Oz has dedicated this episode to explaining the importance of ‘understanding data’. It is different from ‘doing data’ or ‘following instructions’.
Following instructions is a mindless task. And often, in face of exceptions, it is improbable the set of instructions would cover them.
Doing data is about truly being engaged with data and with the tools that help with cleanse and analyze it. This involves understanding data generation processes, e.g. knowing where are the weak spots in them and how the analysis would need to be adjusted.
Understanding data falls in the middle. It revolves around an understanding of the business and its processes. People who understand data may not know about pivot tables or database queries, but they would know about the reports available. They would know about the business rules and how everything fits together. Most importantly, they would know what report can be trusted for what data.
When Oz works for an organization, he goes as an outsider to develop solutions. Partnership with someone inside the organization who understands data helps get work done much more efficiently. Someone who understands data can anticipate the questions Oz might ask and would be able to warn about the credibility of certain sources.
This means that Oz would not have to analyze the processes end-to-end himself to understand data quality. This also means the solutions developed need not be complex.
Even if a company subscribes to a third party to provide some reports, these reports tend of focus on very specific aspects of the business. Then, when the company want something else, these third parties either can’t do it or will charge an exorbitant sum for it. But, if the company has people who understand data, then all they need is some data analysts to turn these already existing reports into a solution.
Today, everything is data: sales, website visits, inventory levels, staffing. And playing with data is like playing with fire. Someone who understands data can help control this fire.
Share write your thoughts with us in the comments section below. And keep that data clean!
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