Click here to learn more about author Andy McCartney.
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:
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.
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.
Even when the ingredients for a digitally-savvy, data-driven organization are in place—best-of-breed BI software, a great IT department, a team of data analysts, and forward-thinking executive leadership—raising an organization’s digital IQ via self-service BI won’t happen on its own.
The key to addressing this issue is analyzing the way in which information is shared. The most exceptional companies are those best situated to disseminate information across the enterprise. This means leaving behind the approach of the past, in which BI was sequestered to the offices of the Data Analysts and dispensed without much insight into what information knowledge workers and other business and non-technical users needed.
Why self-service BI benefits organizations
The primary benefits of self-service BI tools are independence and autonomy from IT, agility and smarter decisions, and strategies via data-driven business.
In order to do a true BI application, other facilities are needed. Among the capabilities a BI tool should include are:
- Full meta-data envelop to help automate processes
- Push as well as pull distribution (email vs browser)
- Push with custom data for each recipient
- Direct access to full data base not extracts for up-to-minute information
- Non-stop operation
- Anti-hacking facilities
- Complete user security permissions to allow variety of user populations
Deciding on an approach
Self-service BI is a powerful tool, but underutilized because many companies aren’t spending the resources to ensure that information is accurate and reliable, regardless of its format or source.
Any self-service BI environment depends upon Enterprise Data Quality—a critical yet elusive goal for many organizations. In order to ensure Enterprise Data Quality, companies must develop comprehensive rules, policies, and procedures to eliminate errors, mistakes, duplication, and inconsistencies in their back-end systems and sources. They must also implement cutting-edge technology tools to facilitate the ongoing execution and enforcement of those guidelines. Companies of all sizes, across all industries, are struggling to achieve and maintain Enterprise Data Quality.
Adding to the confusion is the fact that there is a growing awareness among organizations that self-service data visualization tools do not equate to Data Quality. At the same time, an increasing number of industry analysts are raising concerns about the dismissal of Metadata within some self-service BI software.
As such, organizations must implement a Data Governance strategy in tandem with other initiatives to ensure the success of self-service BI. As more workers rely on enterprise data, companies without Data Governance plans risk providing inaccurate information that could cripple the business.
Perhaps the most important component when evaluating a self-service BI tool is how it fits into the overall enterprise Data Quality initiative. After all, it is the technology tools that drive the strategy forward. The right solution will offer a wide array of capabilities—from data profiling, validation, and cleansing through enrichment and Data Governance—to make information as timely and trusted as possible.