We have established structures around how data is managed and used via rules and processes around a variety of data related operations and decisions. Our data governance entails establishing & maintaining standards around data, establishing accountability for data, managing & communicating data development, and providing information about the data environment.
We have four levels of standards related to data privacy: LEGAL STANDARDS which are established by law, order, or rule to compel treatment of certain classes of data; ETHICAL STANDARDS established by industry or professional organizations which see to achieve some level of non-legally binding treatment of information; POLICY STANDARDS, which are our clients and our own internal standards established to guide our treatment of data; and GOOD JUDGMENT, even if some actions are not prohibited by any legal, ethical, or policy standards.
We define good data through the following characteristics: COMPLETENESS, a measure of whether or not we have all the data we expect to have; ACCURACY, a measure of whether the data we have is an accurate representation of the idea it’s trying to capture; CONSISTENCY, which is about capturing the same data the same way every time; TIMELINESS, which speaks to whether data is captured or made available soon enough after a real world event for it to be useful; and PROVENANCE, which is the degree to which we have visibility into the origins of the data.
Confirmation bias is the tendency to favor information that confirms one’s beliefs or hypotheses. There are really two ways that we can exhibit this bias. The first is by selectively gathering information that is we only seek out data that would serve to support a hypothesis and fail to seek out data that might disprove the hypothesis. The second is by selectively interpreting information. This happens when we only focus on data that supports our hypothesis even when we have data that refutes it. As data analysts, we try our best to remain objective and avoid both of these traps.
The framing effect is the tendency to draw different conclusions from information based on how it’s presented. It turns out that framing things in a positive way can elicit different results than in framing them in a more negative way. As data analysts, we keep this in mind both as we look at options and make our own conclusions, as well as how we present options and recommend results to decision makers.
Availability or vividness bias is the tendency to believe recent or vivid events are more likely to occur. There are all sorts of common examples of this, like how people perceive the risk of a plane crash or getting attacked by a shark. Both events are exceedingly rare, but are often perceived to happen more frequently than they actually do. It turns out that some biases are hard to overcome. The good news is, we’re actually able to use data to show the truth and prevent unnecessary actions that wouldn’t have had any impact.
Anchoring is our tendency to focus or rely too heavily on the first piece of information that’s available to us. This bias is regularly exploited in areas like pricing and negotiation. As analysts, we always question our analyses. But it’s also important to question why we’re questioning our analysis and make sure we’re asking ourselves the right questions.
Fundamental attribution error impacts how we interpret things that we observe people doing. Specifically we have a tendency to focus on attributes or intentions of the person themselves versus the situation or the environment when explaining a person’s behavior. Where this comes into play in analytics is often in the process of translating analytical observations into strategies and tactics for action. If we misinterpret why and how customers behave, we may end up taking action that is ineffective or counterproductive. At Analytic Scientist, we give people the benefit of the doubt and the focus on the structures we create for ...