Data quality is essential, whether you’re a small startup or a multi-billion dollar international organization. Nothing can undermine or torpedo revenue, employee satisfaction, or company growth, than having bad data. According to SiriusDecisions:

“The major impact of bad data on conversion rates really becomes clear when we roll these stages up and look at the difference between an average and strong organization. Using an example of a prospect database of 100,000 names at the outset and a constant campaign response rate of two percent, a strong organization will realize nearly 70 percent more revenue than an average organization purely based on data quality.”

For international companies, data quality can be an even bigger challenge. With multiple languages, dialects, country codes, and more, international companies are seeing more duplicates, inaccurate reports, and poor automation and segmentation.

Here are four ways to attack bad data at the international organization level.

1. Configure your database correctly from the start

Configure your database to support multiple languages. This includes considerations such as spell check, automaton, deduplication, standardization, etc. Languages such as Chinese, which include non-standard or double byte characters, may conflict with traditional CRMs and add-on applications, such as deduplication apps.

2. Learn about your existing data

Before you can ensure data quality, you need to know the overall state of your existing data. Analyze and benchmark data quality. Start by asking yourself the following questions:

  • How bad is our duplicate situation?
  • Where are the duplicates coming from?
  • How does our data quality stand up against other databases and organizations?

Establishing a duplicates dashboard will tell you not only how many duplicate leads you have, but where they are coming from, when they were created and other key, actionable information.

3. Standardize your data

Data standardization is a key part of ensuring data quality. Lacking standardization results in bad data, which has numerous negative effects, from sending bad emails, to mailing to bad addresses, to losing customers altogether. For international companies, dates and currencies may differ depending on the country. It’s important to standardize and format dates, time zones, country codes, and more. In addition, setting a global or standard time zone in your CRM will help with reports.

Data standardization, also known as normalization, creates an enforced, organized and consistent environment for entering data into your CRM.

Here’s an example of data that is not standardized, in the form of many ways to write “Director of Human Resources”:

  • Director of Human Resources
  • Director, Human Resources
  • Director of HR
  • Director HR
  • Human Resources Director
  • HR Director

The values of “Director HR”, “Director of Human Resources”, and “Human Resources Director” are, in essence, the same job title. However, to case-sensitive reporting systems such as Salesforce, or a marketing automation database, these are all different values. These different values occur because the data comes in through many different outlets, such as web forms, manual entry, list uploads, etc., resulting in multiple ways to phrase the same title.

Don’t expect perfection: data normalization is an ongoing process for improving data hygiene over time. Individual “hiccups” can be separated out for manual follow-up, but the normalization process can significantly reduce the manual effort needed, and tools like ours can get you there faster.

4. Shield Your Data

Without a protection strategy, your data will continually decay. Not only do phone numbers, emails and titles change, but as your employees are entering data into your CRM, they are creating duplicates and entering data inconsistently. An employee in one country may enter data differently than an employee in another country. Moreover, as you grow, scale and add more countries to your organization, new data will come with it.

Your protection strategy must include:

  • Ongoing duplicate prevention
  • Ongoing standardization
  • Continuing to complete missing data, validate data, and update existing data

If you remember the key steps to data quality, you’ll be on your way to a stronger, faster and more robust global organization.

Achieve total data quality with the free complete guide below.