We’ve all heard of toxic employees, and perhaps have even met a few in our careers. They are those employees who infect teams with negativity, revel in disagreement and discord, and generally make the workplace a miserable place to be. Left unchecked, they can do massive damage to teams’ morale, productivity, and work quality. The destructive nature of these employees is somehow more devastating because employees should, theoretically, number among an organization’s most valuable assets.
The same can be said of data. It is arguably an organization’s most valuable asset, but that value is only realized if it’s reliable and trusted by business users across the enterprise. When data quality is in doubt, it has a toxic effect across the enterprise, as reports are questioned, insights are suspect, and business decisions are doubted. The toxicity of data distrust causes business users to stop utilizing it; or worse yet, bad data that is used can lead to false assumptions, negative customer experiences, lost revenue, and missed opportunities.
With data constantly in motion across the data supply chain, continually being ingested, created, and transformed, challenges are always arising. Issues with data quality, data inconsistency, and a lack of data visibility can all contribute to real and perceived data integrity concerns. Verifying data for accuracy and consistency is critical to ensuring that all systems are in sync, data formats are correct, and that business users can quickly and easily access data for analysis to confidently build data-driven strategies. Poor quality data not only threatens the value of analytical outcomes, it can mean huge delays in preparing data for analysis. And in a highly competitive business landscape, speed to insights matters.
To complicate things further, existing data quality tools have several flaws. Traditional tools used in typical operational environments often won’t scale for big data. Further, while these tools measure basic quality dimensions and cover completeness, conformance, and validity, they don’t utilize advanced integrity and accuracy checks or incorporate analytics for ongoing monitoring and quality improvement as part of an integrated data governance effort.
Just as toxic employees can negatively impact their own teams and those that interact with them, within a company there are measures (e.g. performance reviews, peer reviews, etc.) and frameworks that can manage these types of personalities (i.e. HR, team managers, etc.). For toxic data, there is data governance that provides a framework which prevents a toxic data environment from forming, and promotes a data-driven culture. It is about coordinating people, processes, and technology to leverage data as a valuable enterprise asset. Data quality is essential to achieving this ongoing objective. As data travels through the data supply chain, it is exposed to new processes, uses, and transformations, which can significantly impact on data integrity. It is critical to establish data quality controls throughout the data supply chain for reasonability checks, timeliness checks, balancing and reconciliation, and statistical controls. Within a comprehensive data governance framework, organizations can also score and monitor data quality to prevent the proliferation of downstream data issues and help monetize data assets.
There are clearly synergies between data governance and data quality efforts, not the least of which is the connection between trust and understanding. It is human nature to distrust that which we don’t understand. Data quality is certainly a critical part of the trust equation, but data governance is as well. Governance, at its foundation, is about establishing data understanding across the enterprise. By ensuring an organization’s data is both accessible and understood among business users through the use of business glossaries, data dictionaries, data lineage and uniform policies and procedures, data trust is reinforced, and data utilization is encouraged. It is one thing to have well curated and understood data, but without knowing the quality of data prior to consumption, data governance initiatives might just be tracking bad data. By combing both governance and quality, consumers of the data can find, trust, and derive true insights from the data that is being managed within their organization.
An integrated solution that incorporates extensive data quality, data governance, and analytics capabilities can help empower business users and build trust across the organization. By employing machine learning algorithms for self-learning, data integrity can be continuously improved, encouraging utilization enterprise-wide and ensuring quality business decisions and insights. Without well governed and quality data, analytics teams would struggle to trust any of the outcomes that their models are based on. Analytic teams need to know that the quality of their model features is high before they can accurately predict successful outcomes.
Data quality issues are a common challenge. One great example comes from a retirement services organization. The company was ingesting data from multiple third-party sources. The data quality issues around the third-party data was causing revenue leakage because business users couldn’t accurately track and capture when their customers upgraded, downgraded or cancelled their services.
To combat the problem, the company implemented an enterprise data intelligence platform to establish end-to-end data integrity checks to ensure enterprise-wide data quality, and reduce risk and minimize loss of revenue. These efforts helped the company reduce errors from third-party data by 88 percent, saving a significant amount of time and money, and establishing trust in data among business users. This is a great example around not only do you need to provide the basis for data quality around internally generated data, but also around data that is being purchased and “trusted” from third parties.
Are you looking for additional details about how to avoid the dangers of toxic, poor quality data, and how to establish data trust among business users? Please download our data sheet to learn how advanced data quality initiatives can build trust and give users data they can count on.
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