This post is my way of elaborating my ‘aha’ moment as someone who deals in data on a daily basis.
Both data creators and data analyzers in most organizations are not the same person/profile. Those who are data creators have different needs and create ‘data’ that helps them in getting their work done efficiently.
However, data analyzers though they may understand the data creators process are not necessarily analyzing for factors of efficiency when analyzing data. They may be more interested in the data for how it can be analyzed to bring about effectiveness. This tension between creation from a efficiency point of view and analysis for assessing effectiveness exists in most organizations and ways of managing is defined by the organizational culture.
The incentives for data creators may derive legitimacy from a culture of delivering with minimal transaction costs of time, resources and effort. Data analyzers have their ‘…own norms and standards for the imagination of data’ (Lisa Gitelman, 2011). Interpretation is the heart and soul of data analysis. Be it at the design stage for those creating data or at the analysis stage those who look for patterns and connections.
As long as both who deal in data understand this important aspect, neither will claim their work as the business of facts but rather of interpretation.