Executive Summary | Maximizing Business Value with Data Platforms, Data Integration, and Data Management
Executive summary for the TDWI Best Practices Report: Maximizing Business Value with Data Platforms, Data Integration, and Data Management
- By David Stodder
- September 29, 2022
Maximizing the value of data platforms with data integration and data management capabilities is essential for organizations to become data- and analytics-driven. Increasingly—but not entirely—based in the cloud, these systems and services are the engine of modern applications as well as smarter processes for higher business efficiency and innovation. Business imperatives such as digital transformation, personalized customer engagement and marketing, agile and resilient supply chains and manufacturing, and faster response to unexpected situations are driving interest in modernization.
In this TDWI Best Practices Report, organizations show strong interest in modernization; 54% are actively modernizing now and 29% plan to do so in the near future. Legacy data systems rooted in fixed configurations and one-size-fits-all solutions for business intelligence (BI) reporting and analytics are inadequate. Too many organizations are mired in data silos and custom code for data integration and management.
Organizations need faster, in-context data insights, increasingly through development of artificial intelligence and machine learning (AI/ML) models and algorithms to drive modern decision-making and automated actions inside applications and business processes. Users are demanding self-service BI and analytics. They need data integration to empower them to answer business questions faster and collaborate on decisions more efficiently. This report reveals where organizations are facing the greatest challenges and how they can overcome them.
Research indicates the need for tighter integration between two traditionally separate worlds: one devoted to analytics generation and the data integration, platforms, and management that support analytics, and the other devoted to mission-critical business applications and processes essential to operations across enterprises.
Digital transformation and cloud migration are major drivers of change. Innovative data applications depend on continuous data flows and intelligent insights based on high-volume and highly varied data. In this report, organizations show interest in gaining the benefits of a range of data management systems and platforms to maximize the value of all their data and use new, active approaches to provisioning the right data at the right time to more users.
Distributed data scenarios create challenges, including hybrid multicloud environments that combine on-premises systems and services based on multiple cloud provider platforms. Organizations report concerns about data quality; fixing data quality issues is a top priority. However, distributed data environments often feature numerous data silos that contribute to poor data quality as well as incompleteness and inconsistency problems. Some organizations are consolidating data silos into central, unified data platforms in the cloud. They show interest in more tightly integrated data warehouses and data lakes. Others are using data virtualization layers to enable trusted data views drawn from multiple sources.
Building resources of knowledge about an enterprise’s data is vital for supporting different types of workloads. Data intelligence is also critical to meeting data governance priorities for protecting sensitive data and locating and accessing trusted data sets faster. Semantic layers are essential to building knowledge resources about data and aligning business representations with the data amid change. This report discusses the importance of enterprise data catalogs for meeting goals with semantic layers to make data governance more comprehensive and enable faster, more trusted data insights.
The report concludes with a discussion of how technologies and practices are coming together to create unified data environments, including with emerging data mesh and data fabric frameworks. It discusses how modernization makes this unity flexible rather than restrictive. The balance enables organizations to empower teams to maximize the value of enterprise data assets. We close the report with 10 best practices recommendations for success.
Alation, Alteryx, Denodo, MongoDB, SAP, and Snowflake 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 (twitter.com/dbstodder), and on LinkedIn (linkedin.com/in/davidstodder).