A brief guide to Self-service analytics

Self-service

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Self-service analytics is a commonly used term among organizations using BI. Self-service analytics is often characterized by simple-to-use BI tools in organizations with basic capabilities and does not require much knowledge.

But what is self-service analytics?

Why analytics solution is called self-service?

What are the benefits of self-service analytics?

What are the challenges faced in self-service analytics?

How can you deploy self-service analytics?

What is self-service analytics?

Self-service analytics refers to self-service business intelligence or self-service BI that helps all analytics users the ability to access, analyze, share their data, and discover opportunities without having the deeper knowledge and skills in data or analytics.

The role of self-service analytics is to help those users who don’t have a deeper knowledge of analytics to improve business outcomes and use data analytics regularly to know the next situation coming shortly. This has driven a drastic shift from IT-centric reporting to self-service tools.

Why analytics solution is called self-service?

While using traditional business analytics you will be dependent completely on end-users and depend on ITs whereas in self-service analytics you can work with data only and a dashboard and can make reports and analytics on your own, hence it is self-service because you don’t need help from anyone else. Just you need is data and a dashboard and you are good to go for the analytics of your business.

Self-service analytics are characterized as containing the following:

  1. Augmented analytics capability that streamlines analysis
  2. Flexible and straightforward data connectivity
  3. Drag a UI with a low-code interface
  4. Simplified data querying
  5. Straightforward dashboard and report building

Whether it is building a dashboard, analyzing reports, visualizing data from graphs or charts, or sharing analytics with others, self-service analytics is the best tool to make raw data accessible and simplified for an average person to understand as it is for trained analysts.

What are the benefits of self-service analytics?

Self-service analytics and reporting data offer many advantages to the business organization. It not only reduces the workload of IT departments and the workforce but also can benefit in many ways as mentioned below

1. Increase efficiency of report and staff productivity

Using the traditional way of business analytics can take days and weeks to analyze the business situation and also create stress among the IT department but self-service analytics makes a long list of complete reports and hence self-service analytics increases efficiency and saves time as well.

By giving immediate access to end-users you enable them to create an accessible and comprehensive report to quickly update reports than if done manually by IT departments.

2. Generate accurate results

The more your data changes from one hand to another, the more there is a chance of errors and discrepancies. Moreover, re-checking of data takes a lot of time and effort and analysts are also not much familiar with it. Self-service analytics create a user-friendly environment reduce the number of steps involved in generating report and put data in those hands which has contextual knowledge to correct the errors if any.

3. Better decision making

Self-service analytics improves the accuracy and efficiency of decision-making because it delegates the analytics to those who are expert in making analysis and report for business organization. It also encourages better communication between business and technical personnel.

4. Speeder analysis and reporting for analysts

The self-service analyst helps technical and advanced users like IT developers, and analysts because it frees them from loading and making data analysis or building reports so that they can devote their time to more important work.

Challenges of self-service analyst

There are many challenges faced by self-service analysts. Here are some points that are the challenges for self-service analysts. Let’s explore them.

1. Security of data

Your IT or development team must be able to maintain data governance over data sets, user permission and protection can be put in place over what data is made available for reporting.

2. Lack of user adoption

The self-service analyst’s success relies on how well the organization receives and utilizes it. Employees won’t use it if there is too much resistance. Employees usually don’t embrace new changes.

3. Poor data culture and data literacy

It is very important to carefully see whether your traditional BI can shift to self-service analysis without being prone to resisting change or with data knowledge being low. The new technology adopted must be clear and understood by all employees.

4. Clean data

End users will only be able to find valuable insights with self-service BI tools if what they are exploring is correct in results and reliable for making decisions making the process of clean data and accurate data to prepare for the correct analysis is important.

How can you deploy self-service analytics?

Matching the type of tools intended for userbase ability with self-service analytics is a much more important aspect of deploying self-service analysts.

There are basically three analytics personas to cater to when it comes to deploying self-service analytics:

  1. Consumer: This is the average non-technical business user who can read, analyze, filter, and share content that is built for them. They can’t build reports, they only rely on streamlined BI tools that make analysis of pre-built data.
  2. Explorer: This is a business owner with more understanding and intermediate experience with analytics tools. They also use pre-built data similar to consumers but they can also explore data, create dashboards, and share data of their own.
  3. Expert: This is an advanced user or an expert with an analyticsl tool who uses tools to prototype new data sets, build data, and create reports for those which are not already accessible. Still, self-service tools are helpful for this group to spend less time building reports.

Conclusion

The need for self-service analytics is increasing day by day. Analysts will always need a powerful tool to build reports that take less time and give accurate data. It has a high-in-demand career path and the USA is not an exception in this scenario. Self-service analytics in USA is high in demand. It takes less time and effort to provide accurate results.

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