Executive Summary | Evolving from Traditional Business Intelligence to Modern Business Analytics
Executive Summary for the TDWI Best Practices Report: Evolving from Traditional Business Intelligence to Modern Business Analytics
- By David Stodder
- September 25, 2020
Demand is rising for business analytics—for data interactions that go beyond simple reports and dashboards to deliver sharper insights that explain why, how, and what is likely to happen in the future. Data-savvy executives and managers who question long-held, tradition-bound assumptions need business analytics so they can develop innovative ideas based on a thorough understanding of all relevant data. They want to use visualizations to analyze data relationships from across sources and examine data from different perspectives to reveal an unexpected pattern or trend. Decision makers in all industries need good business analytics tools and an agile data stack to respond to unexpected changes such as those brought on by the coronavirus pandemic.
Analytics began as a specialists’ art, but over time self-service solutions have been incorporating more analytics functionality and democratizing the pursuit of deeper data insights. Expert data scientists and data analysts will always play a key role in complex and cutting-edge projects, but today, “citizen” data scientists and data professionals are excited to have tools and cloud services at their disposal to go beyond standard business intelligence (BI).
This TDWI Best Practices Report focuses on the evolution from traditional BI and data management to more modern business analytics based on an updated data stack increasingly based on cloud platforms. The report discusses tools and practices necessary to create satisfying user experiences, including improved data integration, cataloging, and management. The report also looks at organizational priorities for higher data literacy and effective governance.
One of the most important trends is how technologies are augmenting human data interaction with artificial intelligence (AI). Techniques such as machine learning and natural language processing are improving the scale, speed, and accuracy of business analytics. AI-guided automation is reducing delays and streamlining steps that have traditionally been dominated by manual work. However, amid the excitement about AI, our research underscores the importance of providing tangible benefits to users. The majority of research participants surveyed, for example, would like to see AI-driven insights integrated with their daily dashboards and key performance indicators (KPIs).
Business analytics cannot progress without the right strategy regarding data integration, data catalogs, data management, and governance. Data quality is a key focus—and ongoing challenge—for most organizations surveyed. Our research finds that enabling users to access or view valid, trusted new data within the time frame they need it is a challenge. Organizations acknowledge the value of data catalogs, glossaries, and metadata repositories in improving user productivity and streamlining data preparation and pipeline development. However, without modern technology, most continue to struggle to establish an accessible and accurate knowledge base of information about the data.
Governance is essential for safeguarding sensitive data, adhering to privacy regulations, and improving accountability for how data is used, analyzed, and shared. However, organizations surveyed still express uncertainty about whether they can govern, secure, and trust the data used in business analytics.
This TDWI Best Practices Report offers recommendations for ensuring that the evolution toward modern business analytics is successful.
Alation, Denodo, and Wyn Enterprise by GrapeCity sponsored the research and writing of this report.
About the Author
David Stodder David Stodder is an independent data and analytics industry analyst. Previously, he was senior director of research for business intelligence at TDWI, where he spent more than 13 years. Stodder focuses on providing research-based insights and best practices for organizations implementing BI, analytics, AI, data intelligence, data integration, and data management. He has been a thought leader in the field for over three decades as an industry analyst, writer, and speaker. He was the founding chief editor of Intelligent Enterprise where he also served as editorial director for nine years. Stodder is a TDWI research fellow.