Real-time Data Quality and Data Enrichment as-a-Service

Real-world applications of data and analytics are exploding, due to the power and affordability of advanced analytics and the explosion of information is available than ever before.

As the quest for powerful data insights captures the attention of business leaders across every industry, many are realizing the benefits of introducing curated third-party data into their analytics initiatives. These leaders they have developed a keen awareness that unless they proactively manage and control data quality, their data analytics will fall short of expectations, or worse yet, will deliver the wrong results altogether.

Data enrichment and data quality/data governance comprise two of the four key capabilities that make for strong data integrity. The two remaining pillars of data integrity are integration (that is, eliminating data silos within an organization) and location intelligence. The latter focuses on connecting people, activities, and things to the “where and when” that provides rich contextual information about them. This further amplifies data enrichment, delivering a much deeper understanding of the things that matter most to your business.

Data Quality as-a-Service

Data quality tends to decay over time. There are a number of factors that contribute to this problem, including data entry errors, incomplete or inconsistent business rules within individual applications, duplicate entries, and conflicts between two or more systems that purportedly contain the same information. One of the most important factors, however, is the tendency for information to change over time, such that internal data becomes obsolete.

Customers move or change their names. People get married, have children, and eventually pass away. Business customers merge, re-brand, or go out of business altogether. Only rarely does the updated information associated with all these customers get pushed to the companies that do business with them. As a result, customer information is especially prone to data quality problems.

The primary lesson here is that data quality requires a proactive approach. Access to externally sourced information is a critical element of success. When a customer moves to a new residence, it’s important that your company can continue to send them mail, route deliveries to the correct address, and otherwise continue to engage them on a regular basis.

Just as importantly, a change of location often correlates with other life changes, which indicate the customer has some new, unmet needs. Telecom companies may wish to reach out with offers that reflect the customer’s changing status–for example, when a college student is graduating and making the transition to self-sufficiency and a higher income lifestyle.

Data Enrichment: Opening up New Possibilities for Data Insights

This example provides a good illustration of the connection between data quality (making sure you have an accurate and consistent address, for instance) and data enrichment. Knowing a customer’s address, standardizing, verifying it against trustworthy third-party sources, and resolving it to its specific geographical coordinates opens up a whole new world of information. In many respects, location becomes the Rosetta Stone that unlocks a wealth of additional context with respect to customers.

Data enrichment can often be useful, for example, simply to identify distinct affinity groups and psychographic profiles which can be associated with existing or prospective customers. By segmenting groups of customers according to location and demographic categories such as age and marital status, and general lifestyle preferences, companies can ensure that their marketing messages reach the right audiences in just the right ways.

This capability can be especially helpful when making decisions that exist in a geospatial context, such as selecting a site for a new retail location or placing digital billboard advertising. By understanding who lives in a particular area as well as using football data to see who typically visits a specific location for work or leisure, advertisers and retailers can pinpoint their efforts to achieve maximum benefits.

Data Enrichment as-a-Service

In the past, data enrichment has generally involved purchasing data from a third party, then harmonizing and integrating that information with existing data to be stored internally. There are several challenges associated with this approach. First, integrating high volumes of third-party data to be stored internally alongside existing customer data can be a resource-intensive task.

Data enrichment as-a-service, on the other hand, provides access to only those records and data attributes that a company requires. It’s a simpler proposition from the standpoint of implementation, and it is significantly less costly than the traditional approach.

Precisely’s data enrichment as-a-service (Spectrum OnDemand) handles the sometimes difficult process of matching internal customer records with enrichment data. This includes address resolution and matching, fuzzy name matching, and much more.  Using a service-based model for data enrichment, companies can offload this very important (but challenging) task and focus on factors that truly differentiate them from their competition.

Precisely’s robust data portfolio provides a wide variety of datasets that help businesses to conduct complex analytics for virtually any industry. Built for versatility, these datasets can be used in Precisely software environments to enrich existing property data to generate deeper business insights. To learn more about Precisely’s data quality and data enrichment offerings, watch our free on-demand webcast, Data Enrichment Series: Data Enrichment Using Spectrum OnDemand from Precisely.