Traditional BI tools were largely designed for highly skilled analysts and IT professionals, who analyze data, run queries, create reports and present actionable insights to decision-makers. Organizations have struggled to provide the same capabilities to the average employee, who typically requires IT assistance to use complex BI tools.
This has led to the development of self-service analytics, which enables ordinary users who aren’t tech-savvy to access and analyze timely, relevant data. Self-service analytics tools are accessed through a user-friendly, browser-based interface that enables any user to run queries and customize reports. Data governance ensures data consistency, accuracy, reliability and access control.
Benefits of Self-Service Analytics Tools
Self-service analytics has a number of advantages over traditional BI tools. With self-service analytics, every user, from the CEO to an administrative assistant, can conduct role-based analyses. They can focus on data that is most relevant to them so they can simplify the decision-making process and solve specific problems. Data analysts and IT teams can spend less time assisting users and more time on strategic initiatives.
Instead of having different departments create reports from siloed data, which can lead to inconsistency and conflict, self-service analytics tools aggregate data from across the IT environment. They provide a single version of the truth by automatically integrating data from a variety of sources into one data model. Customized dashboards and reports and near-real-time responses to even the most complex queries accelerate the process of converting data into insights.
This simplicity translates to minimal training and significantly lower support costs compared to traditional BI platforms. Cloud-based solutions reduce hardware and maintenance costs and can scale without additional hardware purchases or operational disruption.
AI Makes Self-Service Analytics Smarter
The other big distinction with these self-service tools is the use of smart data discovery and visualization interfaces. These tools allow users to create personalized, interactive charts, graphs, reports and dashboards from multiple data sources with drag-and-drop simplicity. That is a marked contrast from the old method of getting someone in IT to explore, analyze and filter data, then export it into a graphics application to produce a visual element to be dropped into a PowerPoint slide.
Generative AI is taking self-service analytics to a new level. Analytics tools are being embedded into a wide range of applications and services so users can access them easily. Some tools can automatically generate analyses with little or no input, and users can share and comment on the results using collaboration platforms.
However, organizations must take steps to ensure these solutions don’t go rogue. Without sufficient governance, these tools could adversely affect the business through inconsistent or incomplete data, unreliable metrics or oversharing of results. Users without any background in data analytics may not be able to interpret the results.
How Technologent Can Help
Technologent has experts in data management and AI who can help you tap into the power of self-service analytics. Our team will help you select and deploy the right tools and ensure that users across the enterprise can access data appropriate to their job roles. We can also help you develop a governance strategy that ensures data is accurate, consistent, secure and well managed.
Self-service analytics tools are making BI accessible to a broader group of users. According to a new report from ISG, more than two-thirds of users will have access to embedded analytics tools by 2026. With self-service analytics, employees at all levels can turn data into business insights that can help reduce costs, identify new opportunities and quickly respond to customer needs.
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