Self-Service Analytics

What is self-service analytics?

Self-service analytics empowers people across an organization to discover, access, and analyze data without assistance. It’s a solution to the common challenge of becoming a data-driven organization with a strong data culture: getting data into the hands of those who need it.

Typically, a worker who needs data or analysis has to ask a data analyst or data scientist. That data worker gathers details from the requestor, ensures the requestor has permission to view the data, creates a query to retrieve the data, and then cleans, processes, and prepares the data. Finally, the analyst delivers the results to the requestor. Even this simplified representation of the process is complex. 

Self-service analytics eliminates everything between the requestors and the needed data by giving them easy-to-use tools to discover, understand, and retrieve data while adding controls, collaboration, governance, and guidance to ensure they have appropriate permissions and quickly find the best data for their needs.

Benefits of self-service analytics

It’s obvious that self-service analytics saves time for data consumers and allows data analysts and data scientists to be more productive and focus on more valuable tasks. However, it also benefits the organization by increasing trust in the data and related outcomes and bolstering a data culture. 

Here are several additional benefits of self-service analytics:

• Faster access to insights: Eliminating the manual processes and associated wait times required for traditional data analytics also eliminates the wait from when a need arises to when the data is available. Manual analytics can take hours or days, even in the best cases. Self-service analytics puts data and related metadata at the worker’s fingertips to speed up access and eliminate wait times. Forrester suggests that “real-time” data analytics should be measured in seconds or minutes.

• Overcomes data silos: When data is spread across applications, retrieving and combining it is difficult and time-consuming. Data in obscure, cloud, or “rogue” systems can be missed, creating gaps that lead to flawed analyses and poor decisions. Bringing organizational data into a centralized source of truth, like a data catalog, eliminates these silos and provides information to help users find the appropriate data.

• Increases data literacy, data democratization, and the benefits of a data culture: Separating workers from data diminishes their understanding of the data and the resulting analysis. Self-service analytics turns data into a valuable resource by breaking down those barriers, building a common data language, and encouraging teams to understand and use data more effectively.

• Increases confidence in data analytics: Trust in data and resulting insights diminishes when teams don’t clearly understand data’s source, meaning, and context. Self-service analytics provides information about the data, connections to subject matter experts, and details on past usage, as well as guidance on how the data is to be used so workers have more trust in the eventual results.

• Enhances data governance: Self-service analytics platforms automatically put data governance into action as they control data access, highlight related policies, and provide added security for sensitive data.

Best practices for self-service analytics

Moving to self-service analytics is a valuable effort that has many benefits. It’s an enhancement that many organizations have done successfully. The following are proven best practices for self-service analytics.

1. Define the goals, determine which data sources to include, solicit ideas, and gather expectations from the users of self-service analytics to craft a strategy and plan. Also, consider security, privacy, and other data-related accommodations.

2. Select a platform for self-service analytics that fits the needs defined above. “Self-service” implies intuitive and easy-to-use yet highly secure with effective guardrails. Modern solutions include artificial intelligence (AI) capabilities for natural language data discovery, automated data curation, and more. Data catalogs are a proven solution for self-service analytics.

3. Start small and scale as workers gain confidence and become more familiar with the new self-service analytics platform. Consider starting with one department to limit the initial scope of data, sources, and users.

4. Incorporate data governance standards, policies, and rules into the platform and processes supporting self-service analytics.

5. Identify, catalog, and connect additional cloud, on-premises, and other data sources to scale across the enterprise and meet the entire organization’s needs.

6. Understand that many inexperienced data consumers will still require assistance, so bring data stewards into the process to assist and encourage.

Self-service analytics and data governance

Self-service analytics makes data accessible to workers across the enterprise; however, unrestricted access can have security and compliance implications. Most self-service analytics solutions provide built-in data governance capabilities to control access to data, enforce policies for data quality and usage, and provide auditing tools to log when and by whom data is accessed. 

Leading platforms infuse data governance into self-service analytics to empower data consumers without increasing risk. Good data governance ensures organizations always understand how data is being used, again highlighting the importance of a data catalog for self-service analytics.

Alation: Self-service analytics at everyone’s fingertips 

Alation makes it easy for teams to find, understand, and trust data to increase analyst productivity, create business agility, and improve decision-making.

Key features include:

• AI-driven semantic, behavioral, and keyword search, with over 100 connectors to common data sources.

• Data-driven collaboration using descriptions, usage, and popularity to ensure the right data is put to work.

• Seamless data access to governed data in the tools workers already use, such as Excel, Google Sheets, Microsoft Teams, Slack, Tableau, and more.

• Tools to publish, share, and reuse data and powerful Intelligent SQL editor to assist with query writing.

Next steps: Learn more about self-service analytics

Dive deeper into self-service analytics and why a data catalog is the best solution by using the following resources