Many companies’ experience of self service business intelligence (BI) is similar to the early days of agile development; companies were then drawn to a model that promised improved business-IT relations and expedited delivery cycles. This was particularly attractive to those businesses that struggled with user engagement and unmet delivery expectations. However, many did not realize that success did not come for free and without any effort on the part of the business. It still required greater user engagement, more accountability, disciplined decision-making and an ongoing commitment.

Self_Service_self_actualization_0Kimberley Nevala, director for SAS Best Practices Team, and writing for CIO.com, argues there are many parallels here with self-service BI. Teams that are struggling under “an avalanche of unmet BI needs are embracing the self-service mantra,” but those expecting a quick fix are quick to learnt the error of their ways.

A functional self-service ecosystem requires a well-considered strategy. This must include some of the following points.

One size doesn’t fit all: In the same way a company’s customer base represents a wide variety of segments with individual needs, so too, do the needs of self-service BI users vary. For example, executives may need access to different things than line of sight business users. Enabling a serf-service mechanism means a clear stratification of user types and delivery of a range of capabilities and solutions.

  1. Different SLAs: Each user group will have their own expectations and support requirements for any system. In turn, this drives a need for differentiated service level agreements (SLAs) and engagement models. Addressing new requests and resolving issues will, in turn, require discrete intake and prioritization mechanisms.
  2. Start with good foundations: Self-service needs good foundations to work. Starting with an intuitive and robust dashboard or report sandbox environment that helps to address the most common metrics, report dimensions, analysis and/or data. Users should then be able to customize and further modify – specific to their needs.
  3. Enablement trumps development: In the past, BI consisted of ‘units’ comprising reports, dashboards or in some instances a cube for analytics. A self-service model should shift this from the delivery of mere ‘widgets’ to full-scale access and exploitation of whole datasets. Training should be an ongoing task that utilizes multiple delivery paradigms: formal classroom training, ‘lunch and learns’, and user helplines for example. Team skillsets and roles may need to shift and change to training and support.
  4. Collaboration is vital: teamworking and networking is pivotal in the adoption of self-service viability. Collaboration should promote and harness the power of the collective by providing features that prompt users to: vet data and analysis and develop a common understanding of the data among other things.

Ultimately, self-service is a balancing act that – done right – can extend the visibility, value and adoption of BI and analytic solutions to the whole organization.

Big Data and related technologies – from data warehousing to analytics and business intelligence (BI) – are transforming the business world. Big Data is not simply big: Gartner defines it as “high-volume, high-velocity and high-variety information assets.” Managing these assets to generate the fourth “V” – value – is a challenge. Many excellent solutions are on the market, but they must be matched to specific needs. At GRT Corporation our focus is on providing value to the business customer.

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