One thing is for sure, when it comes to scalability, less is always more. Doing less, but doing it right is what really improve the scale of your business.
In the world of performance analytics, scalability isn’t just about improving the speed of calculation, it also means ensuring the accuracy and availability of results and their subsequent consumption, the ability to handle large volumes, and last but not least, it is about synergy with the business growth strategy.
However, although speed, volume and timely delivery are important, data integration and data accuracy of the end-results are critical to achieve scale because redundant and time-consuming data corrections are one of the main factors affecting scalability.
Too often analysts are forced to spend an inordinate amount of time with manual exceptions, where the corrections result in reprocessing the data and starting the process from the beginning again. Some teams have dealt with this by building custom processes within their performance application which often results in slowdowns and bottlenecks.
The need for process customization is introduced by the lack of upstream processes and the subsequent need for accuracy and consistency. However, there are consequences when a number of customizations are added to address operational inefficiencies.
In my experience, customized processes are not meant to be part of the performance production process as it will undoubtedly affect the speed and availability of the final results.
This, however, does not mean that customization is out of reach, or that there has to be a trade-off with scalability.
With this approach, the impact on scalability is minimized and the processes reflect the business needs so the performance production is free from any redundant or cumbersome operations that are not needed and create slowdowns.
Performance analysts ultimately spend less time manually ensuring that the data and returns are validated thoroughly and can focus on providing more value-add, analysing the numbers and delivering much needed insight into the investment process.