When a data analyst exclaims, “I’ll just do it myself!”, it typically arises out of frustration. They either can’t wait for someone else to find it or can’t explain exactly what they need, so they set off to search and dig and waste time looking for that data needle in the proverbial haystack. But they then risk using the wrong data, pulling others away from more important jobs, or making bad decisions based on bad data.

Self-service analytics aims to remedy that situation by giving business users the knowledge needed to quickly find, access, and use the right data for the right job. And, it removes roadblocks and takes pressure off IT, data scientists, and others who constantly respond to repeated and redundant requests for data.

Here’s everything you need to know about self-service analytics and how it can help your organization move faster and make better decisions.

What is Self-Service Analytics?

Self-service analytics empowers business users to find and use the data they need, quickly and confidently, without bothering others or taking others away from more valuable tasks. More precisely, it lets business users locate and understand datasets, create and run queries, and generate reports with little to no support. It goes beyond just searching for data to also provide tools that are intuitive and easy to use for non-technical users.

Why is Self-Service Analytics Important? 6 Benefits Worth Noting

Self-service analytics removes friction from the decision-making process for business users while relieving the burden on stretched-thin IT, data scientists, analysts, and other valuable employees. Business users gain a complete understanding of the available data. This enables them to use data with trust and confidence, powerfully and compliantly.

Here are six other ways self-service analytics helps businesses.

1. Enables people to discover and govern strategic data

Data is an abundant resource, but it’s difficult for users to find the right data they actually need. Workers might not even know what they need until they know what’s available. Self-service data analytics helps workers understand what data is available, but more importantly, the details behind the data so they know how to use it. Self-service data analytics also helps businesses understand who is using which data, what data is being used and why, and where governance efforts should therefore focus.

2. Builds a common business language

A self-service analytics platform goes beyond data discovery. Business users need nuanced understanding. For that they need context, such as the lineage behind every dataset, and how it’s been used in the past. Such details guide them on how they should use that data now.

But that also requires even a common and obvious term like “customer” to mean the same thing across every data set. And herein lies a key advantage of a self-service analytics platform: terminology standardization. Aligning disparate departments on key terms creates shared understanding, so data is used consistently and appropriately.

3. Empowers people to innovate with intelligent SQL editor

Of course, once users find the data they need, self-service data analytics must help them use the data independently. Knowing how to query in SQL with statements like SELECT AVG(column_name) isn’t a common skill, especially among business users. A modern self-service analytics platform will empower workers with English language querying capabilities so they don’t need to guess at SQL – and risk using incorrect results – to make critical decisions.

4. Establishes trusted data

Having confidence in the data, from finding the right data to ensuring the correct SQL query was used, helps workers better trust the insights they ultimately gain from their self-service analytics. Trust is key, especially when moving from a process where workers requested data from a data scientist and just assumed it was the right data. To maintain trust and confidence, it’s critical that self-service data instills a level of trust so workers can get the data they need and make quick, confident decisions.

5. Connects BI to data science tools

Business intelligence (BI) tools are designed to evaluate past data, while data science goes a step further to use that data and analysis to spot trends and make predictions about future performance. With business users in the middle, a self-service platform ensures the right data is always used for the right analysis.

6. Facilitates a strong data culture

Building a strong data culture requires more workers to understand, trust, and use more data to advance your organization’s goals. But you can’t be data-driven or data-first until workers can discover, understand, use, and trust the data — everything a self-service analytics platform is built to deliver.

Obstacles that Prevent Organizations from Leveraging Self-Service Analytics

Moving to self-service analytics can be a daunting process. To give business users the proper information, you first need to understand the data itself, know where it’s located and who owns it, and evaluate the data quality. You’ll need talented data experts, but may find a shortage as they’re in high demand. You may discover more data volumes than you imagined, opening up potential data storage and handling challenges. Your data will be of varying quality and require decisions on what to keep, what to archive, and what to clean. And any modern data initiative, especially one for self-service data analytics, requires a formalized and structured approach to data governance.

Data Analytics Governance: What You Need to Know

Data governance is a key component of self-service analytics, but also a common sticking point when moving to a self-service analytics platform. Understanding who is using which data, what data is being used and why, is the crux of good data governance. But research from Gartner shows the chief barriers to successful data governance are a lack of standardization, key roles, and the skills necessary to implement data governance.

The best approach to data governance is to connect the expected success of self-service analytics with your data governance goals. Data governance then helps your self-service analytics improve because your governance efforts are designed to increase self-service success.

​​How to Enable Self-Service Analytics

Setting your self-service analytics efforts up for success aligns well with everything you’ve read here. Governance is a critical and foundational component of any self-service initiative. Creating a common set of terminology and definitions helps bring consistency to data utilization and trust in the data. And, using champions to extoll the success of your self-service analytics initiatives is crucial to building a strong data culture.

How Alation Self-Service Analytics Can Help

Alation Data Catalog helps organizations overcome the challenges of self-service data analytics by making it easy for anyone to find, understand, and trust data. These are what drives accurate, impactful data-driven decision-making. But Alation also adds capabilities for governance, building a common business language, accessing data with an intelligent SQL editor, and giving business users the ability to quickly find the answers they need and move on.

BARC 2021 Data Culture Survey