Improving data quality provides the differentiator to improve your business.
Improving data quality can be a daunting task for many reasons, including addressing challenges associated with the sheer volume of data, its rapid pace of change, legacy systems, along with other reasons that may be more specific to a particular business or industry. Not only is it critical that businesses begin to tackle the quality of their underlying data to better position themselves to take advantage of coming transformational technologies, but there can be significant rewards—in terms of improving customer experience, confidence in decision-making and lowering operational costs—that can be realized now. Likewise, the costs of poor-quality data are real and present: reputational harm, missed opportunities and potentially even violations of the law when it comes to financial compliance.
As businesses participate in an economy fueled by advanced technology and innovation such as autonomous vehicles, artificial intelligence and the Internet of Things, many find themselves grappling with the complexities of data and analytics. The role data plays in enabling these future technologies is critical—but one that will be undermined if businesses do not make data quality a priority.
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Learn what data quality really is – and what it looks like
Data quality can be hard to define because so much depends on the needs and objectives of the organization, who is using it, and for what purpose. Regardless, there are criteria that experts can point to that indicate good, or suitable, quality of data.
- Accurate: The data is correct—addresses that guarantee mail will be deliverable—or transaction data that properly reflects a customer's purchase history, for example. And there's a freshness component to accuracy, too, one that measures the timeliness of data points.
- Complete: The presence or absence of data, or, as Pitney Bowes' vice president of data product management, Dan Adams, puts it: "How well you've populated everything you want to capture."
- Standardized: This means finding meaningful ways to compare data sets that may differ. For example, inputs and formats of names and addresses can vary widely—an address in Japan is constructed very differently from an address in North America. So the ability to standardize input to the correct format, even in the presence of input errors, is important.
- Authoritative: Data sources must be authoritative, credible and fit for purpose. In other words, the source must have credibility to provide the data in an accurate and complete way, whether that source is internal or external.
Data is only going to increase in importance for businesses as technological change continues its push toward a more digitized world. This provides an opportunity for those that capitalize upon it early to differentiate themselves and stand out in an extremely crowded market. Laggards may find that it becomes exponentially harder to catch up the longer they delay on the data front.