1) Data Entry
Most standardised systems will struggle to cope with missing or incorrect data. Unless pre-specified many will just ignore missing data and assume all other data is correct. This is an important issue because it can bias your results leading to erroneous conclusions. Dealing with missing data is a common challenge in statistics and there are sophisticated methods available. However, choosing the correct approach depends upon the problem; it cannot be offered in a standardised platform. Beyond a statistical approach one could also look to enrich and validate the data by using external data sources, but this would also require a tailored solution.
2) Statistical Inference
In order to conduct statistical inference it is imperative that you have an understanding of the problem and the data. For example, if your sample data is not representative of the entire population or the target group, then you will have biased results. This can also occur if you only have a small sample set or one that does not include significant events. In this situation one may wish to use Bayesian statistics, which incorporate expert knowledge of the problem. Unfortunately, it is not possible to employ expert knowledge in a standardised approach. Another risk is when using Machine Learning for prediction. These methods are excellent at modelling what has happened but they are often very poor at predicting regime change. These problems can only really be solved on a case by case basis by statisticians and/or data scientists.
This leads back to the previous point that it is important to understand the problem, the data and the method of statistical inference. This is important as it restricts what questions you can ask of the data and under what conditions your inferences are valid. It prevents the business from acting on erroneous results. Again, this can only be solved on a case by case basis by statisticians and/or data scientists.
Data analytics can be applied to a wide range of business functions and if you wish to develop a data-driven organisation it is vital to do this and integrate your approach. However, it is unlikely that standardised platforms will have all the required functionality. To add functionality the best approach will be to select a platform that allows third-party add-ons. Unfortunately, this will require users to pay additional fees and the add-ons may still not be suitable/ideal.
5) Competitor differentiation
It is natural that the more common standardised platforms become the less opportunities there will be for competitor differentiation. Once the benefits from standardised techniques – of which there are many – are exhausted, businesses will have to start tailoring their systems to remain competitive.