4 Attributes of Quality Business Data
Recognizing quality data is not always easy because it vale sometimes depends on who is using it, and what their objectives are. To provide clarity around what businesses should be aiming for when constructing a data strategy, here are 4 aspects of quality data:
This attribute is seems straightforward, we are looking for data that is correct. For example if we are mailing something to a client the mailing address we use should guarantee deliverance, or a corporate hierarchy insights should properly outline business linkages.
Keep in mind as Anthony Scriffignano, Dun & Bradstreet’s chief data scientist, stated, “accuracy itself can be situational—for example, among 10 telephone numbers that might be associated with a business, the one for investor relations is inappropriate if you are trying to reach the main switchboard for a local office.” In other words, accuracy is sometimes dependent on context. “In many cases, we talk about accuracy as if there’s some grand truth against which it can be measured, and that’s not always appropriate,” says Scriffignano, adding: “The whole concept of accuracy is really nuanced, and it has to be taken in the context of the particular attribute that you’re talking about, and sometimes in the case of the ultimate usage of that data, in order to measure it.”
Completeness is determined by whether or not data sets capture all data points available for a given instance. For example if two-thirds of a customer’s purchases are not recorded then that data set will underestimate the customer’s value. This incomplete data will continue its disservice by inhibiting a company’s ability to identify all high-value customers.
Having standardized data enables users to find meaningful ways to compare data sets. For example, establishing data standards for addresses allows companies to compare mailing information for customers around the world, even when comparing vastly different address layouts like Tokyo versus United States addresses. Standardization of your data format is necessary for inputting information, but it is especially important in identifying duplicate data points.
Data sources need to be authoritative, credible, and fit for purpose. Your data source must be reliable, an industry authority, and trusted by everyone using the data. Without the best input from authoritative data sources your business decisions will be faulty.
Why Data Quality is Essential for Companies Today
Businesses everywhere are considering, testing, or adopting today’s emerging capabilities such as “AI”, blockchain, and predictive analytics for competitive gain, organizational efficiencies, and tech investment time to value.
For the business and data science professionals who are perfecting their organizations’ best formulas for data and technology synergies, data quality becomes paramount. It’s critical as they treat data as a high-value asset, building strategies and platforms to ingest, reason with, distribute, and present unprecedented business insights across their organizations for countless business use cases.
The Data Differentiator: How Improving Data Quality Improves Business, a report (see below) commissioned by Pitney Bowes and prepared by Forbes Insight, delves deeply into why and how data quality is vital for today’s organizations looking to reap the promised business gains from fully leveraging inextricably connected data and technology.
Infographic: Successful Data Quality Initiatives
For those who live and breathe data, determining the best data sources to use based on the needs of the organization can be tricky. In this infographic, Anthony Scriffignano, Ph.D., Dun & Bradstreet’s Chief Data Scientist, shares his perspective. Leading a team of data scientists focused on advancing Dun & Bradstreet’s strategic thinking around data and related IP – as tens of thousands of customers around the world constantly use our data and analytics to make trusted data-driven business decisions – Dr. Scriffignano uniquely understands why data quality is mission critical.
When asked how companies can successfully differentiate between data sources and their appropriateness for a given purpose, Dr. Scriffignano replied with the deep knowledge of a data scientist responsible for one of the world’s largest, constantly changing commercial databases:
“Imagine a series of blocks named discovery, curation, synthesis, fabrication, and delivery. Those are the five steps in the mental model that I use, and then alongside those, quality assurance and governance. Every one of those steps has a quality assurance step and a governance step.”
ROI gains become more likely when co-dependent data and technology have equal footing. Data quality therefore stands to be the differentiator for organizations who make it a business imperative.
Get more information about data quality across Dun & Bradstreet’s Data Cloud. Learn more about data quality from leading data experts in The Data Differentiator: How Improving Data Quality improves Business.