Faced with rapidly changing customer demands and digital-driven experiences, today’s business leaders walk a tightrope where they must quickly acquire information to drive outcomes and improve performance.
Unfortunately, the 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 given, market conditions may have changed, making it impossible to make the appropriate decisions at the right time. If only these users had self-service tools to quickly access the data and create ad hoc explorations and interactive reports or dashboards to get the insights they needed to solve business problems on hand.
Self-service BI, data visualization and analytics tools allow business users to autonomously prepare data and create and share analytic content in a timely manner, offering faster time to value and higher business user adoption over the traditional IT-led BI approach. To gain flexibility, more departments and business units are bypassing IT and embracing self-service BI and analytics to cover more users and use cases. But this growth also presents challenges as organizations move from limited to broader use (e.g. more users, different workloads, production use, multiple departments or enterprise wide use).
As more business units and users become self-sufficient, information silos will proliferate and demand for consistency, reuse and transparency will increase. And this can lead to chaos and confusion between IT and business regarding ownership, creation and sharing of data and analytic content. So it’s important for organizations to think through topics such as flexibility and governance, speed and control, and bottom-up and top-down content creation. While some of the friction is related to politics and behaviors, the need to govern the BI and analytics content will be important for it to be trusted and used in a repeatable manner for making valuable decisions.
IT may try to get in the way of business users and bring back the rigidity and restrictions. It will only exacerbate the situation and compel business users to go back to using their spreadsheets or their own island of self-service BI. Hence, successful organizations will find it worthwhile to balance flexibility and governance.
Partnership between business and IT
To achieve this balance, it’s critical to build a partnership between business and IT. IT must focus on addressing common reporting and analytics requirements and administering policies, while business users can quickly mash up data and create reports, dashboards or analytics content to meet their specific business needs. IT must understand that governance is not all about rigidity, but also about user enablement. On the other end, business teams must understand that without proper enablement or guide rails, self-service BI and analytics could lead to chaos. IT and business users can move forward with help not only from technology, but also the people and process dimensions.
From a technology standpoint, a three-step approach will be useful:
- Monitor: BI on BI is the critical step toward establishing a governance paradigm. Understanding the content creation, sharing and usage metrics will help to decide which ones you should use. Monitoring how often a data set is used, how often a report or dashboard is used, how frequently the requirements of a report are changing, etc., will help you understand the current use, enable users to become more productive and 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 data, users, groups, permissions, etc., can avoid chaos with use of self-service BI and analytics. Finding redundant content like tables, reports and explorations; informing users that somebody has already loaded data from similar or same sources; and making users aware of how to avoid creating many rarely used assets and how quickly they become out-of-date goes a long way in creating consistency and promoting reuse.
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 or lose interest in it altogether. To mitigate impact, the role and responsibilities of the analytics team need to evolve. It should be more about offering validated access to data, promoting best practices, triage common requirements, etc., and less about creating and authoring content.
Rules of engagement
Even though BI, data visualization and analytics technologies have become easier to use and self-service oriented, an organization must define end user roles clearly, assess the analytics maturity level of business units or teams; and then decide on proper training, levels and regimen. Empower users with the proper training for different skill levels and let users decide the level they need. Training effectiveness will depend on a variety of factors including experience, knowledge levels, training methods, validation approaches, individual competency level, etc. Some organizations are getting creative and using achievement of different certification levels as means to provide access to advanced capabilities (e.g., predictive analytics, data preparation) included in the BI and analytics tools.
Organizations also need to align their BI and analytics content governance processes with broader data governance and metadata management initiatives for better accountability and transparency. New data discovery and analytics solutions will create insights that may not align with existing BI and reporting solutions, or it will cause inconsistencies with insights from different teams. Hence business and IT must collaborate to set policies, rules and accountability to monitor use, sharing, reuse and retiring of data and analytics content. Relevant staff will have to be assigned to monitor, understand, explain and avoid overlaps, gaps and inconsistencies between business-user build analyses.
Transparency is important for any organization to build a culture of innovation and participate 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. In the end, 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.
After all, the goal is for everyone to make it safely to the other side with better outcomes and value from their BI and analytics initiatives.