Data Strategy

Best Practices for Data Hygiene

As companies look to more automated, AI-driven solutions, customer data is being held to a level of data quality previously thought to be unattainable.

Customer data is most often a brand’s most valuable resource. Frequently thought of as the “oil of the digital era,” clean, reliable customer data is the key to forming a relationship that extends beyond that of your competitors. 
Despite its undeniable value, examples of mismanaged and poor-quality customer data surround us every day. Many enterprise companies have experienced data breaches that have damaged reputations and cost millions in settlements.
Large volumes of data are cumbersome to manage at the best of times, and customer data can be downright impossible to manage without the right strategy in place.
Now, throw in the threat of increasing regulation and penalties for when it is mishandled, and quality data is no longer a nice-to-have but more of a necessity.
Due to the dynamic nature of customer data, maintaining the quality of this data can feel a little like chasing your own tail. Regular data cleansing, and a sound strategy behind it, suppresses or modifies data that is incorrect, incomplete, irrelevant, or improperly formatted and is fundamental when working with customer data.

How to Cleanse Customer Data 

An ongoing cleansing strategy should include processes for cleansing existing data, merging accounts, removing duplicates, and establishing a benchmark in data quality. Creating a repeatable process ensures that quality is maintained and minimizes the need for manual, time-intensive processes.
In short, the top four ways to maintain clean customer data are:
  1. Cleanse your existing data 
  2. Unify and merge business and customer records to a single source of truth 
  3. Accurately remove duplicate records
  4. Prevent duplicates and maintain accurate records 
1. Cleanse Existing Data 
Before anything can be done with your data, the first step is to ensure its accuracy. To clean existing records, contact data can be “scrubbed” with software that corrects and applies standardization to customer details such as an address, email, or phone number.
At this stage, keep an eye out for gaps or inconsistencies in customer information that will help you in merging records and preventing future challenges.
2. Merge Business Accounts & Customer Records 
Businesses today create and depend upon large volumes of data, and each department may depend on its own segment of data integral to their operations. As a result, data inevitably becomes siloed and disparate, and merging quickly becomes complex due to the variety of databases, file formats, structure, schema, and outdated records.
And while joining various datasets can seem like a fairly straightforward task at first glance, innumerable inconsistencies and challenges with customer data usually make it a very challenging one to fully automate.
To ensure a complete, 360-view of the customer — one that is accessible across the organization — software must be able to analyze and match with human-like perception so you don’t need to comb through results line-by-line.
To create the most complete and up-to-date view of your customer, ideal solutions should apply a hybrid of matching algorithms, such as the data matching software within the Syniti Knowledge Platform, which mimics an expert human user to make decisions automatically for you. 
3. Remove duplicates 
After contact information is cleansed and consolidated, duplicates can be removed. But manually deduplicating contact records that span into hundreds of thousands (or millions!) is, of course, simply impossible.
When it comes to duplication identification, it’s important to use as much of the relevant data as possible to put it into context. Rather than rely on any single field always being accurate like the name as per most off-the-shelf solutions, advanced solutions consider all relevant data and score it contextually.
Pro tip: Sophisticated data matching software shouldn't require data to be correctly formatted or consistently structured prior to loading. However, downstream processes and users may benefit from standardized outputs. Look for a solution that offers a built-in normalization tool to do the heavy lifting in these situations and save on hours, if not days of preprocessing, yet also allow for the flexibility of avoiding the data prep altogether. 
4. Prevent duplicates 
Don’t let all your hard work cleansing and wrangling data be for naught! Without proper maintenance and point-of-entry implementations that keep duplicate records and bad addresses out, that freshly-cleaned data will quickly decay—to the tune of as much as 30% per year.
Fully embracing data quality can involve rethinking traditional business models, processes, or resources. It likely involves revisiting legacy technology or systems that can better process and analyze incoming data at scale.  If new investments are required, solutions that make data management accessible to everyone, regardless of coding experience, are worthy competitors - especially when backed by the processing power to grow with your users needs.

The Expected Standard of Customer Data 

As companies look to acquire more automated, AI-driven solutions, customer data is being held to a level of quality previously thought to be unattainable. Now, this optimized, squeaky clean, and regulatory compliant level of quality is becoming the expected standard.
With the right data strategy in place, enterprises can better lean on the customer data they already own to adapt rapidly-evolving technology at scale, foster new growth and operations within the business, and enhance customer relationships.
Learn more about the Syniti Knowledge Platform which integrates data matching technology here.

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