How self-service analytics is changing modern-day businesses

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.

Rise of the Citizen Data Scientist

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.

How Self-service Analytics benefits businesses

Allowing the end-users within a business to perform their own analysis has some important advantages as compared to using the traditional BI platforms:

• The time taken to arrive at crucial business insights is drastically reduced. This is because teams don’t have to rely on the IT team to deliver specific reports and dashboards based on the organizational data. Quicker insights from self-service BI tools mean businesses can take decisions faster with higher confidence and deploy appropriate strategies to maximize business goals.
• Because of the relative ease of use, business users can get up to speed with the self-service BI tools/platform in no time and with very little training as compared to being trained on complex BI solutions. This means relatively lower training costs and democratization of BI analytics which in turn reduces the workload on the IT team and allows them to focus on their own core tasks.
• Self-service analytics helps the users to manage the data from disparate sources more efficiently, thus allowing organizations to be agiler in terms of handling new business requirements.

Challenges in Self-service analytics

While the self-service analytics platforms offer many benefits, they come with their own set of challenges too.  Let’s see some of them:

• Defining a clear role for the IT team within the business by addressing concerns such as:
  • Identifying the right BI tool for the business - Among the many tools out there, identifying which tool is the best fit is very important.
  • Identifying which processes and business groups can make the best use of self-service BI and who may require assistance from IT
  • Setting up the right infrastructure and support system for data analysis and reporting
  • Answering questions like - who will design complex models and perform high-level data analysis

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.

• Defining a strict data governance policy - This is a critical task as unauthorized access to organizational data can be detrimental to the business. Identifying the right ‘power users’, i.e., the users who need access to the data and the tools, the level of access that needs to be given to them, and ensuring the integrity and security of the data are some of the key factors that need to be kept in mind. The IT team plays a major role in establishing strict data governance policies and ensuring the data is safe, secure and shared across the right users for self-service analytics.
• Asking the right kind of questions on the data - When users who aren’t analysts get access to data and the self-service tools, asking the right questions of the data in order to get useful, actionable insights from it becomes highly important. Failure to perform correct analysis can result in incorrect or insufficient findings, which might lead to wrong decision-making. Regular training sessions and support systems in place can help a business overcome this challenge.

To read more about the limitations of self-service BI, check out this interesting article.

In Conclusion

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:

• Learning Tableau 10

• Tableau 10 Business Intelligence Cookbook

• Learning IBM Watson Analytics

• QlikView 11 for Developers

• Microsoft Power BI Cookbook