HOME / Self-Service Analytics: A growing need of the hour to achieve data democratization

Organizations worldwide have recognized the importance of data analysis in identifying trends and patterns and unlocking powerful insights, which can play a big role in helping them stay ahead of their competitors by taking critical decisions at lightning speeds.

According to a study named Predictions 2022: Customer Insights conducted by Forrester, companies engaging in insight-driven decision making are three times more likely to perform better than their competitors.

However, in many instances, data tends to be buried in different systems or siloed across various departments in organizations, resulting in users not being able to access the right kind of data for performing analysis or deriving critical insights on time. Sometimes, in many organizations, a lot of users do not possess the requisite skills to build simple dashboards using standard tools like MS Excel, Power BI, and Tableau. Also, a large number of business users might not be comfortable using advanced features of MS Excel like pivot tables and formulas to summarize data and derive insights. In a survey conducted by Harvard Business Review Analytic Services, 86% of organizations worldwide believe that their employees require enhanced technologies to make data-driven decisions in the moment.

Analysis of real-time data plays an important role in helping businesses make decisions pertaining to the framing and implementation of effective strategies. This can enhance customer experiences and provide them with an edge over their peers.

Owing to a lack of specialized technical skill sets, many business users tend to be dependent on advanced analytics teams for obtaining custom reports and dashboards. Typically, when a business user wants to obtain any report or dashboard for finding answers to business questions, they reach out to people in the advanced analytics team who examine the request, locate relevant datasets from data repositories, construct a complex query and run it, validate the results, and then create a set of custom reports or dashboards. This can result in long wait times, usually ranging from a couple of days to several weeks and involves multiple iterations between the users and the analytics teams before the users get the information that they can use to make business decisions. Furthermore, if there are changes in the data or in the format of reports and dashboards desired by users, the same cycle needs to be repeated, resulting in a loss of agility in decision-making.

Self-service analytics solutions can play a powerful role in enabling users to get answers to business questions without the requirement of any complex coding or specialized skills. Such solutions allow them to enter a question in simple English and obtain answers instantly, just like a Google search. Self-service analytics solutions can also empower business users to aggregate data and build dashboards independently. In this way, users can access datasets stored in large databases, run queries without writing any complex code themselves, and obtain information that they can use to make important business decisions. While allowing users to interact with and explore data, these solutions also have granular policies in place for ensuring that the right users have access to the right kind of datasets and therefore help in implementing data governance at granular levels.

According to a report named Empowering the New Decision Makers to Act with Modern Self-Service Analytics published by Harvard Business Review Analytic Services, in organizations that have adopted self-service analytics solutions, business users have been able to perform 80% of their data analysis, while specialized data analytics teams were required to answer queries only 20% of the time. This has greatly hastened their process of drawing insights and taking critical business decisions.

Self-service analytics solutions possess the below characteristics, making them very convenient and easy-to-use for business users who are not very conversant with complex queries:

  • Intelligent search capabilities: An intuitive natural language interface allows users to ask questions in simple English and obtain answers instantly. Self-service analytics solutions are also equipped with the capabilities of providing search prompts, contextual suggestions, and correction of ambiguities.
  • Interactive visualizations: Self-service analytics solution can have inbuilt visualization tools that present answers to the questions asked by users in the form of interactive visualizations and interesting data stories. This can help business professionals consume insights from data faster and allow business users boost the usage of analytics in their day-to-day operations.
  • Data security and governance: Self-service analytics solutions can also have granular policies embedded in them, which can ensure that the right set of people have access to the right datasets and that sensitive data is not exposed to unintended recipients. This can help prevent crimes related to data and ensure that organizational rules as well as data protection and regulatory standards such as GDPR, ITAR, PII, PHI and PCI DSS as enforced by the respective authorities are also fulfilled.

Thus, self-service analytics solutions have huge potential for facilitating data democratization across organizations. But what does data democratization mean?

Data democratization involves making data available, accessible, understandable, consumable, and actionable for business users across all levels within organizations. It ensures that all stakeholders are comfortable and confident in working and interacting with data without having to learn about the technicalities or being dependent upon specialized teams for data analysis or the sharing of reports. These teams can then become more productive and focus more on critical issues like data governance, data quality, and the building of advanced data models.

Sounds exciting right? Let us now understand more about how self-service analytics solutions work.

Self-service analytics solutions leverage natural language processing and advanced machine learning algorithms to empower business users at all levels of organizations to draw powerful insights from huge volumes of data stored in databases. Such solutions are analogous to a no-code/low-code platform, allowing business users and CXOs to aggregate data and build functional dashboards on their own. The working principle of self-service analytics solutions offering a no-code environment to users for creating dashboards is shown below:

The working principle of self-service analytics solutions leveraging natural language processing and machine learning is explained below:

Users can formulate their own queries in a language of their choice and get answers in real-time from huge volumes of data getting updated in the cloud, rather than waiting for data teams to respond. Being dependent on data teams often leads to multiple iterations in which stakeholders think of asking more questions upon seeing more data. If stakeholders can drill down on data independently and ask questions in English without being dependent on technical teams, the waiting time for gaining insights and taking decisions is greatly reduced as compared to frequent iterations with data teams.

This is critical because the granular insights needed to make the right business decisions get uncovered only after multiple questions have been asked. By using such a solution, users do not have to ask the data team repeatedly for reports or go back and forth before getting exactly what they want. Being able to independently drill down on data also allows stakeholders to apply their in-depth knowledge of the business for digging deeper. This allows them to derive high-quality insights, which, when coupled with their own skills and business knowledge, can really help them be confident while engaging in data-driven decision-making.

Modern self-service analytics solutions are also customizable making them easily integrated with the existing data environments of organizations and facilitating faster deployment at scale. Such solutions can also use data in any relational format and reuse access settings present across existing databases. Most importantly, these solutions do not require users to have any technical training and have a simple user interface that can accept any business question in simple English and display answers in a flash!

However, investment in self-service analytics solutions alone cannot deliver business value. To increase adoption of self-service analytics tools, organizations need to ensure that all business users can access the data conveniently and have trust in it to use the solution effectively and gather powerful business insights. Data needs to be integrated from disparate systems, and a data catalogue needs to be created for self-service analytics solutions to yield valuable insights. It also aids in data governance and help make business users confident that the insights they gather from the solution are based on trusted data.

Self-service analytics solutions possess a huge potential to deliver on the promise of helping organizations achieve data democratization by enabling business users lacking specialized technical and coding skills to interact independently with large volumes of data and draw powerful insights in a matter of a few seconds by asking questions in a natural language of their choice and receiving appropriate results, tables, and visualizations as a quick response.

This allows organizations to leverage the data they possess by overcoming barriers such as siloed data in numerous storage systems. By unlocking powerful insights and implementing strategic business decisions faster, self-service analytics solutions can increase their ability to grow and stay ahead of their competitors.

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