When you hear the word pollution, you probably think of plastic bags in landfills and emissions from a diesel engine. But for data-driven organizations, environmental pollution is not the only type of pollution to worry about. Dirty data inside of Salesforce and Marketo is a huge problem for organizations of all sizes across all industries.

Unfortunately, keeping your database clean is not as simple as picking up litter on the side of the freeway. To tackle your dirty data problem, you must first define what exactly constitutes dirty data. These are the 7 types of dirty data polluting your database — and the data hygiene practices you can use to combat each type.

The 7 Types of Dirty Data

  • Duplicate Data
  • Outdated Data
  • Insecure Data
  • Incomplete Data
  • Incorrect/Inaccurate Data
  • Inconsistent Data
  • Too Much Data

1. Duplicate Data

Duplicates are among the worst offenders of data pollution. Duplicates can form in a number of ways including data migration, via data exchanges through integrations, 3rd party connectors, manual entry, and from batch imports. The most common duplicate objects are Leads, Contacts, and Accounts.

Polluting your Salesforce or Marketo with duplicate data can cause:

  • Inflated storage count
  • Inefficient workflows and data recovery
  • Skewed metrics and analytics
  • Poor software adoption due to data inaccessibility
  • Decreased ROI on CRM and marketing automation systems

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

How to clean and prevent duplicates:

Before the age of mass data accumulation, manpower alone was enough to merge duplicates and link leads to accounts. Nowadays, there are automated solutions for detecting and merging duplicates. External solutions to duplicate data like RingLead allow users to match Leads, Contacts, and Accounts based on customizable criteria and prevent duplicates at all points of entry into Salesforce and Marketo.

2. Outdated Data

Have you ever found a report or study that looks promising, only to find out that the information is several years old and no longer relevant?

Common causes of outdated data:

  • Individuals change roles or companies
  • Organizations rebrand or get acquired
  • Software and systems evolve past their previous iterations

Such is the nature of the modern digital ecosystem, change is too rapid for the average database to keep up. Organizations need to be able to trust that their data is fresh and up-to-date before using it for insights, decision-making, and analytics.

How to keep data fresh and up to date:

Purging your database of records created before a certain date can help expedite the process of cleaning outdated records. RingLead’s Mass delete tool lets you bypass Salesforce restrictions of 250 records at a time, allowing you to delete thousands of records that have no business use to you.

Data enrichment can also solve the pitfalls of outdated customer records by appending fields with newer information.

The 7 Most Common Types of Dirty Data (and how to clean them)

3. Insecure Data

Data security & privacy laws are being put into place left and right, giving business extra financial incentive to follow these newly placed laws to a tee. With steep fines for non-compliance, insecure data is quickly becoming one of the most dangerous types of dirty data.

Digital consent, opt-ins, and privacy notifications are becoming the new norm in an increasingly consumer-centric business landscape.

Major data privacy laws include:

Without proper database hygiene, remain within these stringent regulations becomes nearly impossible. For example, let’s say an individual does not consent to your data sharing policy but their customer profile is fragmented throughout your organization’s various databases. Adhering to this opt-out will need manual intervention to reconcile all instances of this personal information (and let’s be real, no one is doing that).

How to remain within data privacy regulations:

Disorderly databases are the most likely candidates to house insecure data. There are several data hygiene practices you can implement to combat insecure data.

  • Delete outdated & unusable records form Marketo and Salesforce
  • Merge duplicates to prevent fragmented profiles
  • Automate lead-to-account linking
  • Consolidate your stack as much as possible

With a clean database, complying with data privacy regulations becomes an afterthought.

4. Incomplete Data

A record can be defined as incomplete if it lacks the key fields you need to process the incoming information before sales and marketing take action.

For example, let’s say your organization is running a campaign to target non-profit institutions. If a new or existing record is missing the ‘industry’ or ‘sector’ fields, it will not be included in smart lists for the campaign and a valuable revenue opportunity may be missed.

It’s not rocket science to know that more data points on a record equal more insights. Data processes like lead routing, scoring, and segmentation rely on an aggregation of key fields to operate.

How to Fix Incomplete Data:

The first option to combat incomplete records would be to manually conduct research to append the missing fields, but you will soon find that this strategy is not realistic nor scalable. Enriching your data with a data service like RingLead before the lead gets handed to sales is the best way to automate the filling of empty fields and gain a more complete profile of targets and customers.

5. Inaccurate/Incorrect Data

Collecting consumer data is all about gaining a better understanding of your customers to inform strategic business decisions. And this all depends on the data you collect being accurate and complete. In fact, 69% of Fortune 500 companies say inaccurate data undermine their efforts (Experian). Extracting insight and making data-driven decisions based on dirty data can have disastrous results for any organization.

Incorrect data is data that is stored in the improper location e.g a text field containing a numerical value

Inaccurate data, on the other hand, occurs when a field is filled but the information is not correct e.g a fake email address

These each can cause a slew of issues, including poor targeting and segmentation, irrelevant non-personalized messaging, mistimed or nonexistent email delivery, and a lack of competitive insights. It’s not marketing 101 to know that accuracy and precision matter in all data-driven business decisions.

How to clean incorrect and inaccurate data:

Keeping track of all data entry points and diagnosing the cause of inaccurate data is the first step towards combating this type of bad data. If the problem is caused by external data sources (web forms or connected systems), seeking an external solution is the best option to maintain accuracy.

Data enrichment services like RingLead can correct mistakes and override dirty data with clean data sourced from reliable vendors. By augmenting existing data with purchased third-party information, organizations can attain more accurate data that may not have been possible before. The video below demonstrates how RingLead on-demand enrichment works.

6. Inconsistent Data

Just like how duplicate records exist in various places within your database, multiple versions of the same data elements can exist across different records in your Salesforce or Marketo system.

Inconsistent (non-standardized) data is data that looks different but represents the same thing.

For example, let’s say you were targeting decision makers for an upcoming email blast and wanted to segment all “Vice President” roles into one persona. ‘V.P’ ‘v.p’ ‘VP’ & ‘Vice Pres’ all mean the same thing, yet will only be included if you are certain all variations exist in your smart list or campaign. Inconsistent data hurts analytics and make segmentation that much more difficult when you have to account for all variables of the same title, industry, or other criteria.

How to standardize your data:

First, create standard naming conventions and ensure your organization follows this closely going forward. As for existing inconsistent records, tools like RingLead can normalize records in batch for more unified field names and more accurate segmentation.

Integrating in a data management tool that can standardize data from multiple sources can help to create a centralized approach to data management. This will enable data to be processed, analyzed, and leveraged across each department within an organization, enabling a successful data sharing strategy and increased accessibility throughout your organization.

7. Too Much Data

Yes, data hoarding is a thing. And even though you won’t find yourself in a TLC show for data hoarding, this oft-overlooked issue is a big problem in many organizations.

Data Hoarding causes:

  • slower data exchange
  • inflated record counts
  • failure to stay within storage compliance limits.

Maintaining a sleek (but not small) database is a big part of data hygiene, driving alignment between departments and improving accessibility throughout your organization.

How to reduce database size:

While it might seem like “too much data” can never be a bad thing, more often than not, a good portion of the data simply isn’t usable. This means that your team is spending excess time digging through the bad so they can get to the good. Data hoarding and outdated data go hand in hand. So you will find that these two types of dirty data can be solved at the same time.

RingLead’s Mass Delete tool allows users to bypass Salesforce 250 record action limit to delete thousands of records at once.

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