To stay competitive in today’s economic environment, organizations can no longer be reliant on just their IT team for all their data consumption needs. At the same time, the need to get quick insights to make smarter and more accurate business decisions is now stronger than ever. As a result, there has been a sharp rise in a new kind of analytics where the information seekers can themselves create and access a specific set of reports and dashboards – without IT intervention. This is popularly termed as Self-service Analytics.
Per Gartner, Self-service analytics is defined as:
“A form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.”
Expected to become a $10 billion market by 2022, self-service analytics is characterized by simple, intuitive and interactive BI tools that have basic analytic and reporting capabilities with a focus on easy data access. It empowers business users to access relevant data and extract insights from it without needing to be an expert in statistical analysis or data mining.
Today, many tools and platforms for self-service analytics are already on the market – Tableau, Microsoft Power BI, IBM Watson, Qlikview and Qlik Sense being some of the major ones. Not only have these empowered users to perform all kinds of analytics with accuracy, but their reasonable pricing, in-tool guidance and the sheer ease of use have also made them very popular among business users.
The rise in popularity of self-service analytics has led to the coining of a media-favored term – ‘Citizen Data Scientist’. But what does the term mean?
Citizen data scientists are business users and other professionals who can perform less intensive data-related tasks such as data exploration, visualization and reporting on their own using just the self-service BI tools. If Gartner’s predictions are to be believed, there will be more citizen data scientists in 2019 than the traditional data scientists who will be performing a variety of analytics-related tasks.
Allowing the end-users within a business to perform their own analysis has some important advantages as compared to using the traditional BI platforms:
While the self-service analytics platforms offer many benefits, they come with their own set of challenges too. Let’s see some of them:
Thus, rather than becoming secondary to the business, the role of the IT team becomes even more important when adopting a self-service business intelligence solution.
To read more about the limitations of self-service BI, check out this interesting article.
IDC has predicted that spending on self-service BI tools will grow 2.5 times than spending on traditional IT-controlled BI tools by 2020. This is an indicator that many organizations worldwide and of all sizes will increasingly believe that self-service analytics is a feasible and profitable way to go forward.
Today mainstream adoption of self-service analytics still appears to be in the early stages due to a general lack of awareness among businesses. Many organizations still depend on the IT team or an internal analytics team for all their data-driven decision-making tasks. As we have already seen, this comes with a lot of limitations – limitations that can easily be overcome by the adoption of a self-service culture in analytics, and thus boost the speed, ease of use and quality of the analytics.
By shifting most of the reporting work to the power users, and by establishing the right data governance policies, businesses with a self-service BI strategy can grow a culture that fuels agile thinking, innovation and thus is ready for success in the marketplace.
If you’re interested in learning more about popular self-service BI tools, these are some of our premium products to help you get started:
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