Self-Service Data & Analytics
What it is, how to support it, and why it is inevitable
Self Service in Data & Analytics is an individual process depending on different factors. Indeed I differentiate between 3 levels:
Self-Service Analytics is aimed at the use of front-end solutions to create reports, analyses, visualizations or data exports for use by business users in their daily work
Self-Service for Data Preparation means that central data is integrated with proprietary data and the necessary transformation and harmonization of the data is carried out independently. The result is not a data product, but can (preferably) be mapped on the data platform.
Self-Service for Advanced Analytics comprises solutions that use algorithms, pattern recognition, statistical methods or artificial intelligence to change and provide data. In particular, legal framework conditions (especially the EU AI Act) must be observed.
👉 Self-Service just happens in many companies. It is how business people work with data without asking IT or a central department. How does your Self-Service look like?
🔢 We have to be aware that like in the following image, Self-Service is often more than building a Dashboard or compiling numbers in a spreadsheet. To understand the end-to-end process helps to tackle challenges and support data usage.
Fig.1: Example of a Customer Self-Service Process
🚫 Some would think - all reports and data work should come from a central unit, with a unified understanding and central governance. But this is typically not working, as it is often against end users needs.
🏋♂️ Be aware of typical challenges like shown beneath for every step, the user could face. It is not that they don't want to align with company standards but rather they need ways to do their business.
Fig. 2: Challenges and obstacles of Self-Service Data & Analytics
Foster Self-Service in your company
Different factors can help to align Self-Service and foster data usage and reduce obstacles for creating value from data:
✔️ Establish Self-Service principles by learning from the actual usage
✔️ Provide data that people understand and can access via a capable data platform and a Data Catalog (without making things to complicated)
✔️Care about Data Governance and data quality to foster an end-to-end responsibility and trusted data
✔️Consider to support Self-Service Data Preparation with the right tools and standards
✔️Offer Data Literacy trainings and establish Data Communities
✔️Invest in AI Literacy and make AI accessible in the solutions they already use
Data Literacy - is the ability to read, understand, create, and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data.
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AI Literacy - is the ability to understand, use, monitor, and critically reflect on AI applications.Conclusion
Self-Service in Data & Analytics is inevitable. People will do it, and they will do it more and easier as the demand is rising, more data will be available than ever and technological progress and AI will lower the bar to make it work.
What is your experience and success factors for Self-Service Analytics?
Remark: I first published a reduced version of this article on LinkedIn.