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Executive Summary | BI and Analytics in the Age of AI and Big Data

Executive Summary for the TDWI Best Practices Report: BI and Analytics in the Age of AI and Big Data

To drive higher value from data and analytics, organizations are anxious to capitalize on advances in artificial intelligence (AI), big data, and cloud-based services—trends that will reshape how organizations set their BI, analytics, and data architecture strategies. In this Best Practices Report, TDWI research shows strong interest in using these technologies but also concerns about data governance, data quality, user satisfaction, and having the right skill sets to move forward.

The key areas of investment focus are not unexpected. About three-quarters (74%) of organizations want to invest in new technologies to improve operational efficiency and effectiveness, which have long been leading objectives for using BI and analytics. However, rather than stay with existing technologies and platforms for business intelligence (BI) reporting, analytics, and other use cases, the majority of organizations want to either augment or replace them. Organizations want to accelerate their pursuit of operational excellence through use of new technologies and cloud-based solutions.

Organizations also want to focus technology and services investment on increasing self-service BI and analytics capabilities. As organizations determine how to adopt AI and big data technologies, they will be interested in how these new technologies and practices can contribute to empowering a burgeoning community of users to move beyond simple data consumption and do more reporting, data exploration, data preparation, and analytics on their own with less IT involvement.

One of the most difficult aspects of BI and analytics democratization is how to govern and manage it. Many organizations fear a "Wild West" if they let self-service technologies spread too far and fast. Beyond obligations to comply with regulations demanding protection from unauthorized use and exposure of sensitive data, concern about how to properly govern and manage the expansion of self-service technologies is another driver behind strong interest in data governance. TDWI research finds that governance and data quality challenges are barriers to adoption of new technologies such as AI as well as to greater user independence from IT.

Another barrier is the deficiency in skilled personnel. This is always an issue regarding adoption of new technologies. However, innovations made possible by AI and other means of increasing automation will themselves help organizations address skill gaps by providing "augmented intelligence" capabilities that automatically guide and recommend data sets, visualizations, analytics models, and follow-up questions to ask of the data. AI will improve the speed and efficiency of search and natural language-based exploration of big data. Still, technology alone won’t be enough. Organizations need to assess requirements for building skills internally or hiring and contracting personnel who can help them achieve the next generation of BI and analytics objectives. Arcadia Data, Hitachi Vantara, OpenText, Oracle, and ThoughtSpot sponsored the research and writing of this report.

About the Author

David Stodder is senior director of TDWI Research for business intelligence. He focuses on providing research-based insights and best practices for organizations implementing BI, analytics, data discovery, data visualization, performance management, and related technologies and methods and has been a thought leader in the field for over two decades. Previously, he headed up his own independent firm and served as vice president and research director with Ventana Research. He was the founding chief editor of Intelligent Enterprise where he also served as editorial director for nine years. You can reach him by email ([email protected]), on Twitter (, and on LinkedIn (

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