Data Driven: Making Sense of It All
Step 2: Properly Absorb and Understand the Data
Interpreting data can be like reading tea leaves. Common tips for a proper understanding include:
• Scan all of the data in advance of making any judgment or analysis. Allow the cognitive portion of your brain to absorb what you are looking at.
• Know your data source and its pros and cons. This includes where it comes from, what systems create it and what time period it covers. In written presentations of data, always read the footnotes and fine print.
• Nomenclature is critical. Fully understand how data sources or collectors define key terms. Because of a lack of commonly used industry standards, terms such as "gross sales" or even "Web visits" can mean very different things to different people, organizations and systems.
• Realize that not all data are created equal. Some data are more important to your business or your efforts than others. Balance the quantity of data points with the quality of the data and its ability to help tell a story, support a point of view and truly track performance.
Step 3: Derive Meaning and Form a Hypothesis
This is where things get tricky and ultimately where the value of data and analysis come into play. Logic is the overriding mantra for good, solid data analysis. Logic can guide an ability to see patterns and trends in the data, or even identify relationships between various data points. Cause/effect relationships from seemingly independent data points can be a valuable observation to help form a hypothesis.
Whenever possible, validate key data points against other data points. This triangulation can often minimize any inherent risk in your analysis or uncertainty of a hypothesis. And keep in mind; the use of any single data point to derive intelligence or meaning can be risky.