Data Scientist (n): A machine for turning data you don’t have into infographics you don’t care about.
I would like to examine a process people often follow when they deal with data.
The intelligence is not used for action.
We act when we believe the action will result in a desirable outcome. We never know for sure, but we estimate probability based on our experiences in similar circumstances. These dynamics influence how we select, search and interpret the data into intelligence, or lack of thereof. Subconsciously we select data that would likely provide confirmation of our existing beliefs. This usually means that we heavily rely on internally generated (controlled) data and heavily discount externally generated data.
We like to use such terms as unbiased and objective, but the very process of selecting a data set introduces bias and subjectivity. It is unavoidable. It is a much better practice to embrace and understand a bias that is pragmatic, and define a purpose of an inquiry. You don’t see people mining a mountain to find “whatever” is there. They carefully select and test an area for an indication of high concentration of desired mineral before the exploration and mining start.
If the purpose of your inquiry is improvement of customer experience, assemble a data set from the most relevant internal and external data sources available. If you limit your data set to a company controlled data, you introduce a company bias. In such a case the likelihood of discovering any new intelligence for improving your customers experience is quite low. Forget about data mining and just continue your archaic surveying exercises of “guess and validate”. If you include data generated by customers without solicitation and control, you will introduce customer bias. Introduction of channel generated return data and customer service data will allow for balancing of the biases. Correlation of trends in controlled and external data sources will help to discover potential gaps between your beliefs and emerging evidence. However, even the best evidence cannot automatically make people abandon their beliefs and start acting differently, but that is a subject of another article.
The point is – data cannot lie to us; we have to do it ourselves by not mining it honestly and competently.
This entry was posted in Customer Experience CX, Social Media Research and tagged Customer Experience, Voice of Customer. Bookmark the permalink.
Not a lie but important nonetheless.
Advocates of Big Data (including me) need to address the concerns of many regarding the perceived abuse of Big Data. We spend a lot of time talking about what we can do with data, while the public, in general, increasingly including legislators, see it ranging from benign invasion of piracy all the way to identity theft and worse.
Truth be told it sometimes is that, but nowhere near always. And it sometimes is and could more often be an incredibly beneficial economic growth engine. But not until misconceptions are corrected. Until that happens false perceptions of reality will continue to trump reality.
Big Data needs a PR makeover.
Data doesn’t lie, but it might not tell the truth if you don’t know how to ask the right question, or how to ask a question in the right way. Proper data analysis requires some understanding of statistics, causation and correlation. This is why the scientific method was invented. Interpreting data is difficult and many businesses struggle with it.