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Executive Summary | Achieving Success with Modern Analytics

This report examines the drivers for modern analytics and the current state of analytics adoption.

Modern analytics can provide a significant path to value for organizations. Although many companies are still analyzing structured data, newer data sources such as machine data or text or image data, together with newer analytics approaches, including automation and new techniques, are becoming part of an evolving data and analytics landscape.

This report examines the drivers for modern analytics and the current state of analytics adoption (including tools and platforms) and explores the differences between those who are using modern analytics successfully and those who are not.

TDWI research finds that organizations are embracing modern technologies in their efforts to support a modern data infrastructure for analytics and a complex data landscape. These include:

  • Cloud platforms and technologies
  • Automated and augmented tools that help manage and govern data
  • Data fabrics to address hybrid environments
  • Pipelines that can support new data types such as streaming data
  • New governance tools including automated data quality and lineage tools

On the analytics front, about a third of survey respondents are utilizing more advanced analytics techniques such as machine learning and deep learning. Some are embracing self-service tools that may automate the surfacing of insights for business users. A small group is already starting to embrace generative AI models that create new outputs—such as images, music, text, or other forms of media—based on the training data. Organizations are using both open source and commercial products; they are using first-party data as well as data from partners and third-party providers, including marketplace data.

A unique feature of this report is its examination of the characteristics of companies that self-identify as being successful with modern analytics. In other words, it explores how those companies compare to those that are not successful. Some highlights include differences in leadership, organizational style, data platforms, and tools used, as well as how they approach issues such as data literacy and governance. For instance:

  • Those who are successful are more likely to have a committed analytics leader in place (82% versus 51%)
  • Data literacy is a priority (78% versus 44%)
  • They are more likely to state that cloud platforms are a priority (69% versus 44%)

Despite some organizations’ success, many businesses still struggle to implement or see benefits from modern analytics. Part of the issue is with technology. Additionally, there are organizational challenges that must be addressed, which often include building cultures, funding, hiring the right people, and organizing to execute. This report concludes with recommended best practices that can guide your organization to overcome these difficulties and be successful with modern analytics.

SAP, Snowflake, and StreamSets sponsored the research and writing of this report.

About the Author

Fern Halper, Ph.D., is vice president and senior director of TDWI Research for advanced analytics. She is well known in the analytics community, having been published hundreds of times on data mining and information technology over the past 20 years. Halper is also co-author of several Dummies books on cloud computing and big data. She focuses on advanced analytics, including predictive analytics, text and social media analysis, machine-learning, AI, cognitive computing, and big data analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead data analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her by email ([email protected]), on Twitter (, and on LinkedIn (

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