Common Types of Dirty Data and How to Clean that Data Yourself

Today's data-driven world is so saturated with information that it would be easy to ignore the fact that there is worse data than inaccurate data. Just as our environment is plagued by plastic bags and automotive gas, so are our businesses plagued with dirty data. Dirty data within CRMs like Salesforce is a major problem for organizations of all sizes across all industries.

What is dirty data?

If you were using Google's obscene data, you would not encounter articles that talk about incomplete, incorrect, inconsistent, and duplicate data as descriptive data. However, the truth is more than that.

According to the Data Warehousing Institute (TDWI), the measure shows that contaminated data is costing U.S. businesses. greater than $ 500 billion each year.

Unfortunately cleaning a website is not as easy as cleaning our house or our place. To deal with we first need to find what constitutes all dirty data. This article discusses 5 types of data that pollute most of the sites and processes you need to combat.

1. Duplicate data

Duplicate data records or entries that negligently share data with another record on your website. The most common type of duplicate data is the completely same as another record. These are considered to be the worst form of data pollution. The most common duplicate items are contacts, track, and accounts.

Duplicate data on your CRM can lead to:

Reduced ROI in CRM and automated marketing programs

Invalid customer service

Bias in metrics and statistics

Misdirection and wasted marketing effort

Incorrect reporting and poorly informed decisions

Duplicate Data has no place in the system of any organization that deals in data-driven. Extracting your CRM duplicate database should be a priority for any data purification campaign.

2. Expired Data

Imagine getting a report that matches your project, only to discover later that the report has expired. Thus, Outdated data is basically incorrect, incomplete, or obsolete information.

Common ways in which outdated data is collected:

Duplicate copies of unwanted emails

People who change roles or companies

Old server time cookies

Inaccurate web content

When organizations reproduce or acquire

Software and systems where it changes from your previous duplication

See: Improving Firmographics

3. Incomplete Data

Incomplete data is the most common contaminant. A record that lacks important fields in key data records such as category, title or surname, etc. is useful for business. For example, if you have failed to differentiate your customers by industry, you cannot target your sales and marketing by industry. Imagine trying to sell geolocation software to someone who might find "N / A".

4. Incorrect / Incorrect Data

Gathering information about your customers helps to better understand them and to make informed decisions to our satisfaction. This can only happen if the data is properly collected, completely, and accurately and can lead to costly errors.

Incorrect data: Occurs when field values are generated outside a valid value range. For example, when filling in the monthly field the width should include a width from 1 to 12, or the address of the house or office should be a valid address.

Incorrect data: There are many instances where field data is correct but incorrect considering the context of the business. Incorrect data can lead to costly disruptions. For example, errors in a customer address may result in the delivery of the product to the wrong location even though the address provided is correct.

Statistics related to incorrect / incorrect data:

43% of sales and marketing groups say it is a challenge for them to fight the lack of accurate data
54% of B2B businesses say they cannot achieve success due to lack of data quality
69% of Fortune 500 companies say that incorrect data hampers their efforts

5. Incompatible Data

Incompatible data is also known as data redundancy where the amount of the same field is stored in different locations, resulting in exchange. For example, companies have customer experience across multiple systems, and data is not stored in sync.

The problem with inconsistent data can be explained, for example, if you want to target every "Vice President" with an upcoming email marketing campaign. As ‘V.P’ ‘v.p’ ‘VP’ & ‘Vice Pres’ all mean the same thing, however, these will only be included in the campaign if all these differences are included in the campaign list. Incompatible data hinders analysis and makes division difficult if you have to consider all types of the same topic, industry, etc.

Best Practices for Cleaning Data

The following are some of the best ways to consider the data.

Create a Data Quality Program

It is important to build clear expectations of what the right website should look like. It is recommended that you create KPIs (key performance indicators) for all employees involved in your project. What are these KPIs and how will your employees implement them? What methods should be used to account for the health of your data? How can you maintain a clean record?

By regularly using advanced data purification procedures, you can learn more about errors, error detection, and understanding the cause of data health problems. This will lead to future data storage and cleaning.

Rate Contact Data on Login

It is very difficult to maintain good data purity if you allow incorrect or incorrect data to enter your database. Even before the cleaning day takes place it is important to check the data at all access points. This will ensure the installation of quality information and will help to remove duplicate data.

One way to handle this is to guide your team to create an SOP (Standard Operating Procedure) for data entry. Following this SOP will allow quality data to enter your database.

Verify Data Accuracy

Verifying real-time data is a challenge. There are other tools like imported data purification lists. There are various hygiene tools including phone, email, and address verification.

Note- Effective marketing campaigns only occur when a company uses high quality data and the right tools to easily integrate various data sets.

Combine Repetitions

Duplicate data on a CRM website can lead to a waste of sales and marketing efforts. This prevents you from having a clear picture of your entire website. It is always advisable to add duplicates and clean the site as soon as possible. With each passing minute, there will be a few repetitions until the end, no duplicates will be left.

We recommend combining duplicates and dealing with data deletion. Every small piece of data has value, so integration is always recommended. However, in order to ensure that duplicates are linked to the correct contact, you will need to set a master rule. This way, you can have new data like the primary or original record that will match and merge automatically. For example, if you have 5 records in Salesforce, you may have kept the leading source in the original/primary record, however, use all current titles and phone number fields in all recent entries.

Conclusion

If you can identify the source of the malicious data that is plaguing your site, you can prevent malicious or duplicate data from accumulating. Using a powerful data management solution will help you get the data you want. Using unimaginable data management tools will help you increase revenue and the acquisition of new customers with just a few clicks. Request a demo today.