Why is there a bias against using data? And……What exactly is data? Is it a number, a unit, a quantity, a quality, a description, or all of the above? When discussing the relevance of data the term is often interpreted (and often used as well) as to refer to numbers only. But is this the correct way to look at data? Or does it depend on the question being asked?
Data has always been used when making decisions. Through heuristics, gut impressions, and best-guesses data is crucial to make decisions. If this sentence does not quite resonant with you let’s try using a different word:
“Information has always been used when making decisions. Through heuristics, gut impressions, and best-guesses information is crucial to make decisions.”
Some readers may be comfortable with both terms and sentences, whereas others (and probably a majority of the group) are more comfortable with the use of the term ‘information’. The use of the term ‘data’ often shuts down conversations as we are often inclined to translate the request for ‘more data’ as non-relational, mechanical, and requiring a plethora of numbers, spreadsheets, statistical tools, and overtly rational biases to produce & use. Although this may be surprising to some, data does not require any of these to use it.
The word data is often used to reflect the existence (or need) of relevant information that can be used to evaluate performance systems and help make decisions. When this ‘information’ is not available decisions are at risk of missing the ‘larger picture’ and failing to meet objectives.
The following graphic depicts how data is already being used in most business’ performance management systems:
Variation exists between industries, and types of businesses, regarding the scope and quality of their performance evaluation systems. Some industries prefer using more casual processes and mechanisms to evaluate performances whereas others are more interested in (and often pressured) answering more granular and detailed questions. The quality of the information available determines the precision in which the questions can be answered. This also impacts the precision & accuracy upon which future iterations, decisions, and improvements are generated. Some industries naturally account for these variations (of higher & lower qualities of information being available to the leadership teams) but they shouldn’t have to.
In industries that practice more casual methods of inference and decision making the use of heuristics and ‘rules of thumb’ are predominant. On the surface these methods seem superficial and to leverage different mechanisms of information sourcing and evaluation; but this isn’t necessarily true. Although the level of detail and precision may be less these use of heuristics and ‘rules of thumb’ also leverage data as well.
One of the biggest challenges above is that teams will frequently input different information (and interpretations) into the above boxes. Using a more formalized performance evaluation/management framework can not only improve the team’s alignment & performance but also expedite learning and insure the conflict that exists in healthy and productive. The following image depicts a generic performance evaluation/management framework:
Creating a performance evaluation/management framework supports employee evaluations, financial goals, operational alignment, identify human resource training areas to emphasize, and improves business-to-market fit. When information is defined, gathered, and evaluated appropriately both the business and the customer wins. When the quality of the data available is low the business’ competitors are given unlimited opportunities to expand their market share into areas previously occupied by your company. Don’t let this happen to your business.
How is your business using data to improve performance? What data does your business emphasize and find the most useful? Share your comments below.
Travis Barker, MPA GCPM
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