Data Enrichment

The 5 Most Common Types of Dirty Data (and how to clean them)

Table of Contents

Today’s data-driven world is flooded with so much information that it can be easy to ignore the fact that there is more to dirty data than just erroneous data. Just like our environment is plagued by plastic bags and automobile emissions, so are our businesses plagued by dirty data. Dirty data inside of CRMs such as Salesforce is a huge problem for organizations of all sizes across all industries.

What is dirty data?
If you were to Google dirty data, you would involuntarily come across articles that speak about incomplete, inaccurate, inconsistent, and duplicated data as the definition of dirty data. However, the truth lies beyond that.

According to The Data Warehousing Institute (TDWI), an estimate shows that dirty data costs U.S. businesses more than $600 billion each year.

Unfortunately cleaning a database is not as simple as cleaning our house or our locality. To deal with this data problem we first need to define what all aggregates to dirty data. This article speaks about the 5 types of dirty data that pollute most of the database and the practices you need to combat them.

1. Duplicate Data
Duplicate data are records or entries that negligently share data with another record in your database. The most common form of duplicate data is a complete carbon copy of another record. These are considered to be the worst kind of data pollution. The most typical duplicated objects are contacts, leads, and accounts.

Duplicate data in your CRM can lead to:
Decreased ROI on CRM and marketing automation systems
Poor customer service
Biases in metrics and analytics
Poor targeting and wasted marketing effort
Inaccurate reporting and less informed decisions

Duplicates have no place in the system of any data-driven organization. Ridding your CRM database of duplicates should be a top priority in any data hygiene campaign.

2. Outdated Data
Imagine you found a report which fits your project, only to later discover that the report is outdated. So, Outdated data is basically information that is incorrect, incomplete, or simply no longer in use.

Common ways how outdated data is accumulated:
Unwanted duplicate copies of emails
Individuals who have changed roles or companies
Old server session cookies
Web content that is no longer accurate
When organizations rebrand or get acquired
Software and systems when evolving from their previous iterations

Check out: Firmographics Enrichment

3. Incomplete Data
Incomplete data is the most common occurring dirty data. A record that lacks key fields on master data records such as industry type, title or last names, etc. which are useful for business. For example, if you failed to classify your customers by industry, you cannot target your sales and marketing initiatives by industry. Imagine trying to sell geolocation software to a prospect who is located at “N/A”.

4. Inaccurate/Incorrect Data
Collecting information about your customers helps in better understanding them and making informed decisions to satisfy them. This can be only possible if data is collected properly, completely, and accurately and can also lead to costly blunders.

Incorrect data: It occurs when the field values are generated outside of the valid range of values. For example, when filling a month field the range should encompass ranges from 1 to 12, or a house or office address should be a valid address.

Inaccurate data: There are many instances where the data on a field is correct but inaccurate considering the business context. Inaccurate data can lead to costly interruptions. For example, errors in a customer’s address can lead to the delivery of the product at the wrong location even though the address on which it was delivered is correct.

Stats related to Inaccurate/Incorrect Data:

43% of sales and marketing teams say that it’s a challenge for them to battle lack of accurate data
54% of B2B businesses say that they cannot achieve success due to the lack of data quality
69% of Fortune 500 companies say inaccurate data cripple their efforts

5. Inconsistent Data
Inconsistent data are also known as data redundancy is when the same field value is stored in different places, which leads to inconsistency. For example, companies have customer information on multiple systems, and data is not kept in sync.

The problem with inconsistent data can be explained, for example, if you want to target all “Vice President” for an upcoming email marketing campaign. Since ‘V.P’ ‘v.p’ ‘VP’ & ‘Vice Pres’ all mean the same thing, however, these would only be included in the campaign if all these variations are included in the campaign list. Inconsistent data hinders analytics and makes segmentation difficult when you have to consider all variables of the same title, industry, etc.

Best Practices for data cleaning
Following are some of the best practices which can be considered while data cleaning.

Create a Data Quality Plan
It is vital to create a clear expectation of how an ideal database should be like. It is advised to create KPIs (key performance indicators) for every staff involved in your project. What are these KPIs and how will your staff accomplish them? Which methods should be used to account for the health of your data? How can you continuously maintain data hygiene?

By regularly applying best practices for data cleansing, you can learn more about error occurrence, identifying incorrect data, and comprehending the cause of data health problems. This will lead to the maintenance and cleaning data for the future.

Standardize Contact Data at the Point of Entry
It is quite difficult to maintain good data hygiene when you allow unhealthy or faulty data to enter your database. Even before the date cleaning can happen it is vital to check data at all points of entry. This will ensure standardized information input and will help in eliminating duplicate data.

One way to do this is to ask your team to create an SOP (Standard Operating Procedure) for data entry. Following this SOP will only allow quality data to enter your database at the point of entry.

Validate Data Accuracy
Validating data accuracy in real-time is a challenge. There are some tools such as list imports for data cleaning. There are various hygiene tools to include phone, email, and address verification.

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

Merge Duplicates
Duplicate data in the CRM database could lead to the waste of marketing and sales efforts. This prevents you from having a crystal clear picture of your entire database. It is always advised to merge the duplicates and cleans up the database quickly. For every minute that goes by, there will be fewer duplicates until finally, no duplicates will remain.

We recommend merging the duplicates versus deleting data. Every little piece of data holds value, so merging is always recommended. However, to ensure the duplicates are merged with the right contact, you will need to set a master rule set. In this way, you can have new data matching the master or original record that will match and merge automatically. For example, if you have 5 records in Salesforce, you are likely to keep the lead source from the original/master record, however, use all the current titles and phone numbers fields from all the recent entries.

When you can identify the very source of dirty data plaguing your database you can prevent inaccurate or duplicate data from heaping up. Using a powerful data management solution will help you get the data you desire. Using Unthinkable data management services will help you increase revenue and new customer acquisition with just a few clicks. Request a demo today.

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