To Become Data-Driven, Focus on User Empowerment
Many organizations face obstacles on the path to becoming data-driven. What strategies are working for your enterprise?
- By David Stodder
- October 23, 2017
To compete in an economy where margins can be thin, customers fickle, and the ability to act at the right moment critical, organizations need to derive full value from data resources and expand the role of analytics into all types of decisions. Achieving a high level of confidence in data and analytics is the essence of becoming data-driven and data-informed.
Although the goal sounds pretty universal -- what organization today would not want to be data-driven? -- in reality, organizations face significant obstacles. In a survey conducted at TDWI's Conference and Leadership Summit this past August in Anaheim, California, the majority of the 132 attendees who responded to the survey gave their organizations middling grades for how close they were to being data-driven.
TDWI will examine this issue in greater depth in an upcoming Best Practices Report, to be published at the end of the year. My colleague Fern Halper and I will develop research-based recommendations for how organizations can improve processes, analytics, data management, governance, and technology strategies to quickly enhance the role of data and analytics in decision making.
Our research survey is currently live and we invite you to participate. We would value your input.
In most organizations, progress toward becoming more data-driven or data-informed is uneven. For example, there could be isolated departments or individual projects at the leading edge where users are working effectively with data and analytics to drive decisions. However, the rest of the organization might be getting by with haphazard data access, canned reporting, and data analysis dominated by personal spreadsheets, none of which is fundamentally changing how decisions are made.
Self-Service to the Rescue?
The arrival of easier to use, visualization-oriented technologies for business intelligence (BI) and data discovery has changed expectations about the role of data and analytics in decision making. Users have long been frustrated by the wait for IT deployment of BI, only to find that applications developed are inflexible and built primarily for managed query and reporting rather than flexible, ad hoc analysis. Self-service solutions allow users to do more on their own, including selection, preparation, and blending of data. The solutions enable users to choose their own path with BI and analytics rather than having to depend on IT; BI becomes less of an IT project and more of a business-driven experience for users.
Self-service BI and visual analytics solutions make it easier for users to start with the business questions they want to answer and make those the context for data analysis and visual discovery. They waste less time hunting through static reports and spreadsheets or having to learn the intricacies of the data architecture to find what they need. However, just giving users tools is not going to get organizations to the data-driven promised land. Users need to work with IT to expand, protect, and sustain what they are achieving on a smaller scale with the tools.
In many organizations, deployment of self-service solutions has led to tension between IT and business users over data access, data ownership, and the development and execution of analytical models. Some of the friction is about politics, but many of IT's concerns are valid. Democratization of BI and analytics has the potential of introducing data chaos. Problems with the data, including lack of trust in its quality, will push organizations further from data-driven goals, not closer.
Along with addressing data quality and trust in the data, IT can also facilitate user training and skills development. Our survey of TDWI Anaheim attendees found lack of skills to be the most common barrier to increasing users' self-reliance (see Figure 1).
Figure 1: Based on answers from 130 respondents to the TDWI Technology Survey conducted at the Anaheim TDWI Conference and Leadership Summit. Respondents were asked to select their top three most significant barriers.
Governance: Critical to Being Data-Driven
Figure 1 shows that governance, security, and privacy concerns are the second most prevalent barrier to self-reliance and reducing dependence on IT. Strong governance and related data stewardship practices are increasingly regarded as essential for avoiding the potential chaos caused by the spread of self-service technologies, not to mention ensuring adherence to regulations covering data use.
Governance comprises multiple objectives. First, it is about giving the right users access to the right data, while protecting sensitive data. Data preparation and hygiene are also key, particularly to improve and safeguard data quality. Governance and data stewardship are also about promoting efficiency, consistency, and reuse of analytics models and processes, including the management of champion models. Finally, governance and stewardship practices must oversee the content that is produced by end users, such as dashboards and visualizations, so that they adhere to standards and reduce, rather than spread, data chaos.
Think Empowerment, Not Just Independence
Ultimately, governance and self-service should fit together in a strategy to empower users with the ability to apply data and analytics to solving business questions. It is not enough to supply users with self-service tools so they can be independent of IT. Increasing users' freedom is vital, especially if their starting point is little or no independence from IT. However, users also need what IT can supply in terms of structure, governance, and facilitation of training and skills development.
Governance and self-service should not be opposing goals that increase tension and pull an organization apart; they are both vital. Focusing on empowerment, not just self-service, will help business users see the value in investing in governance, data architecture, and infrastructure. These will help users overcome barriers and become more data-driven while also safeguarding and improving the quality of the organization's data.
About the Author
David Stodder is director of TDWI Research for business intelligence. He focuses on providing research-based insight and best practices for organizations implementing BI, analytics, performance management, data discovery, data visualization, and related technologies and methods. He is the author of TDWI Best Practices Reports on mobile BI and customer analytics in the age of social media, as well as TDWI Checklist Reports on data discovery and information management. He has chaired TDWI conferences on BI agility and big data analytics. Stodder has provided thought leadership on BI, information management, and IT management for over two decades. He has served as vice president and research director with Ventana Research, and he was the founding chief editor of Intelligent Enterprise, where he served as editorial director for nine years.