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What is self-serve analytics and do you need it? Yes!

End users like data professionals have started using self-service data tools such as Tableau to prevent data stagnation and stale data storage. These self-service solutions are easy to use, even for non-technical professionals to gain the desired insights exactly when they need them. With the knowledge of these solutions, any of your team members can wear a data scientist's hat. 

If you are wondering, is Self-Service Analytics suitable for your company to deliver the desired goals, here are a few points that can help you decide.

  • Self-service tools authorize business users: Authorizing the end-users of the data insights empowers them to help themselves whenever the need arises. So, self-service analytical tools provide the user the much-needed timely insights they can arrive at themselves without any assistance.
  • A resource saver: When an end-user raises a ticket carrying a data need and shares it with the data analytics team, he/she will have to wait for them to respond. However, by making use of self-service analytics, you can completely cut the wait time.
  • The data analytics team can focus on priority projects: When the end-user resolves data queries by themselves, they spare the data analytics team a lot of time. Thus, data analysts can focus on high-priority projects that demand their attention.

Bringing the data in disparate systems together to one place is key to the success of self-service analytics. Data governance is the best way to integrate data across functions and locations, making end-users' efforts worthwhile and precise. Self-service analytics pooled together with Business Intelligence gives your efforts a better chance to savor success.

However, along with perks, self-service analytics brings some disadvantages that end-users need to be aware too-  

  • Data inconsistency: If the organization is unsuccessful in governing the data correctly, data across the functions will not be in sync and fetch insights that are not accurate, thereby adversely affecting the decision-making process.
  • Lack of training:  It is also essential to examine the analytics capabilities of end-users. Although some may have the aptitude to use the said tools, the others will need training. The lack of knowledge, both conceptual and practical, will limit the end user's ability to use these tools effectively.

Although self-service analytics is of great use to companies, it is imperative to get started with the correct initiative and preparation.


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