Data quality is a key component of any organization’s long-term success, especially in this data-driven business world we live in. High-quality data can create better customer experiences, increase customer retention and drive higher top-line revenue. However, when poor quality data is used for analytics, the ensuing faulty insights may inaccurately reflect customer patterns and preferences, misalign moments of engagement, and can lead to negative brand experiences. All of these consequences can result in missed revenue and extensive challenges in an increasingly competitive world.
Quality data is an important measure that organizations can use to gauge their ability to apply enterprise information in strategic planning, tactical decision-making, and day-to-day operational activities. A lack of data quality continues to pose a major problem today for many organizations, as information environments become increasingly sophisticated and data is constantly being ingested, transformed and applied in diverse ways across data supply chains. Incongruent applications, databases, systems, messages, and documents make it more difficult than ever to identify and control data quality on an ongoing basis.
Data quality is key to gaining a competitive advantage, especially in mature markets. Organizations that fail to successfully turn raw data into meaningful insights risk losing critical business opportunities that could help influence product development, drive customer satisfaction, enhance business processes and eventually increase revenue. Organizations must treat data as the valuable asset it is, effectively manage it, maintain data quality, and derive those critical insights that can lead to competitive advantages.
Poor quality data can also contribute to lost revenue and lost opportunities. For example, sales communications with incorrect underlying customer data will never result in sales conversions. In the insurance industry, bad property data can cause premiums to be set too low, resulting in lost revenue and higher risk. Efforts to improve the customer experience are also undermined by bad data, such as an incorrect spelling of a customer’s name, or mistakenly sending communications to a customer’s old address. Organizations need to stay diligent and consistent with their data quality efforts to chart a path forward that leads to eradicating potentially egregious data errors.
Regardless of data source, data quality challenges will always arise, but what separates the good organizations from the bad are those who embrace a culture of data quality, rooted in principles that are implemented through technology. However, existing data quality tools used in operational environments are not architected to solve these issues at scale within big data environments. What’s needed to expand into big data quality isn’t an old tool retrofitted for the big data world, but rather an all-inclusive big data platform that can perform cutting-edge data quality checks in any data environment.
The platform should layer in analytics to transform the way data quality checks are performed on an enterprise scale. By performing analytics and machine learning in concert with any data quality business rule, organizations can substantially improve the efficiency and effectiveness of their data quality checks.
The platform should include easy-to-use capabilities to deploy data quality validation including data profiling, completeness, consistency, timeliness, reconciliation/balancing, and value conformity. It should allow users of all skill levels to quickly and adeptly apply powerful data quality checks to data sets. Finally, it should enable a repeatable process that automates data quality routines to standardize processes, reuse rules, and integrate results into interactive reports and case management workflows for speedy resolution of any data quality issues.
To learn more about an all-inclusive enterprise data intelligence platform to maximize data quality, download the data sheet below.
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