Flawed Integration Can Destroy Data Quality and Reliability

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Until recently, there have been very few challengers to relational databases like SQL Server, which have reigned supreme for nearly 50 years. Now, traditional relational databases find themselves being replaced by newcomers like MongoDB, Cassandra and Hadoop along with non-relational, NoSQL, NewSQL and Big Data options. These new technologies have a lot to offer but don’t always play nice with existing systems and applications that were built with relational models in mind.

Firms that are starting to migrate to new technology need to future-proof their environments by finding a 'connector-tolerant' application to ensure seamless connectivity with a multitude of sources such as MongoDB, SparkSQL, Hadoop, etc. The faster organizations can move data for analysis, the faster they can free up the storage required to house the information at the collection points, cutting down on storage costs and management.

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.


Related Topics : Vulnerabilities and Patches, Resellers, Broadcom, Broadband Services, Supercomputing

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