Oftentimes, marketers do not take into consideration many outside, influential aspects when buying contact data. Whether it be age, lack of accuracy, redundancy, or data-entry related errors, it is overlooked more often than not – mostly because the thought of identifying and correcting errors in a massive database is nothing short of daunting. Thus, when searching for a data provider, it is important to find one who can not only provide you with clean information but one who can ensure that the information you need stays up-to-date. Below are some of the best practices for cleaning a dirty, outdated database.
Where Does Dirty Data Originate?
Dirty data stems for a multitude of reasons and factors. In this day and age, with the transient workforce and people constantly changing jobs every few years, contact data is constantly changing and becoming outdated. Therefore, it is almost inevitable that databases are constantly expiring. This is perhaps the most common element in data being dirty or old. In addition to the fast-pace of society today, there are other factors that lead to the degradation of contact data.
According to RedBase Interactive, on average, contact lists decay at around 25% annually. That means a quarter of current database will be invalid and not useful in just a year. While some people believe this can be avoided with call center verification, this strategy often fails because by the time the call center representative gets to the bottom of the list, the top is already bad again.
Other causes for dirty data include legacy data and duplicative data. Legacy data, frequently the most dirty, is data that is inherited over time. While it might have contained good contacts that no one would want to let go of at one point, it is rarely cleaned; thus, it too becomes old and out-of-date. Duplicative data typically arises via data entry or the merging of 2 disparate data systems. Both of these causes are wastes to the budget you have set aside for data investments.
Finally, there is always simple human error to blame. It is easy to misspell a name or address during data entry, which can furthermore affect how it is formatted in your CRM or MAP. The best solution is to cleanse your database more frequently to ensure that it is more accurate, up-to-date, and comprehensive.
Best Practices for Data Cleaning
The first key element in database cleansing is the development of a data quality plan. Identifying the most common data quality errors and incorrect data will help your team find the root of the problem and develop a plan to fit accordingly. Setting forth a plan and procedures to constantly be checking the data is the best way to ensure your tasks, campaigns, and projects are being delivered and completed on time. For instance, appointing a key stakeholder to create a schedule with solid dates for tasks and projects will let you know that you need to cleanse the data before they are launched. This way you save a considerable amount of time and money in marketing efforts later down the road.
- ID Bad Records
The next step in data cleansing is to develop a process by which you can find and identify bad records. With such robust contact records that exist today, it creates even more opportunity to generate bad data – old email addresses, inaccurate names, titles, locations, addresses, etc. It is nearly impossible to identify all bad records manually. Conversely, using a partner company to manually go through and find inaccurate, poor records will have a hefty price tag and still may not catch all the bad records. The best solution to solving this issue is with automation or a partner that has an automated data cleansing tool. This method is particularly efficient if you have a large database. Finding an automation source that can track and locate mistakes and bad records makes the most difficult step in the data cleansing process a piece of cake!
Standardization is the most important step in the data cleansing process. This all starts at the original point of data entry. Whoever is entering the data into the database should devise a standardized, consistent format with rules for how the data is put in, as well as procedures for inputting data moving forward. By constantly following the same format, it will be easier to find errors in database in the future. This includes duplicates, spelling errors, contradicting information, and more.
In this portion of the data process, there should be a step for ensuring validation of the data. There is no point in bothering to buy data if you cannot be assured that it is accurate information. Invest in tools that can identify old/inaccurate information and replace it. Some data provider have the ability to validate email addresses and phone numbers, which is a worthwhile investment because it will save you time.
Additionally, a very important practice in the database standardization process is identifying duplicates. By implementing a consistent format and incorporating data management tools to identify and remove duplicates, you save your team a lot of time. Plus, since tools for sorting and finding duplicates exist, it makes the work a lot less manual, which is amazing.
Last, and most certainly not least, is data appending/enriching. Once your data has been standardized, validated, and duplicates have been removed, see if there is any information that needs to be updated, changed, or added. Some data providers, like Synthio, allow marketers to upload their databases into a system to see if any additional information can be added. This helps strengthen targeting and segmentation to customers and potential prospects.
Database cleansing is not a one-time thing. With the rapid pace of today’s society, data needs to be cleaned at least once a quarter. When selecting a data provider, make sure that your vendor can continually clean your data and keep it up-to-date, ensuring that you get a better return on investment. This process, called Assurance, saves you time, money, and energy.
There are many ways that dirty data originates; however, you cannot expect to see ideal results from your marketing effort if you do not make sure the data is clean! Develop a plan to check for: standardization of data, validation of accuracy of the data, a way to identify and remove duplicates, and constantly append your data. Since change is constantly occurring the modern age, it is vital to make sure that you can continually update your data to see what information has changed on contacts. Remember, don’t select a data provider without being assured that there is a cleansing plan!