Self-Service Analytics: Advantages & Challenges

by November 7, 2017

With increased proliferation of data in every sphere of business, analytics has become an integral operation of any firm. However, compared to the amount of data generated, businesses often face lack of trained data scientists who can analyze these data and derive insights from it. So, firms are gradually turning away from traditional BI tools for reporting towards more flexible tools for self-service analytics. Gartner predicts that, by 2020, self-service BI platforms will make up 80% of all enterprise reporting. In this background, the necessity of self-service analytics becomes more important.


What is Self-Service Analytics?

Self-service analytics is a form of business intelligence (BI)  that allows business users without any background in data science to manipulate data to spot business opportunities, with nominal IT support. Self-service analytics is often characterized by simple and easy-to-use BI tools with basic analytic features and an underlying data model that has been simplified for the ease of understanding and data access.

The software provides users with a dashboard, which helps in reporting and allows the business users to manipulate large amounts of data. Previously, such reporting and data analysis was solely the domain of trained data scientists. However, with self-service analytics, the need for such trained data analyst diminishes.

The purpose of self-service analytics is to empower business users to perform their day-to-day analytics tasks independently with little help from IT or BI team. This frees up trained data scientists to be involved in more critical data analysis. Some of the popular self-service analytics tools are Domo, Sisense, IBM Cognos analytics, Sage Live, Yellowfin etc.



•  A self-service approach fills in the shortage of trained analysts and gets data into the hands of the business users. Business users can then perform less intensive tasks like data exploration, visualization, reporting on their own. It thus frees up trained analysts, who can now focus on more strategic and core work than reporting. It gives a lot of value addition to organization and businesses.

•  A self-service approach allows business users to make data-driven decisions without having to rely on information from IT department or data scientists. The new self-service BI tools help businesses to place data at the core of their operations and decision-making.

•  It allows the analysis procedure to be decentralized and used by the majority of users and thus leads to the democratization of big data.

•  Self-service analytics empowers the business users by including them in the analysis procedure. With the humongous amount of data generated every minute, it doesn’t make much sense to confine the domain of analytics to a limited set of people.

•  With the division of task at hand and diversification of resources, it can help increase productivity. Business users and core data scientists can work together as a team for better results.

The infographics below by Alteryx showcases the power of self-service analytics in a very interesting way.


•  Many believe that a trained data scientist is essential to reliably understand and get insights from certain data correlations. If the analysis process is mismanaged, it may lead to potentially problematic decisions that may cause damage to the company.

•  A data governance policy should be in place before implementing self-service analytics. IT and BI departments should monitor the use of self-service tools on an ongoing basis to detect and correct any compliance issues and queries that could be problematic for the BI system.

•  It is important to educate the business users properly about implementing self-service analytics. Simply handing over the tools and technology to business users without appropriate training will increases the chance of data mismanagement. However, the limitations of business users in terms of skill and knowledge in handling data should be considered and accordingly, specific training should be provided.

•  The end result of self-service analytics may not be perfect. Some degree of messiness is inevitable. This is the trade-off companies have to face: they may not have perfectly managed data but a greater number of people will be empowered to find meaningful correlations in data, which may become more valuable.

•  Organizations have to ensure consistency of data before implementation any self-service analytics. Any inconsistency in data can lead to an erroneous output.



With data explosion, the future will become more analytics-driven. In the meantime, organizations are trying to optimise their decisions by exploring all dimensions of analytics to gain competitiveness and increase business value. Amongst this data revolution, self-service analytics is here to stay and will eventually spread in all the business layers. The major challenge for the businesses will be to balance self-service analytics while ensuring security and integrity.