Marketing Citizen Data Scientist
When things are new they are hard to use and require specialized knowledge to operate. Over time, as common user errors are seen, safeguards and usability gets added, and usage becomes democratized.
It wasn’t that long ago that website could only be built by hand-coding HTML. The first tools were geared to help programmers to speed up their workflow. Today we have tools for non-technical end-users truly democratizing access to building websites. These users are not burdened by the syntax of the code. Instead, they could instead spend that energy purely on the visitor experience and we have an explosion of creativity all over the web.
Time to results is key
We are better off with technology in the hands of the marketing user and we use specialized technical resources to make customizations that need advanced skills. Time to results is key and this model of democratized access enables Citizen Data scientists, marketing users that learn enough data science to perform basic data science tasks without a deep understanding of the math used vs trying to align team projects to get time with the Data Science team periodically. This way you may not get all of the value but your time to value is measured in days.
scarcity of data scientist
Another driving factor is the scarcity of data scientists and data engineers. While more data scientists are being trained every year. The need for their skills continues to grow: https://searchbusinessanalytics.techtarget.com/feature/Data-scientist-shortage-leaves-organizations-uncertain
We can’t wait for a few to be competitive… we have to spread the work across more enabled people.
problems are best solved by the subject matter experts.
There is an added benefit of involving the subject matter experts (SMEs). The constraints (logical and data) are best understood by the subject matter experts… and thus the problems are best solved by the SMEs. Data scientists are experts in data science, not the business and there is an inevitable iterative approach to solving the problem as SMEs look at the analysis and recognize limitations in the data and process.
Few companies have the luxury of having an army of data scientists ready to work on your projects. This framework focuses on getting more people involved and if there are data scientists available leveraging their limited time to solving specialized problems.
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