What is Self-Service BI & How to Choose a Self-Service Analytics Platform?

Self-service business intelligence sounds like a great idea—who doesn't want all their data at their fingertips, available to anyone in the company to answer any question at any time? Everyone intuitively knows that data is not magic, and having a perfect interface and perfect interaction every time is a pipe dream.

Most people would like to see some form of autonomy that they can give to all their employees. Most people intuitively understand the questions they want to ask of their data and the decisions that they want to be data-driven, but there are a lot of technical hurdles that seem to get in the way of a true self-service experience.

In this article, I'm going to give you straightforward information that you can use to understand what it takes to get to a state of self-service BI and to make the most of your investment in dashboards and reporting.

I'll be addressing multiple scenarios and the needs of different types of people. By the end of this article, you will be prepared to decide how you want to go about self-service BI, and how much investment and effort would be required to get you to your target state.

Understanding Self-Service Business Intelligence (BI)

Self-service business intelligence is all about making data analytics easier for people. The vision is that people without a data engineering background can easily use and interpret business data.

What is traditional business intelligence (BI)?

Most organizations operate with a traditional business intelligence data team or a core set of people who handle all requests for reporting and analytics. This has all sorts of downsides. If there are too many requests, the data team becomes a bottleneck. What happens if there aren't enough requests for a period of time? What is the data team supposed to be doing to add value? This can lead to over-engineered systems.

All too often, the data team does not understand the business well enough to interpret the real intent and meaning of the questions they're supposed to be answering. The result is often too many dashboards that offer too little information. For companies that don’t want to build an internal data team, they may be using managed analytics services.

What is self-service BI and analytics?

Self-service BI is an approach to address the downsides of a centralized data team. It brings tools and processes into the hands of any user in a company, with the intent that they don't need to ask the self-service data team for help. They can answer their own questions immediately and get all the information that they need on their own.

The reality of self-service BI

If you look at the demo of any self-service business intelligence software or dashboarding tool and compare it to the reality of using that tool, you will understand that there is quite a gap between reality and the hoped-for ease of use. Some tools offer great capabilities but require a steep learning curve. Others are user-friendly but lack any sort of in-depth analysis capability.

Most BI tools are now offering some sort of natural language querying, where users can simply ask the questions they want answered, and the tool is responsible for generating queries and returning the right results. Generative AI and large language models are not yet mature enough to deliver on this promise. That may change, but there’s a more fundamental gap that a natural language interface will never resolve.

The success of any BI tool relies heavily on having a solid underlying data model. If the data isn't well-prepared, extracting meaningful insights is next to impossible. The data must be clean, consistent, and well-structured to enable any kind of analysis, including self-service capabilities.

Benefits of Self-Service Analytics

As we discuss the benefits of self-service business analytics, keep in mind that this is not an either-or situation. The level of self-service capability that you implement in your organization exists on a spectrum. Different departments and use cases might merit the investment required for robust self-service capabilities, while other scenarios might not require the same level of self-service, or the investment may not be justified.

Benefits of Self-Service Business Intelligence

  1. Empowerment and Agility: One of the most obvious advantages of self-service business intelligence is the empowerment it offers to individuals. Employees at all levels can access data relevant to their roles without having to wait for those with specialized skills to interpret requests and produce a report. This speeds up decision-making within the organization and fosters a culture where data is consulted early in the decision-making process.
  2. Reduced Dependency on IT: Traditionally, generating reports and insights always required the involvement of IT or a centralized data team. With a self-service approach, non-technical users can generate their own reports, visualize data trends, and sometimes perform complex analyses on their own. This reduces bottlenecks and allows data professionals to focus on deeper, more strategic data challenges.
  3. Cost Efficiency: By reducing reliance on IT for typical requests and queries, companies can significantly cut costs. Ideally, the savings from budget normally reserved for IT headcount and tooling more than offsets the investment in self-service business intelligence.

Read Our Whitepaper: How Do Data Analytics And Data Integrity Make Your Business More Effective?

Objectives of Self-Service Analytics

When an organization implements self-service analytics, they are not merely adopting new software; they’re embracing a strategy focused on using data to improve the organization. Understanding the objectives that self-service BI can help achieve will clarify when self-service analytics is appropriate and how much investment is necessary.

Democratization of Data

The primary objective of self-service analytics is to democratize data access within an organization, meaning individuals can access and use data for their individual needs. By making data accessible to everyone, companies not only increase transparency but also cultivate a more informed workforce. People with access to data are capable of making quicker, data-driven decisions.

Increased Responsiveness and Agility

Today's business climate is fast-paced, and organizations must adapt to dynamic environments. Gone are the days when it is acceptable to wait for months, or even years, for a data platform that can answer the questions needed by the business. By having tools to analyze their own data on the fly, employees can adapt to changing business strategies and move more quickly. They are free to solve problems more creatively without having to wait for infrastructure to catch up.

Fostering Innovation through Data Exploration

When users are not bound by the limitations of predefined reports and dashboards, they are free to explore data in ways that might reveal new insights. Domain experts who identify a new opportunity or come up with a hypothesis to improve the business can act immediately, and without much cost or risk. This fosters an environment of innovation and creativity.

Scaling Data Capabilities without Increasing Costs

Often overlooked is the fact that once a data model exists that is flexible and self-service, adding new analyses or reports on top of it is a fraction of the cost and time compared to relying on a central data team. For example, once a central customer entity is defined and accessible, anyone in the organization can use it to analyze their data by customer, filtering, grouping, and aggregating whatever analyses they're performing. This creates a flywheel effect that builds upon itself.

Building a Proactive Data Culture

Data naturally wants to be shared. BI tools often come with easy sharing capabilities, allowing anyone in the organization to publish, comment on, and share visualizations, insights, dashboards, and reports. By making data self-service, and combining that capability with the inherent shareability of the artifacts that are produced, departments in an organization can more quickly and easily collaborate on the insights they find.

How to Choose a Self-Service Analytics Platform and Tools

Before delving into the specifics of this section, it's important to emphasize that this is not about tool selection. There is no single product that can democratize your organization’s data or provide self-service analytics, although every product demo might try to convince you otherwise.

Self-service business intelligence is a strategic approach and investment aimed at achieving the objectives outlined in the previous sections. Certain categories of tools, and specific tools within those categories, may facilitate reaching these objectives. However, recognize that this involves an investment in time and effort that will pay off in the medium to long term, much like a flywheel that can accelerate.

Understand Your Company's Data Analytics Needs

Before diving into a sea of tools, take a step back and assess what you're trying to accomplish with data analytics. Consider the audiences and departments that need better access to data, and whether their skills, experience, and needs align with a self-service data analytics approach.

Note that some of the best clients for data services may not need and would not benefit from a self-service analytics approach. They benefit from analysts who help define the questions, translate those needs into actions for data engineers, and help introduce and train employees on how to best use standard dashboards and reports.

"Note that some of the best clients for data services may not need and would not benefit from a self-service analytics approach."

Understand the Modern Data Stack

Understanding the architecture and infrastructure (known as the modern data stack) required to handle modern business operations and the data produced by siloed systems is crucial. Each component, including data extractors and connectors, cloud data warehouses, data transformation tools, data observability products, self-service business intelligence tools, and data exploration tools and frameworks, can be an asset in pursuing self-service data analytics.

Key Features to Look For in a Self-Service Data Platform

When evaluating BI and data exploration tools suitable for self-service analytics, prioritize ease of use with the non-technical user in mind. Reports and data explorations that exceed the capabilities of non-technical users should have an escalation path that allows more technical analysts or engineers to step in and assist. Collaborative features that facilitate sharing and communication on data projects are also beneficial.

Pricing Models of Self-Service Analytics Tools

Some products allow viewers read-only access to dashboards and other artifacts but charge more for edit and exploration capabilities. In a self-service analytics scenario, these kinds of pricing models can rapidly increase costs. Additionally, some tools charge based on a consumption pricing model, meaning the more data that gets used, the higher your fees. If you are encouraging all users in your organization to use data more frequently, be aware this could lead to increased costs.

Related Article: How Much Do Data Analytics Services Cost?

Self-Service Analytics Use Cases and Success Stories

Here are a few real-world case studies that illustrate the potential of self-service analytics and may provide insights into the outcomes you might expect:

Self-Service BI Helped This Company Jump on Revenue Opportunities

At Equinix Metal, sales management can now identify customers who show signs of being ready for an upsell, as well as those at risk of churning. With the help of Datateer, which provided exploratory tools, Equinix Metal was able to increase revenue opportunities by 10%. Sales management had all the context needed to address these threats of lost revenue and opportunities for increased revenue.

Real-Time Operational Performance Adjustments

A finance team is now able to isolate and explore the downward trend in productivity of some affiliate partners. By identifying the reasons behind the worsening performance of individual affiliates, they are able to provide tailored recommendations. In some cases, the revenue difference between a poor-performing affiliate and a highly productive affiliate is 10 times!

Impacting Educational Outcomes

PERTS (Project for Educational Research that Scales) provides an experimentation platform that allows individual schools and teachers to see the impact of their experiments on key metrics associated with student success. With Datateer's data model, individual teachers and administrators can determine which experiments are most impactful, which had no impact, and where to increase efforts.

Justifying ROI to B2B Buyers

Herrmann Global embeds self-service dashboards into their SaaS applications for executive buyers. These dashboards enable customers to see usage information and the effect of Herrmann’s services on their organization. This increased visibility directly led to a 10% increase in revenue opportunities, as buyers were able to see the tangible impact on their organization.

From Transactional Sale to Strategic Partner

Motion Recruitment, one of the largest recruiters in the United States, introduced self-service dashboards to their account managers and sales reps. This access to real-time information about specific types of jobs and geographies has transformed sales reps into strategic advisors. They are now better equipped to advise hiring managers on how to advertise, where to look, and how to attract better talent. This strategic shift decreased churn and led to upsell and cross-sell opportunities for Motion Recruitment.

These examples illustrate the varied applications of self-service analytics and demonstrate that even individual scenarios can have their own levels of self-service.

The Most Important Takeaway

In the coming months, the hype around AI, particularly natural language querying and reporting, will grow tremendously. If you remember one thing from this article, it should be that no tool alone can bring self-service analytics to your organization. Data visualization products and natural language reporting tools are important components of self-service analytics, but they will never be successful without an overarching strategy and a solid data architecture supporting them. If you need help creating that strategy and architecture a data analytics consultant can help you do that.

Self-service business intelligence brings agility, innovation, cost savings, and speed to organizations that apply it effectively. The best starting point is to identify a set of questions, an audience, or a department that would benefit most from self-service analytics and is ready to participate in the journey.