Empower Decisions Using Self-Service Analytics

Empower Decisions Using Self-Service Analytics

With data taking precedence over every other digital asset for businesses across the sectors, frontline workers should be enabled with powerful insights to make intelligent, fact-based decisions. That happens by democratizing data access and making self-serve analytics available for every user to create a collaborative environment where teams don’t resort to tedious manual analytics. They can leverage existing BI tools to extract valuable data, run queries, generate reports, and do much more.

However, when data is disparately scattered across storage systems and servers, locating and accessing it for analysis is easier said than done. That’s where organizations need to optimize their self-serve data analytics to make data-driven decisions. Here’s how:

Overcome Cultural Challenges with Data Literacy

Sometimes, adopting the right technology isn’t a roadblock; creating a culture of adoption is. Breaking the data silos to prepare teams for data accessibility and self-serving analytics can be challenging, especially if the users can’t see the productivity benefits of new, intuitive tools added to their analytics stack.

Leadership can mitigate these challenges by fostering data literacy at all levels within the organization. It’s just about making data easily understandable for the teams in a way that suits their goals and learning criteria. More than looking at the dashboards, these user groups should be able to slice and dice every metric in any direction for deeper insights without depending on data engineers.

Reevaluate the BI and Data Analytics Stack

Organizations must look at the existing tech stack and find the missing links to empower teams with better insights. For example, sometimes, there might not be an issue with the front end, but data governance behind the dashboards might be missing. Keeping data secure is crucial to maintaining its sanctity and trustworthiness.

The ideal approach is choosing a platform that prevents unauthorized data access by maintaining multiple security layers over the BI stack. A semantic layer that works seamlessly with all cloud platforms and BI tools can ensure complete data protection. It also ensures enterprise security through row and column level access control at user and group levels.

Another great thing would be to choose a platform that’s source and tool agnostic and offers standardized business language to every user firing a query. No matter which and how many BI tools are used within the enterprise, all data should be accessible and query results consistent for all users. This creates a cohesive work environment to get the information that people need for regular or ad hoc analytics without any delay.

Improve the Performance

When implementing self-service capabilities, data leaders must ensure that the teams can access an unlimited amount of data without breaking speed. The tools should allow iterative, flexible, and in-depth analytics with powerful data visualization. Enabling a high technical ceiling enables data teams to work with languages they are comfortable using.

All this should happen without relying on technical experts and by using a tool that can implement a single standard to consume and drive analytics, even when dealing with complex datasets, increasing cardinalities, and multiple dimensions. 

Though measuring the true ROI of an enterprise tech stack is complicated, businesses can start with a clear strategy to enable self-serve analytics using a reliable analytics acceleration platform offering deeper data access with solid guardrails and ensuring reduced analytical costs even at high user concurrency.


I'm a technology content writer with a solid track record, boasting over five years of experience in the dynamic field of content marketing. Over the course of my career, I've collaborated with a diverse array of companies, producing a wide spectrum of articles that span industries, ranging from news pieces to technical deep dives.