Improve Your Business: Everything You need to Know about Data Quality Management

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As more organizations are finding ways to adapt to big data platforms, there are concerns such as the lack of good practices to management of that data. When you discuss big data management in relation to platforms, it’s obvious that big data technologies have to meet the demand for data quality management processes and tools.

What is data quality? This is a set of values that are based on qualitative or quantitative variables. Its intended use is for decision making, operations, and planning. Here are five important things you need to know about big data management to maintain consistency and trust with your analytics.

You Can Do Data Quality Management Yourself

One of the biggest missions of big data is that it provides access to multiple data sets in their original formats. Business leaders are becoming more tech-savvy than their predecessors. They would rather access and use the data in its original format than to allow it to go through multiple data marts, data stores, and data warehouses. Business leaders want access to the data sources they need to create analyses and reports based on their needs.

It’s Not Your Parent’s Data Model

One conventional approach to analysis and reporting is to absorb that data and use it within a predefined structure. But when it comes to big data quality management, both structured and unstructured data sets can be used and stored in their original formats. This eliminates the need for predefined data models. One advantage is that different users can use the data sets in a way that best suits their needs.

To decrease the risk of conflicting information and inconsistency is to establish good procedures found in metadata management that can be used for big data sets. That means coming up with procedures for documentation in the business glossary, relating business terms to data elements, and providing a collaborative atmosphere to share ideas and methods of using data for analytical purposes.

Quality Varies Among Business Owners

In the conventional approach, cleansing and data standardization are used to store the data in its predefined model. One of the disadvantages of this method is no cleansing or standardizations are applied to the data sets when it’s captured. It’s now the user’s responsibility to transform this data. As long as the transformations don’t have conflicting information, these data sets can be used for different purposes.

Big data management must find ways to capture data transformations and to make sure they’re consistent and support the various interpretations.

Understanding the Architecture Increases its Performance

Big data platforms often rely on processing and storing of nodes for parallel computation which is used in distributed storage. If you’re unfamiliar with these execution and optimization models, then you’ll expect poor response times. The advantage here is that understanding how your big data organizes this information and how the database will optimize these queries allow you to create data applications with increased performance.

It’s Based on the Streaming World

In the past, data was used by organizations for analytical purposes and stored in data repositories. There’s an increased use of streaming data thanks to ever-changing technology. There’s data that’s streamed from content on blogs, e-mails, social media, and more. There’s automatically-generated streaming content and machine-generated data that comes from a wide variety of tools.

Most of these sources contain massive amounts of data and can be used for analysis purposes. This can become an issue. Your big data management strategy should support this technology to filter, scan, and select the right information for capturing, storage, and subsequent access.

Managing big data no longer relies on using conventional approaches. It now incorporates the use of the latest processes and technologies to increase data accessibility and usability. Your data management strategy should include tools that support collaborative semantic metadata management, data discovery, data preparation, data standardization and cleansing, self-service data accessibility, and stream processing engines.

Being aware of these practices can increase the time-to-value of your data management program.

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