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Flawed Integration Can Destroy Data Quality and Reliability

  • Flawed Integration Can Destroy Data Quality and Reliability-

    Security

    When working with Big Data, security should be a priority. Several high-profile data breaches in government, retail and banking in the past few years have made this threat very real. In this new world where every detail of our lives can be recorded and stored in databases, the organizations collecting that data have a responsibility to protect their customers. Here are some of the ways that companies can protect themselves when dealing with Big Data:

    • Define responsibilities for both the cloud services provider and the cloud services user regarding specific data privacy controls that are required.
    • Perform ongoing monitoring and audits of cloud services and consistently review any relevant metrics that indicate levels of data integrity, confidentiality and availability.
    • Encourage and invite consumers to access, review and correct information that has been collected about them.
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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-4

    Security

    When working with Big Data, security should be a priority. Several high-profile data breaches in government, retail and banking in the past few years have made this threat very real. In this new world where every detail of our lives can be recorded and stored in databases, the organizations collecting that data have a responsibility to protect their customers. Here are some of the ways that companies can protect themselves when dealing with Big Data:

    • Define responsibilities for both the cloud services provider and the cloud services user regarding specific data privacy controls that are required.
    • Perform ongoing monitoring and audits of cloud services and consistently review any relevant metrics that indicate levels of data integrity, confidentiality and availability.
    • Encourage and invite consumers to access, review and correct information that has been collected about them.

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.