Self Service Analytics - Definition, best practices, importance in 2022 and beyond

Self Service Analytics: Definition, Best Practices, and its Importance

by Chris Afiesh, Director of Sales

What is Self-Service Analytics?

by Chris Afiesh, Director of Sales

Self-service analytics is the practice of empowering and encouraging users to explore data on their own through the use of intuitive software. The objective is to provide non-technical users the ability to generate their own reports and analyses without requiring attention from IT or technical staff.

Modern analytics platforms such as MachEye provide users with a familiar search-based experience to ask questions and instantly gain results. With MachEye, visualizations and reports are automatically created by the tool, and also get delivered with relevant insights generated by continuous machine learning. The leading platforms use NLP and AI to convert users’ questions into SQL statements, making it easier for users to effectively address complex or ambiguous questions.

Strong use cases for self-service analytics

Data-driven functions such as Marketing are key beneficiaries of self-service analytics tools. Users that deeply analyze trends and need to view data from various perspectives also benefit from the intuitive slice-and-dice functionality, which traditional reports simply cannot satisfy. Whether you are a marketing analyst comparing performance across various ABM campaigns, or a CMO that’s looking at the departments’ revenue attribution, self-service analytics can help marketers make more informed strategic and investment decisions.

Other key use cases for self-service analytics include sales & GTM, revenue operations, supply chain, human resources, finance, executive insights, and other functions that depend on data to run the business.

Best practices for effective self-service analytics

Organizations with large and messy datasets must consider what elements of their data are most relevant to end users. In designing an ideal self-service analytics environment, it’s always best to start small and focus on specific use cases, rather than trying to configure and expose all of an organization's data at once. A simple approach is to identify an important use case where users have extensive data demands. Start by collecting feedback on the most important metrics and reports used by that department. You can also identify which are the toughest questions users are struggling to address manually. Simplified or curated views can expose highly accurate datasets to the business while not requiring additional data engineering efforts.

Challenges and risks in adopting self-service analytics platforms

Developing a self-service analytics environment at an organization is not an overnight project. Data leaders must first identify the key use cases and ensure that the right data is being captured to address end users questions and interests. Additionally, if the data is not structured and joined appropriately, users may receive invalid results. Leading modern analytics platforms such as MachEye automatically discover the data schema and create a data catalog, allowing administrators to capture a knowledgebase of commonly used terms and even custom metrics. The majority of failed self-service analytics initiatives are due to complex configuration requirements, insufficient knowledge of the data domain, and long onboarding processes. MachEye is able to reduce these activities down to a few hours through AI and automation.

Can a self-service analytics platform help solve your goals in 2022?

While reports and canned dashboards can certainly address some basic use cases, most modern organizations agree that getting dynamic data into the hands of users is vital in running a successful business in 2022.

If your organization has a bottleneck of data demands from internal and external customers, implementing a self-service analytics platform can reduce this burden tremendously. Decreasing trivial ad-hoc requests to IT can significantly free up high-value resources while enabling the business to operate more efficiently.

Organizations that have structured data in a cloud or on-premise relational data store can quickly gain value from a modern analytics platform like MachEye without needing expensive consultants or an army of developers. Business leaders looking to offer proactive insights generated by machine learning models and the ability to converse with data can depend on MachEye to deliver a fast and intuitive experience.

MachEye is an innovative augmented analytics platform that offers intelligent search, AI-powered insights, and root-cause analyses in a simple interface for every business user.