A primary goal of any data management strategy is to provide a clear understanding of the identities and key attributes of the many businesses that the organization engages. Optimally, this information is available across the enterprise, providing a 360 degree view of customers, prospects, suppliers, and other key relationships. The availability of high-quality data that provides this level of actionable insight restores trust in the data, increases its utilization and supports good business decisions.

But in the real world, holistic data management strategies take a back seat to the near-term needs of the functional teams that own their own data silos and enterprise applications. CRM and sales force automation and spend management applications have their own ways of building databases that are not easily shared.

Moreover, in the absence of a company data stewardship discipline, these siloed databases tend to accumulate a profusion of redundant, unverified, and incomplete records. Users of a CRM application, for example, may skip search-before-create –, and simply create a new record for each contact who may be new to the sales rep, but may be in an organization that is not new to the company. Many duplication issues are a result of this kind of gap in data governance processes for data entry.

To make data non-redundant, structured, and usable, companies have two sources of information to leverage: their own internal data and the data that they acquire from external sources. Each of these are mission-critical to arriving at data that is trusted, complete, and actionable.

Data quality starts with data matching: the elimination of redundancies among records and the accurate linking of multiple contacts that are associated with a single business entity. Internal data is the foundation of inferential matching. 3rd-party data must then be leveraged to complete the process, using referential matching.

What is inferential data matching?

Inferential data matching is the process of working with a company’s data to identify multiple records that should be associated with a single business entity and either linking them, collapsing them into a single record, or creating pointers to indicate that they are contacts within a single organization. But this matching process can be fraught with errors due to inadequate data management, incomplete data validation processes, or the challenges of rationalizing data across multiple systems, each with its own sources of data entry and its own ways of structuring data.

Even within an enterprise application, companies struggle with duplicated data resulting from error-generating data entry practices., For example, in a CRM application, rather than search-before-create, users may simply generate a new record rather than update or append an existing one. Many duplication issues are a result of this kind of gap in data governance processes for data entry. Resolving inferential matching issues requires aggregating data from multiple systems and determining which data components are duplicated, how duplications should be resolved, which data elements are valid, and which should be present in the enterprise’s single source of truth.

Inferential matching is a complex but necessary step towards data cleaning and reliability. Matching records that are alike or have slight differences to discover duplicates is a complex problem, especially when executing against complex objectives, such as standardizing an address in a specified way.

Inferential matching moves the enterprise closer to a 360-degree view of its contacts and commercial relationships  by increasing the trustworthiness and usability of data.

With clear, deduplicated data, the sales and marketing teams can better assess risks and opportunities with each buyer and make business decisions based on a clear picture of relationships across all internal teams.

Join Matchbook AI’ Rushabh Mehta for a live webinar on May 25th, 2017 at 1 pm EDT.  Learn about the data optimization tool set that Matchbook has developed in partnership with Dun and Bradstreet. Rushabh will point the way to the clean, trusted, enriched, high-quality data your teams need to build strategic analytics and grow process efficiencies. Click here to register.

ABOUT US: Matchbook AI provides data quality and data mastering solutions that offer comprehensive and multi-step inferential and referential data matching. It’s no surprise that data quality remains a key aspiration and critical need for organizations. Achieving and maintaining data quality requires processes, governance, and active oversight. The combination of trusted third-party pre-mastered data with the tools and technology that best fit your organization will set you on the right path.