Making Everybody a Citizen Data Scientist Is the Easy Way to Streamline Digitization

The simple solution to speed up digitalization is to turn everyone into a Citizen Data Scientist.

The need for knowledge is growing as firms use data science to gain distinct competitive advantages. Currently, there is not enough supply to satisfy that demand. As a result, unconventional jobs in data science and machine learning are now possible, including those for citizen data scientists. These positions are frequently touted as the solution for helping businesses adopt machine learning (ML) and artificial intelligence (AI) quickly and affordably (AI). But relatively few businesses have been able to make use of the expertise of citizen data scientists.

A brief survey was conducted at a workshop to gauge the issues and opinions of the participants about citizen data science. Additionally, the poll indicates that these IT executives are still in the early phases of implementing citizen data science projects, even if the sample size of 60 respondents is too small to make any conclusions:

Respondents named their spreadsheets (53 percent) and automated reports run by IT and data teams as the top two ways business departments generally access data (43 percent). Self-service BI was listed as one of the main ways business divisions see data by 43% of respondents, however, only 13% of those respondents claimed their self-service BI had solid governance.
According to the survey, the functions with the most potential for improvement and the least use for data analytics are product development (28%) and customer experience (35%)
The top three obstacles to business, data experts, and technologists working together on data-driven strategies were listed in one question. The most common response, given by 40% of respondents, was that business executives just want IT to produce reports and repair the data.

This apparent lack of development is far from encouraging considering that data visualization and preparation tools became widely used 10 years ago. IT and data executives must intensify data governance programs that aid citizen data science initiatives if they want to move things in the right way.

Transform compliance risks into force multipliers for citizen data science

Spreadsheets have a flaw in that business users were introduced to them before data governance procedures existed. Business analysts collected data sets, produced many spreadsheets, and sent the documents to co-workers through email. Replace spreadsheets with your preferred data visualization tools now, and if left unchecked, you can run into much more serious issues.

Issues include:

sharing sensitive information with others and putting compliance in danger;
unlawful disclosure of information to others outside the company;
making incorrect conclusions based on assumptions and misunderstanding data definitions;
sharing analytics and insights without confirming outcomes and evaluating the algorithms;
Making visualizations without following any standards or style requirements makes it harder for employees to comprehend the outcomes.

Of course, the dangers are greater now because the majority of businesses analyze large data sets, employ several analytics tools, and create bespoke code for their machine learning models. For actions that generate income and operational efficiency, the business uses analytical models, and errors can be expensive. To prevent risk from speeding the pace of citizen data science projects, data governance strives to satisfy compliance needs, knowledge gaps, and data quality objectives.

Experts provided some advice on how to enable citizen data scientists within your firm:

Put business analysts and citizen data scientists together

Citizen data journalists should not be provided with self-service analytics that just let the user work everything out on their own. To provide various kinds of analysis and visualization that are useful to everyone, firms should ideally match users with BI experts. According to Michael Golub, senior vice president of analytics and machine learning at Anexinet, "having a business power user linked up with a seasoned BI practitioner may typically lead to greater outcomes faster than putting the burden into either group solely." Visualization requires both technical and creative skills. One strategy to make the most of the time spent creating and developing is to adopt an agile business/tech-working-together approach.

Pick the appropriate self-service equipment

"Making your visualization tool is a fool's errand, as we painfully discovered. It takes time, is quite expensive, and is still inferior to available options "Swann clarified. "You have to choose a technique that makes visualization simple and adaptable." Organizations looking to empower citizen data scientists should search for self-service analytics platforms with scalability and the capacity to effortlessly interface with data sources that are important to the company, according to Samantha Marsh, the marketing coordinator at iDashboards. Consider adopting a solution that kickstarts your projects with established visualization templates if speed and simplicity are crucial to your business," said Marsh.

Pay attention to the users

Both IT and BI teams must pay attention to users to acquire the technology and procedures that work best for them. "One of the toughest realities to learn about big data and visualization is that no IT person can tell marketing folks what the end-user needs to see. Although we may counsel them along the process, they must develop their own opinions to make decisions "added Barak. Data scientists concur, but they underline that citizen data scientists must also put in their labor.

Cultivate personalities

It should go without saying that a large company cannot adapt its visualization techniques to suit the needs of every individual citizen data scientist who wants to analyze data. To meet the needs of the biggest number of customers, several experts advised taking a cue from the marketing division. Hugh Johnson, Suntico's senior vice president of development, asked: "Why not create personas for your visualization users, just like your marketing team creates them to pitch your products and services?" "Then, keep in mind who they are designed for and why as you construct your graphics. The likelihood of the conclusion being something that will genuinely help the business is higher."

Check the data

Maintaining clean and well-managed data streams is one of the key responsibilities that IT plays in empowering citizen data scientists. This involves making sure that people only have access to the data they require to perform their duties and that data is safe throughout its entire lifespan.