When do companies usually come-up with their new Key Performance Indicators (KPI‘s)?
To answer this question, we may need to ask ourselves: when does it make sense to change the way we measure the Business outcome?
Here are three common reasons:
- because of change in strategy
- because of change in management
- because of change in Business
And there may be a lot more, but no matter what the reason, new KPI’s tend to pop up at the end of the year. Why so?
Yes, we may end up deciding on a new ways to work, new strategy, or take over a new line of Business any time of the year, but invariably, the new KPI’s will come when we take the time to think ‘m through, end of year. Often in combination with next year’s budget.
In order to be effective, new KPI’s will likely need to get tied in to a bonus or commission system. They will serve to keep track of individual or business unit performance.
So, to some extent, people’s performance, happiness (and salaries) may depend on them. This will need to happen at the very start of the new year, so just a few days or weeks later.
- So when do we want to be able to get feedback on these new KPI anchor points?
- When do we want to know if we, as individuals, are doing the things expected of us?
- When do we want to be able to adapt activities of the Business Unit, in line with the overall desire of senior management?
Well, as quickly as possible of course.
This is where we can get confronted with Data reality. It often takes 6 months or longer until the BI team can manually connect data sets, ERP systems and Net Promoter scores into the Enterprise Data Warehouse and to our new set of KPI’s. The BI team nurtures a Data Warehouse because it allows them to capture change in data sources, connect multiple data sources to the reports, handle security and maintain the data systems over time. A Data Warehouse allows you to track historical changes, and provide superior data quality. And it just takes a lot of time to maintain such a system by hand coding.
That also means we will be navigating blind for the better part of the fiscal year. By the time we figure how to connect results to data to KPI to salary and happiness,
we may find ourselves to be late in the fiscal to correct.
Invariably, we will find that some of these new (now 6 month old) KPI’s are too hard to measure, or are not able to tie into results. We could try and come up with new and better ones at this point, but that would likely take the rest of the fiscal year to implement. Basically, this cycle of measuring and tuning forces us to work blind, depend on gut instinct or risk steering the whole of the organisation in the wrong direction for half of the trajectory. And by the time we are done, we will be ready to come up with new KPI’s for the next fiscal period.
There must be a better way to approach this. What will often occur, is that Business Units will come up with silo KPI measurements. Some Excel-sharp Business user will manually create a system which will provide ad-hoc statistics. This of course beats navigating blind. Unfortunately, this will not apply for all Business Units. The Excelperson becomes the bottleneck. This might, over time, give birth to a Data-Oligarchy within the organization, and encourage political preferences. The Business Unit with the better data will likely gain power. To the detriment of the rest of the company.
Another apporach is to try and implement a Business facing metrics system. Often accompanied by Data Discovery tools like QlikView, Tableau or the newest version of Excel PowerPivot & PowerQuery. Here we will need to roll-out a quick Data project, invest in software and training and try to get results faster. Invariably, we will. In this scenario, we will likely be up and running within one or two months. The main problem with this approach is that none of these tools are well equipped to handle the over-time change thereafter.
If something changes after the initial project, change of ERP source data, another change of KPI’s, change of requirements, they will have a tough time following through and time to market may explode.
And we know that change will occur, even accumulate over time.
The problem with each of these approaches is that they handle the matter as a project: you come up with KPI’s, you create something to measure those. Of course we all know that new KPI’s come often en regularly in the KPI season. Hence, this should not be regarded as a project, but as a process. We need to be able to incorporate new KPI’s as soon as management decides to roll those out. At the same time we need to fight the silo mentality of the Business Units and provide a good overall picture throughout the whole of the organization. So we do need a Data Warehouse.
Data Warehouse Automation
We do need a Data Warehouse. Only one that is capable of better handling change over time. We need to be able to cut back response time to days or weeks, not months. We need Data Discovery capabilities. We may even want to make use of some of the modern and mobile new Data Discovery tools out there. But at the same time we need to ensure that people are playing around with correct quality data. What we need, is governed data discovery instead.
The need to work with new KPI’s is a given – a side-effect of our need to change the way we do business and adapt to new business realities.
Our capability to automate the process will be key to success.