Executive Summary | Accelerating the Path to Value with Business Intelligence and Analytics
- By David Stodder
- June 29, 2017
With decisions riding on the timeliness and quality of analytics, business stakeholders are less patient with delays in the development of new applications that provide reports, analysis, and access to diverse data itself. Executives, managers, and frontline personnel fear that decisions based on old and incomplete data or formulated using slow, outmoded, and limited reporting functionality will be bad decisions. A deficient information supply chain hinders quick responses to shifting situations and increases exposure to financial and regulatory risk—putting a business at a competitive disadvantage. Stakeholders are demanding better access to data, faster development of business intelligence (BI) and analytics applications, and agile solutions in sync with requirements.
Business stakeholders also want a higher return on investment (ROI) in BI, analytics, and data management. The power of information is not in question; rather, decision makers want more information sooner. Thus, one of the key ways to achieve higher ROI is by reducing the “time to value”—that is, how long it takes from the inception of a BI or analytics project to its completion and delivery of anticipated business value.
This TDWI Best Practices Report focuses on current experiences with realizing value from BI and analytics and how organizations can accelerate the path to higher value. The report looks at multiple factors impacting the ability of organizations to quickly derive greater value from data and analytics, including the organizational issues, practices, and development methods that are often just as important as keeping pace with technological innovation.
TDWI research uncovered a number of important issues that can determine the level of “downstream” value delivered by BI and analytics. Among them are the following:
- Poor project definition and scoping are often why organizations do not realize anticipated value from BI and analytics and why ROI falls short. Projects also need strong, consistent leadership and realistic budgets.
- Self-service BI, visual analytics, and data preparation are important trends, in part because organizations do not have enough skilled personnel to develop applications. Use of self-service data preparation functionality integrated with self-service BI and visual analytics can offload tasks from IT and enable users to do more on their own.
- Insufficient data quality, consistency, and completeness, as well as poor knowledge about the data’s lineage, can slow down the realization of value. Organizations are showing significant interest in metadata, data cataloging, and semantic integration.
- The majority of organizations surveyed show support for spending to reduce time to value. However, reducing time to value is only one among several key goals that organizations must balance to realize overall higher business value; for example, most research participants see increasing the accuracy of data, reporting, and analysis as just as important.
- Our research finds that self-service experiences must be managed and well-governed to avoid data chaos and missteps that could make it harder to achieve value sooner.
TDWI advises business and IT leaders to consider all the factors that contribute to the BI and analytics projects, including user ability to access relevant data and organizational ability to react with agility and flexibility. Accelerating the path to value depends on improvements in technology, methods, practices, and leadership.
Cambridge Semantics, Inc., Looker, Modemetric, Inc., SAP, SAS, Tableau Software, Unifi, and Zoomdata, Inc. 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.