Gartner defines a Citizen Data Scientist (CDS) as “a person who creates or generates models that leverage Predictive or Prescriptive Analytics but whose primary job function is outside of the field of statistics and analytics.” A CDS is different from a true Data Scientist in one crucial way; namely, they do not have the skills or training to be an analyst or a programmer but, with the right tools, they are capable of generating reports, analysing data and sharing data to make decisions.
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CDS represent a new breed of business user. These business users have adopted end to end Business Intelligence Platform with integrated AI to gather and analyse data from varied data sources and use that analysis to identify the root cause of problems, identify opportunities, solve problems and share crucial data to support business decisions. CDS create and generate data models and use sophisticated analytics that are supported by easy-to-use interactive BI dashboards. By definition, CDS are not Data Scientists, or IT staff. Rather, they hold varied positions within the business organisation/MBAs (in HR, Finance, Marketing etc.) and use data analysis to support decisions made within their business role, team or responsibility. CDS are support to come with reports that help their organisations grow and come with those magic insights that are key to the business.
Analytics providers are working hard to build out tools to support the citizen data scientist. Companies are now again moving back to end to end BI Platforms rather than just using a Predictive Workbench or a Visualisation tool.
They are creating data visualisation tools, where data streams can be added, connected, and analysed via drop and drag methods. These tools are powered by an underlying analytics engine, so it is easier for the citizen data scientist to create more SUCCESS using data, algorithms, models and Visualisations.
However, citizen data scientists can’t be relied upon to manage the end-to-end process of analytics development, training, and use of algorithms and models. They lack the advanced mathematical expertise to do so. There is still a need for data scientists, but not as many of them.
In next 5 years a company will need 10X number of Citizen Data Scientists if they need X number of Data Scientists
A company working to build advanced analytic capabilities also needs subject matter experts (SMEs) to provide industry/process-specific context for what the patterns identified by the algorithms and models actually mean, they would expect a Citizen data scientist to fill this role.
In the following Video I have explained a typical Citizen data scientist flow from scratch. This is a modern Analytics tool that provides everything to a CDS using Drag and Drop Methods.
Video Link : https://youtu.be/gnPQeFAgPKY
One can do Data Ingestion, Data Preparation, Data Transformation, Predictive and Prescriptive Analytics, Data Visualisation and Search in a single flow.
Even Engineering Colleges or MBA Colleges (where more Citizen data scientists will get trained) should train using a comprehensive tool to provide training to their Students so that they become more effective. Generally we have seen that MBA candidates even though they come from Computer Science or Engineering background are scared of writing basic SQL. They expect a Data Engineer, Data Scientist and a Data Visualisation expert to be given to them since all these 3 roles use different set of tools and data integration between them is utter complex. In the above flow, if all the tools of Data Prep, Data Transformation, Data Science, Data Visualisation and Search with Mobility are integrated then Citizen Data Scientist can do most of the work themselves. This can save at least 50% cost on deployments.
We suggest Colleges and institutes to use such tools to train people end to end BI. It is the high time when we re-skill our people. Most of the resource based Analytics programmes will find that they are spending lot of money without getting right output. I can see this problem more so in India where management will loose patience over their CIOs & Analytics teams as resource cost is very high and they are spending lot of time on basic things that a smart integrated tool can do it easily. An end to end platform will just cost 20% of overall BI implementation and will effectively do 50% job. While the customers team will cost 80% and will be effectively do 50% job. Lot of companies what’s to do 100% of their BI Analytics without any tool and i see them in same state for years. People will keep on coming an going without giving the desired output to the businesses leaving a doubt in the mind of management whether Analytics works or not!