What Is Self-Service Data Science and Why Should Companies Care?
There is a shortage of data scientists. According to a study, worldwide, there is a need for 28% more data scientists!
This large gap is because of data science has a huge impact on modern business, and a growing number of companies have started adopting data science and artificial intelligence (AI) to build a competitive advantage in the market. In response to this gap, companies have started applying methodologies called “self-service data science”. Since self-service data science helps bridge the gap between the scarcity of data scientists and the business requirements, it is gaining popularity.
Is self-service data science possible in real? Yes, it is possible and is not a crazy idea anymore. Not too long ago, several products such as email, CRMs, file sharing required deep expertise to set up and use. Today, they have become self-service commodities. When was the last time you spoke with a human agent when checking the status of your flight? Today, the majority of the check-in processes are handled via a mobile phone, computer, or at an airport check-in machine.
What is Self-Service Data Science?
Now, let’s take a look at what is self-service data science, and how does it work? Self-service data science is an application that creates value out of data that is accessible to all. It is a highly connected and intelligent, self-learning, and self-healing system. This kind of system is made possible by the combination of data analytics tools and Artificial Intelligence-driven algorithms.
Benefits of Self-Service Data Science
Self-service data science and analytics are helping businesses grow in various ways. More and more organizations are embedding self-service data science into their processes.
– The self-service Business Intelligence (BI) tools give freedom to users to filter, group, and integrate data from various systems within the organization. They just need to understand the usage of the tool. They need not know the nitty-gritty of how the data is cleaned, exported, or ingested into the system.
– These tools help organizations remove the dependency on the data scientists or the IT department for their everyday business requirements or understanding of various domains within the organization.
– The use of self-service business intelligence tools enables business users to create ad hoc reports and dashboards, which was once the sole task of the IT department. Users can produce reports visualizing their key indicators in the most meaningful ways.
– It allows business users to analyze and derive new insights from relevant business processes.
– Furthermore, the reports can be easily reused for other scenarios with a few tweaks and changes in the available predefined templates.
– Another requirement served by self-service data science is the integration of private and local data into existing reports, dashboards, and data models. Self-service functions help users quickly integrate data into reporting without worrying about the raw form of the data, data conversion, data export, data management, or data quality.
– Another aspect of business that is taken care of by self-service data science is the creation and modification of data models independently and in less time. Business users do the work of data modelers adapting their semantic model to a business department’s needs without relying on IT involvement.
A lot of business needs dependencies on the IT department, data scientists, and database specialties are being removed with the help of self-service BI tools. These tools help come to a conclusion or solution at a much lesser time as most of the tedious and time taking tasks have been automated. This also allows data scientists to focus on more value-added tasks. Hence, more and more organizations are caring about on-boarding self-service data science.
Self-Service Data Science Tools
There are various self-service data science and business intelligence tools that are used across industries.
Let us look at a few tools that are widely used across organizations for the work of self-service analytics and data science.
– Microsoft’s Power BI: It is an interactive data visualization tool that does not require technical knowledge and is simple to use. Power BI has an easy reposting system, and it is rapidly evolving in response to market needs. This is being used widely across many organizations.
– Sisense: It is a software that analyzes and visualizes voluminous data sets within a short period. It simplifies complex data preparation, analysis, and visualization avoiding the costly IT investment affair.
– Tableau Desktop: This tool is compatible with all types of data and allows users to have a clear understanding of data irrespective of their type or format. It offers the ability to connect to high volumes of data and perform rapid analysis without programming. Tableau provides seamless interactive dashboards, which make it easier for users to respond to their decision-making needs and come to meaningful conclusions.
– Rubiscape: This is a pioneering data innovation platform that helps organizations make sense of their data without the need of an army of data scientists. It helps enterprises predict trends, understand their customers better, improve business performance, and innovate faster.
Companies using self-service business intelligence tools enable their business users to perform daily analytics tasks by themselves without involving the BI team comprising of data scientists, IT experts, statistical analysis experts, etc. This helps free up the BI team to get involved in more complex data analysis processes. It also helps reduce the operational costs of reporting, dashboard maintenance, improve the experience of business users, and empowers the users to solve business cases at their end.
In today’s fast-changing business environment, organizations need to be more agile and constantly make sense of data being generated from a variety of data sources. Self-service analytics helps in managing the data chaos to some extent. Companies with advanced self-service data science tools embedded in their processes are better off than others in the market. Organizations are now trying to spread the simple analytics tasks to the business users and leverage the core competency of the data science team in more specific areas. This will help organizations achieve the common goal of business success.
Are you ready for the data science revolution?