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The Role of Citizen Data Scientists in the Big Data World

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Citizen data scientists are beginning to perform duties formerly reserved for pure data scientists.

Citizen data scientists can be defined as a set of business users who can perform simple analytical tasks on their own. With the advancement of BI tools and platforms, enterprises are trying to maximize the analytics tasks across all the business layers. As a result, the core data science team can concentrate on tasks that encompass more critical analytics. This step will help the organization to democratize big data and get the benefit out of it. The term “citizen data scientist” may cause some confusion among the user groups, but it is going to spread rapidly in near future.

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What Is a Citizen Data Scientist?

Gartner has defined a citizen data scientist as a person working in the field of business and having at least some limited knowledge in the field of big data. They may have some data skills, which they may have gained from their experience in the field of mathematics or social science. They put this knowledge to use through data analysis and exploration. These people usually have an extensive background in business, so they are often more preferred than normal data scientists. (To learn more about data scientists, see How to Nurture a Data Scientist.)

The concept of citizen data scientists is creating a lot of buzz in the modern-day data scenario. In this age of extended big data range, the concept of citizen data scientists is finding itself right at home due to the fact that some larger businesses cannot allow all their data to be given to an outside data scientist for analysis. And this is where the citizen data scientists are coming into play.

How Does It Come Into Play?

Being a data scientist requires a background in the field of business intelligence (BI), data analytics studies and statistics. And nowadays, several companies are adopting big data very quickly due to the various advantages they get from the insights that are offered. Also, not everyone can have an extensive enough study background in order to become data scientists. Thus, the end result is that businesses are adopting big data at a very quick pace while not enough people are becoming data scientists to fill the demand.

This is the reason why there is always a shortage of data scientists in the field of big data. Also, due to this shortage, data scientists have to be paid a very high salary. So, a solution had to be found so that newer companies could easily adopt and manage big data, and the concept of the citizen data scientist is just the solution for it.

Motivation Behind This Role

The main motivation behind this role is that data scientists are very rare, so they are very difficult to hire and expensive to manage. However, many businessmen with backgrounds in social science and mathematics can do the job as well if trained properly. Also, these people do charge exorbitant salaries. Thus, these “citizen data scientists,” as they are called by businesses, can be of immense help to newer and larger businesses alike. Additionally, as using big data analytics applications is becoming easier, this concept is being implemented in many places.


Advantages of Citizen Data Scientists

There can be many positive outcomes in implementing the concept of citizen data scientists. The first is that they are always available and the business can train new scientists easily. Another major advantage is that they do not have to be paid a six-figure salary. However, the biggest advantage of all is that citizen data scientists can work more efficiently than data scientists due to the fact that citizen data scientists have an extensive background in the field of business, so they know what is better for the business.

We can understand this statement better by looking at an example. A data scientist is analyzing methods of cargo shipping. Will he understand the minute details of this? Will he be able to select the best passage for shipping, considering not only the infrastructure of the airways, waterways or roadways, but also the height of the bridges under which the cargo has to pass? No, these kinds of minute details would only be known by an experienced business user.

There are many such logical questions which arise during daily activities in any businesses. The solution to such problems cannot be taught in any university or school. With age comes experience, and experience in the field of business issues is the only thing that can take care of these small, but significant, problems. Thus, citizen data scientists are becoming preferred over pure data scientists.

Drawbacks of Citizen Data Scientists

The concept of the citizen data scientist is a very useful one. However, there are still some limitations to citizen data scientists.

To start with, these data scientists may have an extensive background in business, social science or mathematics, but they often have very limited data analysis skills, so they have to be properly trained in order to be employed effectively. Another problem is that with citizen data scientists, the probability of security breaches increases, which can result in a great loss of money and time, and privacy can also be at stake.

Additionally, such citizen data scientists can set out to do the work on their own, only to create strange strategies that conflict with the strategies of other scientists and business modules, thus creating serious disturbances.

So, proper governance is very important to implement the concept of the citizen data scientist, otherwise it has the potential to create a mess.

Are We Sold on the Idea?

The concept of citizen data scientists has been around for quite some time, but still everyone isn’t sold on the concept. This is mainly because of several shortcomings.

They think that normal employees, without having a background in data science, cannot do the work well. As data analytics is a crucial element in big businesses, they believe it should not be left in the hands of someone who is inexperienced. However, many also believe that a small amount of training can help them collaborate with each other and do the work well. New sets of tools in data science help them as well. So, it seems that it is indeed safe to trust them with a business’ data analysis.

What Is the Future?

Citizen data scientists have the potential to be the future of business intelligence. With the advent of citizen data scientists, open data will be used even more extensively in the near future. Every business and organization will be able to hire low-cost citizen data scientists, and data skills will be in abundance. However, this doesn’t mean that the existing pure data scientists will be jobless. They’ll train the citizen data scientists, in order to hone their skills in the field of data analysis. (For more on learning data science, see 7 Steps for Learning Data Mining and Data Science.)

Due to the increasingly lower cost of big data adoption, more and more organizations and businesses will be able to adopt big data for their everyday operations. And as the applications for big data analytics get easier to use, more citizen data scientists will be able to use them and the skill set will grow further. So the future of citizen data scientists is seemingly very bright.


Business intelligence and data analytics have come a long way since their inception, but still there is a scarcity of people with skills in data science. On the other hand, big data is becoming very important day by day for businesses, which look to gain useful insights and stay ahead of their competitors. But due to this scarcity of skilled analysts, how can businesses adopt big data successfully? A powerful answer comes in the form of the concept of the citizen data scientist. They can help businesses make full use of the data available openly today. Though there are some shortcomings, they can still help many businesses gain by using big data.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…