When it comes to data quality, how good is good enough
The hallmark of data quality is how well data supports the context in which it’s consumed. Your legal department, for example, may use “Informatica Company” while your finance department uses “INFA,” and both records are of equal quality.
Quality is a relative and never-ending judgment, one that needs to be defined by the business (or business unit) that’s consuming the data. An essential element of holistic data governance, trustworthy data serves critical business needs across the enterprise—from legal to finance to marketing and beyond.
Driving data quality requires a repeatable process that includes:
- Defining the specific requirements for “good data,” wherever it’s used.
- Establishing rules for certifying the quality of that data.
- Integrating those rules into an existing workflow to both test and allow for exception handling.
- Continuing to monitor and measure data quality during its lifecycle (usually done by data stewards).
And because rules and needs change and new systems can be added to the mix, truly successful data quality initiatives need to be scalable to address those new requirements..
Bad data cost U.S. business $600 billion a year, according to the research firm TDWI.
Why data quality matters
Why does quality data matter? An often-cited statistic puts the cost of “bad” data to U.S. businesses at $600 billion annually1. Whether bad data causes you to lose revenue, damages your brand, reduces your competitive edge, or simply results in bad decision-making, the costs are significant.
When looking for a data quality solution, we recommend you put the following at the top of your “must-haves” list:
- Reliability: The demand for reliable data quality is a critical—and persistent—need. Your business will want a vendor with a successful track record as well as a comprehensive approach to ensure a holistic data quality process that supports the key steps around discovering, defining, applying, monitoring, and measuring progress.
- Portability: Whether you’re using legacy mainframes or the latest technology, you’ll want a data quality tool that can evolve as your business does. That means the data quality solution you deploy should allow you to scale across platforms—from on-premise to the cloud, in Hadoop, or in a hybrid ecosystem.
When choosing a data security solution, look for the following attributes:
- Accessible: Make sure your solution can access data wherever it is, whether it’s in new data channels, your mainframe, or your line-of-business application. With the ever-growing data volume and variety, sensitive data must be protected and understood, regardless of its origin.
- Unintrusive: Solutions should work within existing architectures and not require changes to proven and reliable databases and applications.
- Deployment-agnostic: Your solution should have the ability to access a wide variety of deployment models, from big data to cloud to on-premises to hybrid ecosystems.
- Technology-agnostic: Whether you’re using a legacy system or state-of-the-art innovation, your solution shouldn’t require a change in applications and databases to align with the newest technology.
- Adaptable: You want a platform that can handle a wide variety of use cases—from blocking access to fine-grain control—with a single approach.
Using our efficient global delivery Model, Neelblue technologies has the capabilities to implement the most optimized solution for your organizational needs.We are at the front of industry best practices, ensuring your customized solution will propel your business forward as you grow over time. We can also augment your application support abilities, ensuring application health and availability. These are vital in providing the information needed for your company to make the critical decisions at the most opportune moment.
Talk to us about how our consulting services can provide effective solutions for your enterprise.