A recent Dun & Bradstreet survey reveals that poor quality data not only costs companies revenue but customers as well. Among respondents, one in five organizations have lost a customer due to bad data, and over a quarter cite accurate data as their biggest data challenge. The fact is, ensuring enterprise data quality remains a significant obstacle for many organizations as there is a laundry list of reasons businesses struggle with data integrity. For some, their data remains siloed and inconsistent across lines of business and departments. For others, the raw data flowing into their data supply chain from both internal and external sources can create data quality issues and lack of standardization. Now that the inherent cost of poor quality data is multiplying, every organization wants to improve data quality processes, because without it, analysis is flawed, data insights are unreliable, and data assets can quickly become liabilities.
As soon as data enters an organization, its quality is at risk. As data flows through systems, processes and environments, its integrity is continuously threatened, leading to increased operational risks. Without high quality data, businesses risk regulatory noncompliance, making inaccurate or uninformed business decisions, or sending incorrect customer communications such as a bill. That is why poor data quality causes a proliferation of business problems, from flawed conclusions to lost revenue and frustrated customers.
Let’s take a look at some examples.
In the health insurance industry, there are tens of thousands of claims filed each day. One particular health insurer was having trouble identifying missing, late and duplicate claims within their claims process to ensure regulatory compliance. The problem was that their data quality tool could handle neither the formats nor the volume of claims they were receiving daily.
To solve the issue, the health insurer adopted an all-inclusive data intelligence platform that included data quality capabilities. The platform enabled high-volume data quality checks for data profiling, consistency, conformity and timeliness to ensure the accuracy and completeness of data. The selected solution allowed the insurer to monitor 100 percent of claims across all systems and interfaces, automate reconciliation processes, and reduce compliance fines to zero.
However, data quality challenges aren’t confined to the health insurance industry.
Another compelling example comes from a satellite radio provider. The company was facing challenges due to poor quality data from third parties providing subscriber demographics. The results were low retention rates due to campaign integrity issues and an inability to optimize marketing programs.
This company also chose an enterprise data intelligence platform to establish robust quality controls on data from both internal and external sources. As a result, the organization solved their data integrity problems, significantly increased customer retention rates, optimized marketing programs and achieved additional revenues in excess of 3 million dollars within the first quarter of implementation.
Yet this represents just one example of data quality challenges in the telecom industry.
Data quality challenges are an equal opportunity obstacle. Another example in the telecom sector comes from a cellphone carrier who needed help with IFRS15 compliance. IFRS15 provides standards on accounting for revenue from contracts with customers. The company needed to ensure compliance within their finance and accounting departments across the organization. This included five different lines of business with over 30 diverse source systems and applications and file structures, with daily volumes around 5 billion records.
The cellphone carrier implemented an integrated data intelligence platform to conduct high-volume data quality checks. The platform also had analytics capabilities to help the cellphone carrier quickly build a fully automated data integrity solution in their complex data environment, while ensuring compliance oversight.
All of these examples illustrate that the best strategy for improving and protecting data quality across an organization’s data supply chain is through an enterprise data intelligence platform with a comprehensive set of capabilities.
With today’s advanced technologies, organizations can gain more value and ensure data quality across critical systems and processes by having data governance, data quality, and analytics capabilities work together. A data intelligence platform can help provide a solid data governance framework which facilitates processes intended to ensure data quality throughout the data supply chain on an enterprise level.
When data is first ingested or created, organizations want that data to be easily understood, accessed, and trusted by stakeholders every step of the way so that it may be used to generate meaningful insights and drive business decisions. To understand what data means and its quality level, businesses must discern its attributes, lineage, metadata, and quality. Enterprise data governance capabilities within the data intelligence platform delivers these critical capacities, plus the integrated data quality features to monitor and improve data that users require.
Data quality capabilities, within an integrated solution, enable high-volume data integrity checks to verify the quality of data across the organization and ensure trust among all data users. Advanced business rules that protect the integrity of data across different systems and environments ensure downstream issues don’t proliferate. In addition, the platform should combine analytics capabilities and apply machine learning algorithms for self-learning to continuously improve data integrity.
With a multitude of capabilities, the platform should also facilitate a full understanding of an organization’s data landscape. This understanding enables all users to trust the quality of all their data to ensure regulatory compliance and make confident business decisions.
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