Third-party data has given businesses headaches for years. The fact is, when organizations are ingesting large amounts of raw data from various external-party sources, the data is frequently of unknown quality, causing a proliferation of business problems. As a result, businesses often suffer from a multitude of consequences such as lower data utilization among data users, customer satisfaction issues and lack of compelling analytical insights to improve the customer experience.
Today’s customers demand fast, convenient and quality customer service, and the most successful businesses are meeting or exceeding these demands. Across a wide range of industries, business that fail to meet these needs are quickly replaced by those who put the customer experience first. For example, customers require their financial institution to provide easy access to their banking information in real time from any digital channel. Customers want to make deposits, transfer money, and apply for lines of credit without leaving the house. Without high-quality data, both internally and from third-parties, the bank risks delivering incorrect information across digital channels, quickly leading to customer dissatisfaction and loss.
In the telecom industry, cellphone carriers use data to enhance service and create a seamless customer journey. By leveraging third-party data and internal customer data, telecom providers can appropriately target the right product and options to the right customer at the right time to reduce customer churn and improve their company’s bottom line. The caveat being, without high-quality data, organizations risk leveraging inaccurate information that can lower customer confidence, damage their reputation and have a long, lasting negative impact on the brand.
Organizations frequently rely on third-party data to add value to corporate initiatives or to augment existing information to improve the client experience. As this data is ingested, it is critical to ensure its data quality, because as data travels through complex data supply chains, it is then exposed to new processes, procedures, transformations, and uses, all of which put data integrity further at risk. Solving data quality issues upstream is imperative, because a lack of high-quality data poses significant problems for businesses, well beyond the impact on customer satisfaction and public perception. As information environments expand and become more sophisticated, in-congruent applications, databases, systems, messages, and documents become lost in a data forest, making it increasingly difficult to identify and solve ongoing data quality problems for which the investigation of the root causes can be very costly due to the level of resources required.
For organizations to find a path out of the dense data forest, measuring and scoring the quality of data assets is important as business users and data pros alike determine what data is best for critical analytics. By scoring and monitoring data quality directly within a data governance framework, organizations can prevent those downstream data issues that contribute to a negative customer experience from ever occurring.
When businesses proactively solve their data quality issues through data governance, they enhance the customer experience, minimize churn, and increase revenue.
Positive customer experiences are often dependent upon solving data quality challenges. One cable company proactively resolved their data quality issues through data governance. The company was ingesting data from multiple third-party sources, and using that data to distribute marketing materials focused on attracting new customers. The third-party data quality issues caused current customers to receive communications instead of prospective ones. The existing subscribers were frustrated and not afraid to express their anger.
The cable provider implemented rigorous high-capacity data integrity checks with a comprehensive data governance framework. As a result, the organization virtually eliminated these data quality issues, building trust in their data and loyalty among customers.
Still, solving data quality issues through data governance is no easy task. Quality data comes from numerous data checks, authenticating data integrity with monitoring throughout systems and discovering any potential errors that might impact quality. Automating this process with a data intelligence platform allows organizations to eliminate historically manual efforts while saving valuable time and money.
Automation is key to successful data governance and improving enterprise-wide data integrity. With a data intelligence platform that provides a foundation of data governance, businesses can integrate data quality monitoring and improvement to establish a framework for trusted and understood data. This includes high-capacity data integrity checks along the data supply chain, including checks for completeness, conformance, and validity. Automated quality checks can then go beyond basic quality dimensions to ensure data is properly transformed as it flows between and across systems, and that the data remains reliable. In addition, analytics capabilities can layer in machine learning algorithms for self-learning to continuously improve data quality efforts.
Data governance capabilities within the platform should also provide transparency into an organization’s data landscape, including the available data, its location, the data owner/steward, and data lineage. Users across the enterprise will have unified and quickly accessible glossary definitions, synonyms, and business attributes for data, so they may easily define, track, and manage all aspects of their data assets to help make critical business decisions.
Customer experience is a major determining factor when it comes to a person sticking with their financial institution, cellphone carrier, cable provider, etc. The ability of any company to create a positive and personalized customer experience will always depend on the quality of their third-party data. By incorporating data quality within a governance framework and implementing a data intelligence platform, businesses can improve data integrity enterprise-wide and gain a competitive edge.
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