Self-Service Analytics: Strategies and Best Practices for Success

Sundeep DawaleJune 9, 2022

Successful companies depend on data-driven decision-making to thrive amidst constantly-changing business dynamics and ever-increasing competition. Key decision-makers rely on analytics—the ability to gather, analyze, and interpret analytical information in all functions of the business—to get the right information at the right time.

Self-service analytics is a cost-effective and efficient way to do this at scale. It allows line-of-business professionals to perform queries and generate analytics reports on their own to save time, cut costs, and enable faster decision making. However, many companies fail to successfully implement and maximize self-service initiatives, with adoption rates continuing to be low. In fact, according to a study by The Business Application Research Center, the average self-service analytics adoption rate is only 17%. This means that less than 1 in 5 people who have access to self-service data analytics tools are using them.

InfoCepts has partnered with many global clients and helped them realize successful self-service initiatives. We recommend these best practices to ensure the successful implementation of self-service analytics.

  1. Understand your target self-service audience and their needs.

    Self-service analytics means different things to different users. Business analysts, data scientists, citizen data scientists, and information consumers have varying needs and expectations. For instance, data scientists require an environment to develop models in R, articulate insights and results with rich visualizations, and test models. In contrast, information consumers only expect to be able to see and interact with pre-defined reports for common queries.

    A complete self-service solution should provide diverse capabilities for your users. Go over your end-user community’s data maturity, key objectives, and personas before advancing your self-service abilities.

  2. Build the right approach to scale up self-service capabilities.

    Self-service analytics deployment is not just about providing interactive data visualization tools. Enterprise-scale execution calls for awareness, agility, alignment, acceleration, and collaboration between analysts, users, and IT specialists for business acumen.

    InfoCepts recommends designing purpose-built solutions instead of using a one-size-fits-all approach. We also recommend shifting to a business-oriented mindset, ensuring collaborative governance, and setting up sustainable operations.

  3. Implement a holistic plan

    Several elements are necessary to ensure successful transformation at an enterprise level, including having leadership alignment and a clear understanding of essential capabilities. InfoCepts uses a self-service analytics implementation framework that removes silos, provides a process-centric approach, and encourages working as one team. At its center is the E-2-E model, which stands for Experience, Education, Engagement, and Enablers.

    Interested in learning more? Download our e-book to learn about the five questions to ask before starting your self-service analytics journey, best practice approaches to implementing self-service analytics and real-life use cases.

Talk to InfoCepts today to get started on self-service analytics.