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Flawed Integration Can Destroy Data Quality and Reliability

  • Flawed Integration Can Destroy Data Quality and Reliability-

    Accessibility and Consistency

    As data volume, velocity, variability and variety increase, so do the stresses on today's software infrastructures when they can no longer make sense of data deluge. In a well-written, well-tuned application, over 90 percent of data access time is spent in middleware. And data connectivity middleware plays a critical role in how the application client, network and database resources are utilized. In any bulk load use case scenario, database connectivity is the cornerstone of performance. Over the years, technology vendors have made great strides in database optimization as well as the performance of processors and other hardware-based server components. As a result, the bottleneck migrated to the database middleware.

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Flawed Integration Can Destroy Data Quality and Reliability

  • 1 | 2 | 3 | 4 | 5 | 6 | 7
  • Flawed Integration Can Destroy Data Quality and Reliability-2

    Accessibility and Consistency

    As data volume, velocity, variability and variety increase, so do the stresses on today's software infrastructures when they can no longer make sense of data deluge. In a well-written, well-tuned application, over 90 percent of data access time is spent in middleware. And data connectivity middleware plays a critical role in how the application client, network and database resources are utilized. In any bulk load use case scenario, database connectivity is the cornerstone of performance. Over the years, technology vendors have made great strides in database optimization as well as the performance of processors and other hardware-based server components. As a result, the bottleneck migrated to the database middleware.

Analyzing large amounts of data across a multitude of business systems enables companies to optimize the performance of the sales team, identify patterns in the industry, or determine the efficacy of marketing. A variety of tools enable organizations to prepare the data, but if the quality is insufficient, it will provide unreliable insights.

Data connectivity and integration can be affected by a variety of factors including how it is entered, stored and managed. Maintaining high-quality data is reliant on regular updating, standardization and de-duplication. However, if these processes are flawed, the data can negatively sway organizational spend and productivity. When evaluating the quality of your organization’s data, a variety of characteristics need to be assessed.

In this slideshow, Paul Nashawaty, director of product marketing and strategy at Progress, looks at key factors organizations must consider to ensure their data remains of high quality.