Types of Bad Data and How to Combat Them
Companies rely on deep and accurate data to make a variety of critical business and marketing decisions. When data is inaccurate or unclear, it hampers your ability to make the best decisions.
The following is a look at some of the most common bad data types, along with strategies for fixing bad data!
Duplicate Contact Records
A duplicate contact record means you have the same person represented by more than one contact profile in your database. This problem leads to expensive redundancies in your prospecting and marketing efforts. The medical field is trying to combat issues with inaccuracies and fraud stemming from mismatched documents and patient records resulting from duplication.
One of the most common strategies to guard against duplication is a database structure that requires a unique entry in every profile field. This approach doesn’t stop duplication where contacts provide different names, mailing addresses and e-mail addresses. Some software programs have enhanced intelligence that triggers manual reviews when names or other data points are “closely related” to others already registered. For instance, an entry for “Janie Smith” may cause a review on an account for “Jane Smith.”
A system for validating data, or checking it against rules, constraints, and routines, is the ideal long-term strategy.
Incomplete Profile Fields
In some cases, your data problem results from incomplete fields. This problem is typically caused by a lack of diligence on the part of a company rep or a lack of desire from a contact to complete every step on a survey or application.
Training and coaching your reps on the importance of thorough contact information and data helps internally. For contact self-reporting, use input forms that require all necessary data points before allowing submission. Periodically conduct account reviews to identify any missing data points.
Inaccurate Data Entries
Inaccurate data entries occur when a company rep or the contact input a data point that is not correct. If the prospects phone number is “555-333-2222” and the inputted number is “555-333-2221,” the net result is your inability to efficiently make a call. Beyond communication disruption, inaccurate data harms the integrity of analytics reporting.
At the time of data collection, it is up to your team to ensure accurate entry of all data points. Hiring quality data processors and training them on accuracy is job one. You also need a productive work environment where your team can concentrate on accurate data inputs.
Even with initial accuracy, data becomes plagued over time as people move or change contact points. Continuously reviewing data with contacts is your best safeguard against data becoming out-of-date.
Incompatible Software Migration
In some cases, data problems don’t result from manual inputs, they result from problems during migration from one database to another. You could end up with misaligned fields and inputs, which leads to exhaustive manual reviews and fixed.
To avoid software incompatibility problems, ensure you migrate to a software that supports your current database structure and that your team knows how to effectively make the switch.
As you can imagine, these common data problems are a nightmare for a data-driven company. The safeguards mentioned are also expensive and sometimes hard to implement.
For many companies, the best alternative is to outsource data processing solutions to a company that specializes in this area. Let Salesgenie be your expert data partner when you start your free trial!