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Thoughts about NPF-Ken Kucera


As we spoke with visitors to the Firstlogic booth at the National Postal Forum, attended the general sessions, and popped into wor.

Can a $3 Trillion Problem Really be Hidden?


THREE TRILLION DOLLARS. That’s the amount The Harvard Business Review (HBR) says poor quality data costs companies in the US.

What are “Golden Records” and How Do You Create Them? Part 2


PART TWO In part one of this article we discussed the reasons companies want to create golden records, also known as a Single Cust.

What are “Golden Records” and How Do You Create Them? Part 1


PART ONE Whether you call it a Single Customer View (SCV), a “Golden Record”, or a “Master Record”, custom.

Can You Really Identify Your Customers?


Everyone knows connecting with customers on a personal level leads to more sales, better customer retention, referrals, and higher.

By Next Year, You’ll Wish You’d Started a Data Quality Program


Have you deferred implementing a data quality initiative for your organization? Do you think a structured approach to data quality.

Firstlogic News

What are “Golden Records” and How Do You Create Them? Part 1

PART ONE

Golden RecordWhether you call it a Single Customer View (SCV), a “Golden Record”, or a “Master Record”, customer data files purged of duplicates, misspellings, and inconsistencies are the goal of marketers and other business people worldwide. A single and reliably accurate source for customer information makes true personalization possible, improves business decision-making, and provides the infrastructure for enhancing the customer experience. The goal of omni-channel customer communication is only possible once an organization has successfully created golden records.

Ideal customer databases are hard to find. They don’t occur naturally. Companies must establish and follow a strategic approach to data gathering and maintenance to enjoy the benefits of an SCV.

Data quality is not a goal, but an ongoing process. System owners must standardize, match, cleanse, enhance, and consolidate consistently if they intend to rely on the information for effective marketing campaigns, customer communication, or important business decisions.

Data usually enters organizations through multiple channels, many of which are uncontrolled by the corporation. New data can contradict existing information, putting pre-established golden records in question. Unmanaged data is full of non-standard abbreviations, outdated postal addresses, abandoned email accounts, and missing information.

Standardization
Before anyone can clean up their customer data, they must standardize as much as possible. Address correction software can standardize postal addresses to meet the specifications of an accepted authority such as the US Postal Service. Other cases may be less straightforward.

Company names are a good example of challenges businesses face during the standardization phase. How many ways might a person describe the company that makes Chevrolets, Buicks, and Cadillacs? GM, General Motors, General Motors Corp, and GM Inc. are just a few of the possibilities. When it is important to group individuals by the company for which they work, enforcing a standard form and spelling is essential. Most organizations use specialized data quality software to standardize highly variable information such as company names.

Depending on how corporations enter data, the names of individuals can also require standardization. What if some data records list customers as lastname, firstname and other data as firstname lastname? It won’t be possible to build a golden record until databases store customer names in a common format.

Standardization is necessary before matching or consolidation can take place.

Matching
The criteria for matching data from separate files will vary among organizations and the way they use the data. Some companies may match all individuals with the same last name at a common postal address, for instance. Others may consider each family member as individual entities and choose not to match them.

Although matching is necessary to remove duplicates, redundant data is not always the issue. A good example might be transactional data where customers make multiple purchases. The company wants to recognize all those purchases were made by the same customer, but they don’t want to eliminate any of the data. Matching software should include options to craft the routines to meet organizational demands.

NEXT – Cleansing, Enhancement, and Consolidation
All the steps, including standardization and matching, are essential to the process. Though they may be eager to start building golden records right away, businesses cannot skip the preliminary steps, and proceed directly to consolidation.  In the second part of this article we’ll discuss the next steps organizations must take in their quest to create golden records.