Without data, the say goes, you are just someone else with an opinion. No need to debate issues if the data to support a line of argument cannot be gathered. Nowadays, decision-supporting data is collected to avoid byzantine discussions, and reinforce the organisation in its belief that it took the right course of action. But how reliable and granular is this data? Is your market segmentation too simplistic? Are your BOMs reflecting the complexity of your production? Is your capability matrix truly depicting in-house skills? Few would be able to make a call on these questions.
The hidden cost of decisions taken from flawed data can be substantial, and the assumptions behind these decisions rarely challenged. Believing that a business can enjoy infinite capacity or unbridled expansion could seem odd, but it is not infrequent. When assumptions are not properly calibrated with data, the blind can lead the blind very blissfully. In many cases, the inaccuracy of data is compensated by knowledgeable staff, who will adjust micro-decisions, unnoticed. When these staff move on, the organisation starts flying on auto-pilot.
In fact, few organisations should be surprised to have a weak data management approach. Tidying data sets is either left to all, as part of their corporate citizenship, or to an underfunded and under-qualified few faced with an impossible task. To compound issues, data will often be replicated in different parts of the organisation, with little awareness about integrity issues. One would think that the proliferation of data sources could help, when the real issue is to maintain a high quality of the data sources. Many consulting projects have proved their worth by simply pulling together information that was never sanitised and crystallised in such a way.
All this points to something wider than hygiene. As data mastery is a survival requirement, it is surprising that so few organisations are ready to invest accordingly. Establishing a clear hierarchy of data, appointing responsibilities to alter them, and putting in place audit trails for their accuracy and integrity looks like Ground Zero. And it could go as far as setting a master data cell reporting directly to the management team, looking after self-healing mechanisms to maintain clean data sets.
So, when you need to take a decision, big or small, it might be worth asking yourself the following questions:
The hidden cost of decisions taken from flawed data can be substantial, and the assumptions behind these decisions rarely challenged. Believing that a business can enjoy infinite capacity or unbridled expansion could seem odd, but it is not infrequent. When assumptions are not properly calibrated with data, the blind can lead the blind very blissfully. In many cases, the inaccuracy of data is compensated by knowledgeable staff, who will adjust micro-decisions, unnoticed. When these staff move on, the organisation starts flying on auto-pilot.
In fact, few organisations should be surprised to have a weak data management approach. Tidying data sets is either left to all, as part of their corporate citizenship, or to an underfunded and under-qualified few faced with an impossible task. To compound issues, data will often be replicated in different parts of the organisation, with little awareness about integrity issues. One would think that the proliferation of data sources could help, when the real issue is to maintain a high quality of the data sources. Many consulting projects have proved their worth by simply pulling together information that was never sanitised and crystallised in such a way.
All this points to something wider than hygiene. As data mastery is a survival requirement, it is surprising that so few organisations are ready to invest accordingly. Establishing a clear hierarchy of data, appointing responsibilities to alter them, and putting in place audit trails for their accuracy and integrity looks like Ground Zero. And it could go as far as setting a master data cell reporting directly to the management team, looking after self-healing mechanisms to maintain clean data sets.
So, when you need to take a decision, big or small, it might be worth asking yourself the following questions:
- Is the data supporting your decision in any way surprising? Does it differ from your perception of the business?
- Are you tempted to withhold decisions because you do not trust the underlying data?
- Who is in charge of this data? How confident are you that it is well looked after?
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