Balancing Self-Service, Governed BI, and Analytics
Business users and IT can improve their working relationships by following these rules of engagement.
- By Tapan Patel
- September 25, 2017
Faced with rapidly changing customer demands and digital-driven experiences, today's business leaders walk a tightrope: they must quickly acquire information to drive outcomes and improve performance or risk falling behind their competitors.
Unfortunately, business users' need for fast and informed decisions is hindered by long waits from IT due to inflexible business intelligence (BI) and reporting tools. By the time insights are produced, market conditions may have changed, making it impossible to make timely decisions.
Self-service BI, data visualization, and analytics tools allow business users to autonomously prepare data and create and share analytics results more quickly, offering faster time to value and higher user adoption than the traditional IT-led BI approach. Enterprises are embracing self-service BI and analytics to cover more users and use cases, but this model introduces new challenges.
For example, as more business units and users become self-sufficient, information silos may proliferate even as demands for consistency, reuse, and transparency increase. This can lead to chaos and confusion between IT and business regarding ownership, creation, and sharing of data and analytics. Organizations must weigh the trade-offs between flexibility and governance and between speed and control. To be trusted, the BI and analytics content needs to be governed; users must be able to create consistent, repeatable results.
IT may try to get in the way of business users and bring back rigid restrictions, but doing so will only compel business users to go back to using spreadsheets or their own islands of self-service BI. To avoid this retreat, successful organizations need to balance flexibility and governance.
The Business/IT Partnership
To achieve this balance, business users and IT have unique roles to play.
IT must focus on addressing common reporting and analytics requirements and administering policies, realizing how and why business users quickly mash up data and create reports, dashboards, or analytics content. IT must understand that governance is not just about rigidity but also about user enablement.
For their part, business teams must understand that without proper enablement or guiderails, self-service BI and analytics could lead to chaos. Speedier analytics is of no value if a user cannot repeatedly produce consistent, accurate analytics they can share with confidence.
A Three-Step Approach
Technology can help your business users and IT build that partnership. A three-point approach is useful:
Monitor: BI about BI is critical for establishing governance. Understanding the content creation, sharing, and usage metrics will help you decide which metrics you should use. For example, monitoring how often a data set is used, how often a report or dashboard is accessed, or how frequently the requirements of a report change will help you understand the current use, enable users to become more productive, and help IT become efficient in governing BI content.
Measure: Quantifying the size, use, time spent, and diversity of the BI and analytics application helps you understand the behavior, adoption, and level of collaboration among different types of users. It is imperative to find out whether users are sharing and collaborating with the content and getting value out of the insights as they make decisions.
Manage: Providing a centralized mechanism to manage such elements as data, users, groups, and permissions can help an enterprise avoid self-service BI and analytics chaos. Finding redundant tables and reports, informing users when somebody has already loaded data from similar or identical sources, and teaching users how to avoid creating multiple rarely used assets (and how quickly they become out-of-date) goes a long way in creating consistency and promoting reuse.
Perspectives Must Change
Of course, people and processes are also important factors in the governed BI and analytics journey. Unfortunately, business teams are reluctant to involve IT from the beginning because they think users will lose autonomy and be required to jump through too many hoops, which might delay the project.
The role and responsibilities of the IT team need to evolve. IT should be more about offering validated access to data, promoting best practices, and triage of common requirements and less about creating and authoring content. Of course, the business users must step up to ensure that chaos does not reign. They must understand the responsible use of data and the need to create, analyze, and share insights in an agile and consistent manner.
Rules of Engagement
For business-led BI and analytics projects to sustain and be effective, business and IT leadership and staff must align and work together to avoid confusion or wasting budget and resources.
Business and IT must collaborate to set policies, rules, and accountability to monitor use, sharing, and reuse of data and analytics content. Relevant staff will have to be assigned to monitor and explain overlaps and inconsistencies between business-user-built analyses.
A BI/analytics center of excellence team (jointly staffed with business and IT professionals) should take on the responsibility of monitoring self-service environments to identify when BI or analytics solutions or usage strays from the established governance path.
IT should clearly define how (and which) relevant external data sets can be incorporated (e.g. access, security, traceability) into the BI and analytics environment. IT must set guidelines for promoting data sets and certifying their validity. This will help the business team be confident in its use of data.
In addition to training and certification, the BI/analytics center of excellence team should set up regular best-practice forums for business teams to share what they've learned about new insights, variables, and techniques. This will help different business teams guide each other and avoid duplicate efforts, and it will help IT understand how its role can evolve and which business teams need more (or less) support.
To jumpstart your self-service BI and analytics efforts, the business team should prepare a set of dashboard or report templates for relevant topic areas (e.g., sales, churn, credit risk) with proper business metadata, filters, and data sources identified. IT, in collaboration with business users, can monitor the creation of new insights and retire them when no longer needed.
Self-service BI and analytics solutions are usually managed and supported by the business team. If the team lacks the necessary skills and expertise, then they need to work closely with IT to develop a workable service-level agreement from the start of the project.
Transparency is important for any organization building a culture of innovation and participating in the analytics-driven economy. Organizing and governing all the assets and making them visible to the users should not be an afterthought in any self-service BI and analytics initiative.
IT and business users cannot wait for anyone else to better manage and govern the creation and distribution of insights. They must play the game together and enable business users to achieve agility while mitigating risks with adequate governance.
Tapan Patel is principal product marketing manager at SAS where he is responsible for global marketing for business intelligence and predictive analytics and SAS Visual Analytics and SAS Visual Statistics products. You can reach the author at [email protected].