Why self-service analytics is essential today

Organizations accumulate torrents of new data every day, but turning it into actionable insights is a daunting task. And when data is siloed in different departments and systems across the organization, users who could potentially find value in it are blocked from accessing it.

Self-service analytics democratizes data, enabling everyone in the organization to explore it and leverage their specific expertise to identify hidden patterns that can provide better insights into critical business levers and gain competitive advantage, without the need for IT team intervention.

That’s why it’s no surprise the market for self-service data analytics is exploding:

Valued at $6.1 billion in 2020, the global self-service BI market is projected to reach $19.3 billion by 2028. Employees empowered by self-service analytics are more efficient. A recent study in the Harvard Business Review revealed that 72% of organizations providing their team members with self-service tools and embedded analytics saw a significant increase in productivity. 

Four keys to self-service analytics adoption

Adopting self-service analytics across your organization and ensuring that qualified users understand and embrace their use is a marathon, not a sprint. Some of the key principles to follow to successfully enable self-service include:

1. An IT team facilitating access to clean, accurate, secure data
2. An environment equipped with analytic and data visualization tools for users
3. A program to teach business users how to perform data analysis
4. Open, collaborative interaction between IT and users.

The need for accurate data is crucial: without clean, well-governed data, there could be inconsistencies and invalid conclusions, which would bounce problems back to the IT department, defeating the entire purpose of the self-service approach.

A few benefits of self-service analytics

Autonomy

Self-service analytics lets business users perform queries on their own rather than asking IT. Your marketing team can easily find out why Crocs were trending in Japan last year, freeing up IT to focus on more important things like data governance and innovation rather than extracting data.

Real-time data

Self-service analytics can automatically extract and combine data from various sources in real time without the intervention of IT, so that data is presented as it is acquired – especially valuable in economics (stock exchange, for example).

Informed decision making

Being competitive means getting to the information first. With self-service analytics tools, your users can get more information more quickly and base their decisions on a fuller picture. And they can easily create their own reports, dashboards, and visualizations to gain insights and make better decisions faster.

Single Source of Truth

Self-service analytics give all users access to the same version of data, ensuring consistency across all departments and systems such as ERP, HCM, SCM, etc. for enhanced collaboration and productivity.

And a few caveats as well…

Data scientists are here to stay

Self-service analytics tools may be changing the way we do business, but data scientists will always be needed to reliably interpret complex data correlations and ensure that analysis processes aren’t mismanaged, which can lead to potentially damaging decisions.

Data governance is an absolute necessity: When an organization is thinking of implementing self-service analytics, it is vital that a data governance policy be put in place to ensure consistent and proper data and information management.

Not all tools are created equally

Certain BI tools are not self-service and aren’t designed to be used by marketing and sales teams, for example. You can’t simply give BI tools to different users and expect them all to be able to manipulate raw data: While 87% of employees believe in the value of data, Accenture reported only 25% are capable of using that data effectively, and 74% feel overwhelmed and unhappy when they work with data.

Training is still necessary

In addition to teaching users how to operate self-service BI tools, companies also need to support self-service with data pipelines that feed them the data they need.

“Just because it’s self-serve doesn’t mean any data that users need shows up automatically – and it shouldn’t”
Mathias Golombek, CTO at Exasol, an analytics database management software company.

How to choose the right self-service analytics platform

Be sure the data platform adapts to all end-users

Business Intelligence tools were originally designed for IT specialists or data scientists with solid backgrounds in computer science or statistics. Unless you were in IT or data science, there was no way you could use a BI tool on your own. Today, thanks to self-service BI, that’s no longer the case. Now, a member of the marketing or sales team who wants to use a dashboard to analyze data can do it alone.

Although nontechnical users need to be able to access data whenever they need it and be completely autonomous when it comes to creating and using dashboards, a good BI tool must also remain useful to data scientists who need to work in-depth on the quality and security of the data that’s fed into it. IT has to be able to monitor the data to ensure they’re building a single source of truth and not chaos.

Ensure integration and connectivity with existing infrastructure & tools

When adding new tools to existing infrastructure, many organizations encounter problems when they adopt separate solutions that don’t work together or are difficult to maintain. While connecting different databases used to be highly complicated, it’s easy today with native connectors or APIs. Self-service analytics can be embedded into your application, leveraging your own IT infrastructure and connecting securely to all of your data sources.

Establish data security and governance policies at the outset

Security is always at the heart of data usage, whether it concerns self-service analytics or any other BI tool. When you give users access to data sets, be sure your IT team knows exactly what data and which users are involved.

If it isn’t intuitive and easy to use, forget it!

One of the advantages of self-service analytics is its low barrier to entry.

Traditional BI was designed for large enterprises with plenty of internal resources. Today, vendors that once sold traditional BI tools now offer self-service ones; Gartner characterizes modern analytics and BI platform as a set of easy-to-use tools that support the full data analysis workflow with an emphasis on self-service capabilities and augmented analytics features designed to help users find, prepare and analyze data.

Properly implemented, self-service analytics can help every company start with BI.