sure data from different sources is in the same format
- Missing values and extreme values should be looked out for
- Your data type should include both boundaries to identify variants that can skew your results.
This is not like one size fits all, it is based on data that your business has generated, and so it is unique to your business, and for that reason, the way you treat the predictions should be unique too. There exist certain limitations in business that only internal officers can identify, and they have to be considered when relying on models too.
Predictive analytics is a cycle, data access –> data exploration –> modeling –> implementation. While PA can tell you the factors to consider before making a decision, it can’t make your decision for you. It facilitates an informed decision, and that is as far as executives should rely on it. Suppose the decision is complex, or you’re facing the uncertainty that the model hasn’t been exposed to. In that case, you can choose the decisions you should make based on models, experience, industry practices and environmental changes.