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TDWI Upside - Where Data Means Business

Making Analytics the Catalyst for Change

How managing change will help self-service analytics deliver value faster.

From the executive suite to the front lines, decision makers are demanding the power to use data effectively on their own, unrestrained by IT limitations. Every day they are experiencing situations that demand new ways of responding; they need data insights to help them win the case for making changes to business processes and behavior. It is no surprise that investing in self-service business intelligence (BI) and analytics tools and services remains a top priority for most organizations according to TDWI research. The desire for self-service tooling is a prominent driver behind adoption of software-as-a-service (SaaS) products as well as supporting cloud-native data warehousing and data integration.

For Further Reading:

Five Drawbacks to Self-Service BI

Q&A: An Introduction to Self-Service Data Prep

Modern Metadata Management

With new software capabilities at their fingertips, users are excited to jump in and get started. The self-service trend has introduced thousands of nontechnical business users to a new world of data analysis and visualization beyond what they've been able to do with spreadsheets and simple reporting. These users -- as well as business analysts, data analysts, and data scientists -- can employ self-service visualization, analytics, and data preparation tools to explore, blend, wrangle, transform, and enrich data.

Yet, too many discover the hard way that despite the tools' impressive ease of use, realizing full value takes more than just the technology. The difficulties users have accessing all the relevant data they need for analytics often transcend technical issues; they are as much (if not more) about reluctance to share data due to protective data ownership and a lack of trust in others' data.

Overlaying these issues is growing concern about governance and the protection of sensitive data such as personally identifiable information. Many IT leaders are worried that further data democratization driven by the use of self-service technologies will lead to uncontrolled data chaos and poor governance.

Solving these challenges requires greater focus on organizational culture, user adoption, change management, collaboration, and planning for the future. These topics were discussed in depth at the recent TDWI Strategy Summit in San Diego (August 19-20), which was devoted to analytics. The importance of culture was emphasized strongly in the Summit's case study presentations, panels, and expert talks.

Change Management and Analytics

One of the best talks was a case study delivered by Alex Mendoza, senior manager of data, planning, and reporting at Benjamin Moore & Co., a Berkshire Hathaway company and prominent manufacturer and retailer of paints and other coatings. Mendoza discussed the importance of addressing change management, which is about how organizations deal with issues that can thwart progress with analytics projects. The desired outcome of many of these projects is data insights that can inform improvements (that is, changes) to business processes, employee behavior, and how the organization engages with its customers and partners. As everyone knows, change is difficult. Mendoza advised attendees to think about change management and user adoption issues in the earliest stages of projects, not at end, which is all too common. His organization uses the ADKAR model, the centerpiece of a methodology developed by Jeffery Hiatt, an author and expert on change management and founder of the consulting and training firm Prosci. ADKAR is an acronym that spells out five goals that the methodology identifies for change management (borrowing here from a useful piece about ADKAR www.xxx.comat www.tallyfy.com/adkar-model/):

  • Awareness: Leading people to see the need for change
  • Desire: Instilling the desire for change
  • Knowledge: Providing employees with the information or skills they need to achieve change
  • Ability: Applying knowledge and skills to bring about change
  • Reinforcement: Making sure that people continue to use the new methods

Analytics, if delivered well visually and in context, can help personnel understand and adapt to change. Analytics would be particularly helpful in achieving ADKAR Knowledge goals by giving personnel information and insights into why the changes are important and what needs to be done to achieve change.

However, to ensure that the data insights are relevant to personnel, analytics projects should begin with a good understanding of the business outcomes desired and the questions that need answering to achieve the outcomes. This is not easy. Often, business context filters in piecemeal and in an unfocused way and project activity moves too quickly to choosing predictive models, machine learning techniques, data sets, and visualizations. In other words, the cart is put before the horse.

Several speakers, in particular Donald Farmer, principal of TreeHive Strategy, offered recommendations based on their experience for how business leaders, business analysts, and data scientists could be more systematic in categorizing the types of challenges organizations face, the outcomes they may want to achieve, and then the questions that analytics could address.

Self-Service Analytics is Itself Changing

Change management and making sure to launch projects with a solid idea of business wants and needs are important considerations whether projects are driven by data scientists or by business analysts and users working with self-service tools. Technology trends suggest that the relationship between the tools and users' daily decisions and actions will grow tighter. Tools will continue to get easier to use, with more intuitive and graphical workspaces. This democratization based on ease of use will require continuous reappraisal of how projects are managed, who is involved, and how organizations find the right balance between stability and change.

To close this column, here are three trends that are changing self-service analytics:

  • Proactive automated insight delivery. More tools will apply AI, visualization, and notification capabilities to bring users data insights that fit their roles and how they personalize their workspaces, including on mobile devices. Analytics and machine learning development projects will have to envision how users will apply insights in the course of daily decisions and actions.

  • Virtual, augmented reality and voice-enabled interaction. Voice-controlled assistants such as Amazon Alexa and Apple Siri are in mainstream consumer use but not yet for BI and analytics. However, organizations could soon move faster into virtual, augmented reality, where data and analytic insights are layered on top of live views or spoken to users as they repair equipment, inspect inventories, tend to patients, enforce laws, and more. Analytics will need to fit with these types of interactions and be tailored to the users' context.

  • Dependence on metadata and semantic data management. To search for and find diverse data quickly and comprehensively, self-service analytics will need access to virtualized middleware and/or repositories full of knowledge about the data. Using AI and machine learning to enable organizations to develop and update these resources more automatically and inclusively is an important trend. Self-service analytics will require easy access to and integration with more advanced metadata and semantic data management systems to improve access and visualization of all relevant data.

These trends should help ease users' frustrations that often temper their initial enthusiasm for using self-service analytics. However, without attention paid to organizational and process issues such as change management, advances in analytics, visualization, and information delivery will fall short of realizing their potential.

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.


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