Self-Service BI is Business Intelligence for Business Users

Devin Pickell
Devin Pickell  |  October 26, 2017

Roughly 70 percent of all business intelligence users have limited skillsets for manipulating and interpreting data, according to a 2017 BI Survey.

Unfortunately, less than 5 percent of all users could be considered “business analysts” – having a full grasp on the complexities of business intelligence.

No, not everyone is expected to be an expert. This is why there is a high demand in data science professionals and those who work with data for a living.

However, with business intelligence playing an increasingly important role in how businesses make decisions backed by data, it’s crucial to have as many users familiarized with how the software works.

One solution that is aimed at relieving the pressure of IT departments while lowering the barrier to entry for business users is called self-service BI.

What is self-service BI?

Conventional business intelligence tools typically require an internal or external team of data science professionals to query and analyze data. Not every business, however, can afford the high costs associated with bringing on data scientists or analysts. Thus, the rise in self-service tools.

Conventional BI versus self-service BI

To truly understand the benefits of self-service BI, it’s important to understand how it differs from conventional business intelligence.

Let’s imagine your team required the latest business intelligence report to see how you can reduce operational costs for next quarter. Here’s the typical process.

First, you’d need to submit a ticket to the IT department signaling the need for a report. The report will consist of business requirements – like how the data should be visualized and the ways it’ll be applied to a business problem. This alone could take weeks to be approved.

After approval, a data science team will be mobilized to collect relevant data from internal sources such as CRM software, ERP systems, cloud computing services, and more. Data may also be pulled from external sources such as market data and social networks, if applicable.

Data will be standardized, formatted, and cleansed for accuracy. It’ll then be stored in a data warehouse to be pulled for analysis. Finally, conventional BI tools will be applied to generate scorecards, dashboards, descriptive and diagnostic analyses, and more.

If you guessed that the above process is a lengthy one, you’d be right.

Benefits of self-service BI

A background in statistical analysis isn’t needed to access self-service BI. Instead, relevant data is input by an IT department, results are generated, and all business users can access data directly – anywhere, anytime. This is perhaps one of the clearest benefits of self-service BI.

Another benefit is business users can generate their own data models without requiring the knowledge of data mining techniques.

When business users are able to interpret and manipulate data, it frees up valuable time for a businesses’ IT department and data science teams.

Challenges of self-service BI

The proposed benefits of self-service BI are faster, more efficient results that can be accessed on-the-go by anyone within a business. But the proposed solution doesn’t always match the outcome.

Self-service BI, like any piece of software, isn’t without flaws.

Results generated from a self-service BI tool are only as accurate as the user who queried the data. Any analytic tool, regardless of how user-friendly it is, requires an understanding of how data will be applied to a problem and ways it will fulfill the business objective.

A lack of understanding has the potential to generate inaccurate reports – which in-turn, could pose issues for a business further down the line. Fixing inaccurate reports means allocating time and resources that could have been spent elsewhere.

This is one benefit of having data science teams on-hand: To ensure the quality, organization, and accuracy of data. Data science professionals also have great business intuition, meaning they can identify key metrics for determining success.

Future of self-service BI

Leveraging data is an inherent part of how businesses solve problems and reach objectives. This is why business intelligence has become so popular amongst businesses of all sizes.

The future business intelligence, however, will continue to be optimized for the business user instead of data science professionals. Which is interesting, considering a whopping 2.72 million data science jobs are expected to be filled by 2020.

As chatbot technology and artificial intelligence continue to grow, more self-service tools will be voice search-friendly. Essentially, business users will soon interact with their software the same way they ask their Amazon Echo for local restaurant recommendations.

Of course, the design of self-service tools will continue to be refined as more business users take to the intuitive software. For business intelligence results to remain accurate, the learning curve for self-service tools will need to be reduced.

Summary

Self-service BI has grown in popularity as business users and decision-makers within companies look to be more involved in the data analysis process.

Data scientists and analysts, while playing extremely important roles, aren’t always realistic options for businesses with limited resources. Instead, business users could utilize self-service tools to manipulate and interpret data themselves. This is perhaps the most attractive feature of self-service BI.


There are more than 100 self-service BI tools listed on G2 Crowd, each having its own unique features. Some tools are optimized for small businesses, others for enterprises. Some have drag-and-drop features, others experiment with voice search.

To find a tool that fits your exact needs, check out our complete Grid for self-service BI software.

Devin Pickell
Author

Devin Pickell

Devin is a Content Marketing Specialist at G2 Crowd writing about data, analytics, and digital marketing. Prior to G2, he helped scale early-stage startups out of Chicago's booming tech scene. Outside of work, he enjoys watching his beloved Cubs, playing baseball, and gaming.