Challenge the Relevance of Operational Data Tracking

Dashboard approach to displaying data

Dashboard approach to displaying data

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How do we know if the operational data we are tracking on a day to day basis is relevant? It is commonplace that data is being generated unnecessarily, that the wrong data is being generated and the actions being taken are based upon the wrong information. The most susceptible area to data mismanagement is at an operational level – for example SPC data on a manufacturing line.

How then do we ensure that the data is correct and relevant?

Dashboard for Performance: A top level down approach to Data

Top level business metrics have been the mainstay of judging the performance of an organisation for hundreds of years. Top level metrics outline the reason for a company being in existence in the first instance.

A business or organisation can thrive or survive on an adherence to a surprisingly small number of metrics. Typically this would be something like profitability metrics.

Good businesses concentrate only on this small number of key metrics to focus the attention of an entire organisation with simple objectives. Focusing on this small number of metrics can then assist in driving the behaviour of the larger business to move in a single unified direction and give the greatest chance of success. This is often known as a ‘dashboard’ for performance. So called because just as in a vehicle it’s the salient information that informs the driver as to the status of the vehicle and enables the driver to take appropriate action if necessary.

The FTSE or NASDAQ are good examples of key metrics being available and useful and follow a dashboard approach in displaying data, it is also useful for making decisions. The data displayed in this format is top level and concise. Yes, far more data and information is available but the key ‘dashboard’ information is used as the ‘barometer’ of a company’s performance and is presented succinctly. This top level data is the principle focus for business leaders and analysts in deciding what course of action to take next.

 

Data that supports the Dashboard: Data from the Bottom up

At the next tier down, it then follows that key parts of a large organisation should also have a similar focus on generating, tracking and using data, but; only if it has a significant effect on the business data in the next level up i.e. the top level business metrics recorded in the Dashboard.

The cascade continues downward from the overall business at the top of the pyramid, to the day to day performance of an individual line at the bottom. A common thread remains – all data and information remains connected to that of the company’s original reason for being in business. At the operational level this could be a manufacturing line, a sales team, a marketing strategy etc. The bottom of the pyramid also has the greatest potential for the largest amount of data generation, it also has the most potential to be removed from the Top level Business KPI’s.

We live in the age of technology, the ability to generate and track data in huge quantities is something that was not available to our forefathers only a generation ago. The ease in which we can generate data does not come without risk. Phrases such as ‘Paralysis by Analysis’ have not come into common currency without due cause. Data at any level must be relevant to the business or it is a waste of effort and resource to; generate, analyse, or, worst still, irrelevant data can drive behaviours that are incongruous with the top level business metrics.

SPC data fits directly into the Operational data portion of the triangle. An example of SPC data on a manufacturing line needs to fit with the business overall strategy.

Challenge its relevance with questions such as:

  • Who will see the data?
    • Is it more than one person?
    • Is it simple to interpret and act upon?
    • Is it a manager role who will see it and take action?
    • Is it reviewed regularly to change a behaviour? E.g. Scrap data being used to change a procedure or trigger a quality initiative on the line.
  • What will the data be used for?
    • Will it be used to affect a change that will effect metric in the next tier up of the data triangle?

 

Challenging SPC data for its usefulness and whether it fits with the purposes of the organisation is at the core of deciding whether it has a relevant use.

If the data is relevant then it is simply a cost benefit decision based upon, what does it cost, versus what benefit does it deliver that will impact upon the next tiers metrics and therefore benefit the whole business.

 

NASDAQ studio” by Luis Villa del Campo from Madrid, Spain – Times Square – NASDAQ. Licensed under CC BY 2.0 via Wikimedia Commons.

 

 

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