Self-service BI Success Depends Upon Data Quality & Governance

Self-service BI Success Depends Upon Data Quality & Governance

Self-service BI Success Depends Upon Data Quality & Governance There are many benefits that can be derived through the implementation of a self-service Business Intelligence (BI) system. For example, non-technical professionals can generate their own reports, run queries, and conduct analyses, without the assistance of IT staff.

Non-technical workers can make faster, better decisions because they no longer have to wait during long reporting backlogs. At the same time, technical teams will be freed from the burden of satisfying end user report requests so they can focus their efforts on more strategic IT initiatives.

In order for self-service BI environments to be effective, they must be extremely intuitive and user-friendly. The majority of today’s business users simply don’t have the skills or technical savvy to work with complex tools or sophisticated interfaces. A self-service BI application will only be embraced by its intended audience if it gives them a means of simply accessing customized information, without extensive training.

Here are three factors for organizations to consider before they implement self-service BI for their user base:

Read Also:
Business-Driven Data Strategy: A Focused Imperative

Agility: Business users can’t afford to wait for IT or BI specialists to deliver apps, reports, and insights. The window of opportunity to act upon credible intelligence may be missed, so a self-service BI tool should provide non-technical users the ability to access governed data sources and explore the data themselves.

Bandwidth: There has been a gradual increase in the backlog of requests for a wide variety of intelligence, from reports to charts, to dashboards, to visualizations. Business users, whether analytic rookies or data savvy analysts, can now fulfill some or all of their own intelligence needs. Also, the growing volume and variety of information that results from increasingly complex business transactions, third-party sources, and social media channels has created bad data that lives deep within legacy systems.

Personnel: People play an important role in an Enterprise Data Quality initiative. Because it is a widespread and ongoing effort, a successful enterprise will have a solid Data Quality strategy that has sponsorship and support at the executive level. IT staff, as well as the business people who actually consume and use the data in question, must also be very closely involved throughout the process. Additionally, Data Stewards must also be designated so those who will be held accountable for preserving data integrity are fully aware of their responsibilities.

Read Also:
Moving beyond spreadsheets into enterprise-ready statistical analysis

Organizations can gain keen insight into the state of their data by completing an iterative profiling process, thereby avoiding unmitigated risk, bleeding costs, and lost revenue.

Read Full Story…

Leave a Reply

Your email address will not be published. Required fields are marked *