Preventing Dirty Data From Entering your Business

Data is critical for modern-day marketing and sales; however, this data becomes “dirty” often, losing key attributes — accuracy, accessibility, and completeness — at any stage of the buyer’s journey, becoming unsuitable for critical use.   Integrating modern and automated techniques to minimize dirty data collection and enhance the use of cleaning data tools ****can help organizations remediate the problem before it grows into system outages, negative brand image, or lost revenue.

There are many root causes of dirty data, but some of the most common include:

• Incomplete or inaccurate data entry
• Lack of standardization (e.g. different spellings or formats for the same data)
• Data that is out-of-date
• Duplicate records

Data quality is essential to maintaining a positive brand image, and ensuring system outages are avoided, and revenue is not lost. Organizations can take several steps to keep data including

1. Use data quality assessment tools: There are numerous data quality assessment tools available on the market. Like data cleaning tools. These tools help organizations assess the quality of their data, identify and correct errors, and prevent dirty data from entering their systems in the first place.

2. Enforce data governance: Data governance is the process of overseeing and managing an organization’s data assets. It includes developing policies and procedures for managing data, assigning roles and responsibilities for data management, and establishing processes for monitoring data quality. Enforcing data governance can help organizations keep their data clean and avoid the problems associated with dirty data.

3. Use data cleansing tools: Data cleansing is the process of identifying and correcting errors in data. There are many data cleaning tools available. These tools can help organizations cleanse their data and improve its quality.

4. Implement a data quality management system: A data quality management system is a system that helps organizations manage the quality of their data. It includes a set of processes and tools for assessing, cleaning, and improving data quality. Implementing a data quality management system can help organizations keep their data clean and avoid the problems associated with dirty data.

5. Use master data management:

Master data management (MDM) is a process for managing an organization’s critical data assets. It includes developing policies and procedures for managing master data, assigning roles and responsibilities for master data management, and establishing processes for monitoring master data quality.

Data cleaning is the process of identifying and correcting these errors in order to improve the quality of the data. It is a critical part of data management, and it should be done regularly to ensure that the data is accurate and useful.

There are many different approaches to data cleaning, but the basic steps are always the same:

1. Identify the errors in the data.

2. Correct the errors.

3. Verify that the corrections were successful.

4. document the changes that were made.

The first step is to identify the errors in the data. This can be done manually or with automated tools. Once the errors have been identified, they need to be corrected. This can be done manually which takes time and effort or with the use of data cleaning tools to make it easier faster and more effective. Finally, the changes need to be verified to ensure that they were successful.

Data cleaning and enrichment can be performed manually or through automated means. Automated methods are often more accurate and efficient, and can be scaled to large data sets. Manual methods may be necessary in some cases, such as when data is very sensitive or when there is a need for human judgment.

Dirty data’s impact on sales

Sales teams are especially vulnerable to the effects of bad data. A recent study by The Bridge Group found that, on average, only 66.7% of a sales rep’s time is spent actually selling. The rest is spent on administrative tasks, such as searching for contact information or trying to keep their CRM up to date.

Bad data can have a ripple effect on an organization, costing companies millions of dollars per year. In fact, Gartner estimates that poor data quality costs businesses $3.1 trillion annually.

Make Your CRM Suitable

• Set up the CRM’s fields to match your data

You need to ensure that the fields in your CRM are properly set up to accept, record, and store the type of data you have. Otherwise, you’ll be stuck with a bunch of useless data in your CRM.

If you have an existing database, you need to map out the data fields and match them to the appropriate fields in your CRM. If you don’t have a database yet, then you need to create these fields and match them to the data you need.

• Decide how the data will be imported into your CRM

There are two ways to get data into your CRM: manually and automatically. And there are pros and cons to both methods.

-Manual import: A good way to get your feet wet with data importation. But it can be time-consuming and requires a lot of work on the front end from people who know the data well.

-Automatic import: The best way to get your data into your CRM is if you have a lot of it or if it changes often. But it requires a significant amount of planning and technical know-how.

• Duplicating existing CRM data during importation

Duplicating existing CRM data during importation can lead to errors and may cause problems with the functioning of your CRM system later on.

How to ensure good data in your CRM

1. Removing old data before importing new data

on top of old data can lead to duplicates and errors. Always delete any previously imported data from your CRM before reimporting.

1. Creating records for non-existing companies

When you import contacts, make sure their companies are already in your CRM. If not, create them first. This helps you manage your accounts more effectively and avoid duplicates.

1. Creating records for non-existing users

When you import contacts, make sure their users are already in your CRM. If not, create them first. This helps you manage your accounts more effectively and avoid duplicates. This is only relevant if you’re using a multi-user CRM.

1. Creating records for non-existing products or services

When you import opportunities or quotes, make sure their products and services are already in your CRM. If not, create them first. This helps you manage your products and services more effectively and avoid duplicates.

Wrapping up

Having accurate, clean data will help you build an experience by targeting the right demographic, increasing marketing efficacy and, thereby, your ROI.  data cleaning tools are a treasure trove of insights embedded into revenue operations workflows.

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