What is self-service analytics?
Self-service analytics is the type of business intelligence (BI); assists the end users to deeply analyze their data, developing their reports, and discover related queries’ solutions on their own with ease. In a nutshell, self-service analytics allows endusers to procure insights into their business data without any expertise of analysis. It is characterized by business intelligence tools along with nominal IT support to aid non-technical users in processing their complex data. The leading intent is to acquire easy to understand and to-the-point data layout for non-technical users.
Why do we use self-service analytics?
Self-service analytics is for business users to inspect insights of their data on their own at the right time. It pulls the curtain on the free of cost and agile report-making method. The businessmen neither have to consult the IT experts and wait for their response to get their problem solved or to get the analytics of their data, nor have they to rely on costly intuitions that may lead to disastrous results to the business.
Self-service analytics tools are accessible and non-technical users understand them well. These tools are designed for those users who have no or basic knowledge of IT. According to the survey, almost 70% are non-technical users of these tools.
How self-service analysis is helpful in data analysis?
Self-service analytics is for everyday business users to solve their queries on their own. Business users avail it for adapting their reports, answering their problems, creating a hypothesis, making predictions by viewing trends in data, and taking successful measures to promote their business.
Data visualization: Data visualization is far more necessary in developing profitable business strategies. According to a report, 94% of Indian leading firms consider data visualization and reporting skills critical for their business. Self-service analytics tools provide an accessible dashboard to visualize your data in the form of graphs, tables, and pictures.
Statistical analysis: Self-service analytics tools assist in trends identification and information gathering for problem-solving. It abets the businessman in an everyday well-informed decision-making process based on data analysis, not on hunches.
Customer-service: Self-service analytics aids in analyzing the data based on the customer feedback and response for improvement in customer services. The trends in customer behavior are vital to stand a business.
Briefly describe self-service analytics help to gain insights into the data to make well-informed decisions and improve business.
What are the advantages of self-service analytics?
Data analysis is an integral part of every organization. Self-service analytics provides a broad road for drilling the data to get the queries solved.
Empower the business users: Self-service analytics authorize the end-users to solve their problems themselves. It alleviates the dependence of business users on IT experts for the report making.
Real-time insights: Self-service analytics assist in data evaluation without a wait. Business users do not have to hire IT experts and then go back and forth with them to get the desired report. The agile data reports help in timely informed decision-making.
Lower cost: If you have a small business then it would be uneconomical to buy a consultant, organize an agency, and get reports. Companies can save up to 50% of their cost by working on their own on the data. They can reduce the cost of consultants, agency group members, and also everyday report is better than the monthly or weekly outdated report.
Data security: Self-service analytics allows extra data protection. It does not enable unauthorized access to your data and secures the data from breaches. Almost 53% of data breaches are due to third-party attacks. If your data is under your control, the data leakage risk reduces drastically.
Risks of self-service analytics: Some risks are also associated with self-service data analytics as you do not have diversity in your trends and reports and for those who are poor in analytics, It causes them to dive into their data and they do not get as much as the effective decisions.
Substandard data literacy: For informed decision-making, variations in the report are necessary. The chances of the best decision-making increase relatively as the number of reports increases. This is only possible only when the team of experts is carrying the data analytics. Self-service analytics tools provide a few reports for decision-making.
Lack of critical view on data: For better analysis of the data, critical analysis is essential. The data must be specified based on experience, the trends must be viewed based on historical data trends and predictions must be based on critical observations.
Not for one who is poor in analysis: Those business users who are worst in data analysis cannot able to adapt the reports according to a suitable standard to get favorable guidelines to follow.
Conclusion:
Self-service analytics assists non-technical end-users to perform business queries on their own. It provides a straightforward platform to ensure the finest reports for well-informed everyday decision-making. Everyone can easily use the self-service analytics tools. Although this is the fastest and free of cost method for data visualization; few drawbacks minimize its use on an expansive scale but, it is favorable for small-scale business users.
