In this article, we open up a hot discussion of the "rising trend of citizen data scientists".
Who are they?
Imagine the following situation as an example:
You want to analyze your data to sell more, who is more important for your company? an expert data scientist or a marketing expert?
I vote for an expert data scientist. He has enough technical knowledge, for example, to analyze the data of the company's leads and so guides you through the conversion funnel so that you can end up converting them into customers.
But Wait! Let's Be WISER
Can he also provide you with good insights on content of the website and social media? Does he know who your personaes are? or what kind of data is more relevant to the sales? Is he able to do optimization? I changed my mind. A marketing expert is also really important.
NO, Let's Be Even More YZR
What if you have both qualities in one person? The person is called a " Citizen Data Scientist". He does advanced data analysis, but his primary functions are outside the circle of statistics and analysis. They may have different functional roles which is not limited to the aforementioned example.
This population uses Artificial Intelligence and Machine Learning techniques to develop analytical models but they are not formally trained in the field of data science. Today, citizen data scientists are serving to complement the role of expert data scientists.
When a Citizen Data Scientist uses tools, the result analysis is the combined root of his business knowledge and his specific domain skills to have a better insight into trends, issues, and opportunities : reducing time-to-market, improving forecasts...These qualities plus his active engagement in augmented analytics allows the Citizen Data Scientist to interact with the IT team and data scientists more effectively to prepare data and use data in use cases.
Having a large number of citizen data scientists is an opportunity for better analytic performance because business insights always lead to bigger exploration. The reason of their growth is as follows:
Democratization of data usage causes business experts to rely on data experts to handle all operations linked to data. However, data experts (as data scientists) are a limited human resource. So, in order to be independent of data scientists for simple or intermediate data operations, a channel to transfer data knowledge to business experts should be created.
The key to this channel is no code data tools. Citizen data scientists are the best choice to work with these tools as they have comprehensive knowledge about the business and they do not need any IT technical skills to use these tools.
Low-Code and No-Code tools are rising and are embedded in the companies. In the future, these platforms continue to grow offering increased functionality and insights into data, with no need for technical specialization in a full-service platforms.
The ability to predict the market demands powered by artificial intelligence (AI) and machine learning (ML) technologies is another reason for the citizen data scientist population to rise. AI and ML technologies can export pre-built reports based on the most relevant information. Gartner believes that by 2021, 75% of prebuilt reports will be replaced or augmented with automated insights.
The future is seeking automation in analysis. The relation between the user-friendly generation of software, and changing citizen data scientists to the core users creates a demand for recruiting based on user's knowledge.
Gartner predicts companies possessing Citizen Data Scientist initiatives will outperform businesses that do not take this approach and they will be more competitive and better prepared, by increasing productivity and optimizing resources.
The ideal candidate may be a team member who uses data to do his job and he is good enough with tools and eager to use them more. These are the characteristics that he has.
- He knows the business and with his analytical skills always goes further with deeper insights.
- He wants to do his best on what the technology has brought to market.
- He acts as a moderator between IT, Data scientists, and analysts.
- He is a POWER USER of business intelligence tools and technical analytical skills that has a thirst for learning.
You may have noticed that there are already citizen data scientists in your company, working at different jobs. Some are tired of looking at the same old reports and want to find new ways to approach their tasks by learning modern science and using new tools. You should create an environment where citizen data scientists can flourish and influence others to increase the organization’s analytical maturity.
The prerequisite of any kind of analysis is a clean and clear data set. Data Normalization is an inevitable process for each business in which the experts try to qualify their data by removing misspellings, abbreviations, etc, and categorizing it. In most companies, the process is being done either manually which is time consuming, and by experts which is expensive. Manual Data normalization will never result in a clean dataset. This is where YZR comes in. YZR as a no-code tool realizes the opportunity for businesses to normalize their data without any technical skills in a short time and within a reasonable expense. YZR users are mostly Citizen data scientists for whom quality and time talk first. They use YZR to deliver a clean and applicable dataset based on their professional knowledge.