Table of Contents:
- What are Self-Service Analytics?
- 7 Critical Reasons to Use Self-Service Analytics
- Pitfalls to Avoid with Self-Service BI
Gone are the days of “hunches” and “gut-decisions.” In business, the best decisions are the ones backed by data. But without access to a robust data analysis tool, many end-users must request reports from IT. For larger organizations, report requests can take weeks to fulfill and, by that time, the data used in the report is likely irrelevant.
Self-service analytics tools remove the burden of report creation from your IT department, empowering end-users to create the reports they need at the time they need them.
What are self-service analytics?
Self-service analytics is a Business Intelligence platform that enables business users with limited or no IT background to perform queries, create reports, design data visualizations, and more without a deep understanding of the underlying data model.
For modern businesses, this helps build a culture of data-driven decision making. Users gain a sense of ownership in the data, the analytics solution, and the insight they gain through their analysis. They can freely explore the data to unearth hidden insights. They start asking questions that only arose when the data exploration began. By delivering a self-service analytics tool as part of your application, users can examine their data from many angles using many data sources.
Here are 7 critical reasons to use self-service analytics tools in 2021.
- Gain business insights without a data scientist.
There are two truths about data scientists: they’re in high demand and they’re expensive. In fact, there’s been a much-talked-about data scientist shortage going on for years. As more and more businesses look to leverage their ever-increasing data into reports and dashboards, self-service analytics tools look to solve this issue. How? By making everyone a data scientist.
There’s a belief within some organizations that data can’t be truly understood without a data scientist. But rather than spending time and resources hiring additional help or training your staff, deploying an analytics tool that is truly self-service empowers even the least-technical employee. With a self-service analytics tool, any user can create rich reports and dashboards with minimal training.
- Embedded self-service analytics tools put data and decisions in context.
While traditional Business Intelligence platforms can help organizations gain some into their data, embedded analytics platforms can deliver a more valuable and richer analytics experience by keeping the user in the workflow.
Embedded self-service analytics tools blend into your application’s interface to match its look, feel, and navigation. With seamless integration of an embedded BI solution, end users don’t have to learn a new interface. This delivers in-context analytics within the business workflow. End users will never know they are looking at third-party software. It’s your self-service analytics tool, which assures stronger user adoption and satisfaction.
- Data visibility is on the rise.
Business users know the queries they want to make, so it makes sense to put access to self-service analytics into their core business application’s workflow.
With self-service analytics tools, many users see an increased visibility into business data. Users of a self-service tool no longer have to rely on IT for insights and gain greater access and visibility to the data they need. In short, they retrieve information and generate insights faster.
In a survey of 500 applications teams, nearly 50 percent reported that with embedded self-service analytics they were able to reduce the number of reporting requests from users—and 67 percent saw an increase in the time spent in their applications.
With a self-service analytics tool, users are able to drive improvements. And with increased visibility into the company’s data and a shorter time spent in search of information, better business decisions can be made.
- Self-service analytics tools empower your end users.
In 2021, end users have come to expect some sort of reporting and data analysis functionality in their applications. And not only do end users want impressive outcomes, they want to take ownership over the insights they’ve uncovered without needing to ask for help. If your app isn’t empowering your end users, then it’s inhibiting them.
End users want an intuitive analytics platform to analyze, visualize, and share data and insights in real-time. The right solution requires giving them a true self-service reporting and analytics tool that a non-technical user can engage with to create their own ad hoc reports, dashboards, and data visualizations.
- Creating data visualizations helps users uncover business trends.
Data-driven decision making is a critical part of a business’s daily operations in 2021. To stay on top of changing business needs and stay competitive, decisions also need to be made quickly. The most effective way to communicate critical data insights is through data visualizations.
By creating their own data visualizations from the data revealed in their self-service reports, end users can discover trends in the ever-increasing volumes of data. Giving them the ability to drill down through the data visualizations into the underlying data enables them to determine the next question needed to discover the root of these trends.
With self-service data visualization tools, critical business decisions can be made on-the-fly – helping your business stay agile and competitive.
- Self-service analytics tools reduce IT overhead.
For most software teams, Business Intelligence and analytics is most likely not their core competency. However, in many organizations, developers devote half their time to creating reports for end users. Considering the average salary of a developer, your business is likely spending hundreds of thousands of dollars a year on reports alone.
Self-service analytics tools remove the burden of report creation from IT and empowers end users to take control of their data and create their own reports. End users can get answers to their critical business questions much faster while developers can spend more time on the core application.
- Self-service analytics tools improve the value of your app.
In 2021, it’s likely that your competitors have already implemented some form of self-service reporting and analytics in their application. However, there are many different methods to providing self-service BI to end users, from choosing a third-party solution to embed analytics within your app to building the entire BI tool in-house.
The method that brings the most value to your application is choosing a third-party tool to embed self-service analytics and reporting within your app. By delivering embedded self-service analytics within your app, end users never have to leave the application to create reports and dashboards. Furthermore, they get the benefit of using a tool refined by years of updates and implementations.
Pitfalls to Avoid with Self-Service BI & Analytics
Self-Service BI is a useful tool, but it’s important to understand what self-service analytics can and cannot do. Here are a few common mistakes companies make with self-service analytics and how to avoid them.
- Working with Unclean Data
Understanding the data you’re analyzing is important. Without that, you could end up with user bias or confusion. Data often comes from multiple sources, and that data is often a compilation of data merged from various other sources, so even small inaccuracies can impact your ultimate conclusions.
Starting with clean data is essential for accurate analysis. Data cleaning means finding and correcting any inaccuracies or corruption within your data sets, which may include incomplete, incorrect, inaccurate, or irrelevant data. From there, replace, modify, or delete any unclean data to be sure you’re working with the best possible insights.
- Working from the Conclusion
If your team is working with a desired result in their analysis, it can create bias in their analysis. If a user seeks a pattern in data that supports their conclusion, which is working from the conclusion backwards, it could lead to an inaccurate result that excludes other pertinent information.
This could be avoided one of two ways. Data should be approached without preconceptions or bias, allowing the data to lead the way toward a conclusion. If possible, another user could be brought in to offer another viewpoint, which validates both the approach and the conclusion.
- Jumping to Conclusions
With multiple data sets, it’s easy for users to focus on data that isn’t related to the problem. They may become distracted by data that doesn’t directly affect the goal, which leads to misleading results.
One of the biggest benefits of self-service analytics is the ability to view disparate data sets holistically for the most accurate conclusions. Conclusions drawn from data should always be checked for context and accuracy to be sure that they’re the most relevant and accurate.
Self-Service Analytics from Logi Analytics
With self-service analytics from Logi, your end users can quickly create reports and dashboards, perform analysis, and share their insights with colleagues – all within your application. This allows you to get to market faster and improve the value of your offering without compromising product features.