For many companies, the self-service business intelligence (BI) experience is similar to the early days of agile development. Back then, companies flocked to agile methodologies, drawn by the promise of faster delivery cycles, improved business-IT relations and expedited delivery.
The method was particularly attractive to those struggling with business user engagement and unmet delivery expectations. What many failed to recognize was this: agile required greater user engagement, more accountability, disciplined decision-making and an ongoing commitment. Not to mention faster, more responsive development. Delivery cycles were shorter and, when managed well, arguably more on point. But success didn’t come free.
In the same vein, BI teams struggling under an avalanche of unmet BI needs are embracing the ‘self-service’ mantra. Those expecting a quick fix quickly learn the error of their ways.
Self-service aims to make the users’ BI experiences more robust and timely. But self-service doesn’t equal self-enabled. BI teams are not off the hook. They have a critical role to play in enabling a functional self-service ecosystem. A well-considered strategy must address the following truisms:
* One Size Does Not Fit All – Just as your customer base consists of different segments with discrete needs, BI users are a diverse bunch. For executives, self-service may mean access to corporate scorecards on an iPad and (or) the ability to “Ask Jeeves” on demand. For line of site business users the best BI is unseen BI. Specifically, reports and alerts that are integrated into – not distinct from – operational applications and workflows. Knowledge workers including the emerging ‘data scientist’ require access to robust discovery tools and broad data sets. Therefore, enabling self-service requires clear stratification of user types and delivery of a range of capabilities and solutions.
* Different User, Different SLA – Each user segment comes complete with their own expectations and support requirements. This in turn, drives the need for differentiated service-level agreements and engagement models. Not to mention discrete intake and prioritization mechanisms for addressing new requests and resolving issues.
* A Blank Slate is A Bad Slate – As a general rule, we’re all better critics than authors. Self-service works best when users are given a good foundation upon which to innovate. Start with an intuitive, robust dashboard or interactive report or sandbox environment which addresses the most common metrics, report dimensions, analysis and/or data. Then allow users to modify and customize to create variants specific to their needs.
* Enablement Trumps Development – Historically, the unit of BI delivery was a report, dashboard, or perhaps a cube for analytics. Self-service shifts this paradigm from the delivery of a BI “widget” to delivery of data and toolsets to access and exploit that data. Frequently overlooked, but critical to making the turn, is user education. This is far from a once-and-done endeavor. Training and enablement is an ongoing task utilizing multiple delivery paradigms. Formal classroom training. Lunch-and-learns. On-demand user helplines. Enabling self-service is a full-time job. BI teams are not off the hook: the unit of delivery has just changed. Team skillsets and roles may need to change accordingly.
* It Takes a Village – Collaboration is pivotal to self-service adoption and viability. Capitalizing on social media techniques, collaborative BI environments create communities of practice that become their own support system. Collaboration promotes and harnesses the power of this collective by providing features that prompt users to:
Vet data and analysis among themselves
Develop a common understanding of shared data
Solicit input on the cause and potential impact of key findings
Capture and track the effectiveness of actions taken based on reported findings
* It’s a Balancing Act – By their very nature, self-service environments allow for the creation of an even more-confusing morass of disparate reports and analysis. The flip side is that appropriately managed environments, with their focus on collaboration, naturally encourage consensus and, ultimately, consolidation. Finding the right balance requires good governance relative to the sharing and publication of information. Particularly for official corporate reporting, outputs disseminated to third parties such as customers or suppliers, and those reported publicly for regulatory compliance purposes.
Ultimately, self-service is the output of a robust BI and analytics strategy. Done right, self-service can extend the visibility, value and adoption of BI and analytic solutions. Done poorly self-service can exasperate an already disgruntled and overwhelmed user community.
Kimberly Nevala is a director on the SAS Best Practices team. She specializes in strategies for BI, data governance, and master data management programs, and conducts client workshops and industry presentations in these areas.
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