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How To Make Your Data Accurate & Actionable

Written by Josh Hines • September 08, 2024 • 4 Minute Read

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What Is Data Hygiene

Data hygiene is the process of keeping databases clean and error-free. Data that is outdated, incomplete, duplicated, or simply incorrect is known as dirty data.

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Why Data Hygiene Is Important

Bad data can have significant and far-reaching costs for organizations. Here's an overview of what bad data could be costing you:

  • Direct Financial Losses: according to Gartner, poor data quality costs organizations an average of $12.9 million per year
  • Lost Revenue: bad data can lead to mistargeted marketing campaigns, overlooked high-potential customers, and ineffective sales strategies
  • Wasted Resources: advertisers waste approximately 21% of their media budgets due to bad data
  • Decreased Productivity: nearly one-third of analysts spend over 40% of their time vetting and validating data before it can be used for decision-making
  • Inefficient Processes: poor data quality complicates data ecosystems and contributes to inefficient business operations
  • Employee Frustration: dealing with bad data can lead to decreased job satisfaction and potentially higher employee turnover
  • Escalating Costs Over Time: the "1x10x100 rule" suggests that the cost of addressing data quality issues increases exponentially the longer they go undetected

By investing in data quality management and implementing effective data observability practices, organizations can mitigate these costs and maximize the value of their data assets. This not only helps avoid financial losses but also improves decision-making, enhances operational efficiency, and maintains a strong reputation in the market.

What Are The Benefits Of Data Hygiene

Maintaining good data hygiene is crucial for several reasons:

  • Improved Decision Making: clean, accurate data leads to better-informed business decisions
  • Increased Productivity: reduces time spent on manual data cleaning and correction tasks
  • Cost Savings: prevents wasted resources on marketing campaigns targeting outdated or incorrect data
  • Enhanced Customer Experience: enables more personalized and effective customer interactions
  • Regulatory Compliance: helps organizations adhere to data protection regulations like GDPR and CCPA
  • Data Integrity: ensures the reliability and consistency of data across systems

Best Practices For Data Hygiene

To maintain good data hygiene, organizations should:

  • Conduct regular data audits
  • Implement data quality checks
  • Establish clear data governance policies
  • Train employees on data hygiene practices
  • Use automated tools for data cleansing and validation
  • Standardize data entry processes
  • Regularly update and verify data against external sources

Three Biggest Dirty Data Drivers

When thinking through what drives dirty data, the three biggest culprits are:

  • Inaccurate Data: data that contains errors, incorrect values, or mistakes. Examples include misspelled names, wrong addresses, or incorrect phone numbers
  • Incomplete Data: data that has missing or null values. For instance, customer records with missing contact information or incomplete form submissions
  • Duplicate Data: occurs when the same information is recorded multiple times, leading to redundancy and confusion. A common example is when a customer exists in a CRM system multiple times due to slight variations in how their name is entered

Cleaning Up The Three Biggest Drivers Of Dirty Data

To address the three biggest types of dirty data - inaccurate, incomplete, and duplicate data - here are some effective strategies:

Fixing Inaccurate Data
  • Implement Data Validation Rules: setup automated checks to catch errors during data entry
  • Use Data Quality Tools: employ software that can automatically identify and correct inaccuracies, your HubSpot CRM has this built in
  • Regular Audits: conduct thorough audits of your data to identify discrepancies and inconsistencies
  • Cross-Reference With Reliable Sources: verify data against trusted external databases or sources like ZoomInfo, Clay, or Apollo
  • Train Staff: educate employees on proper data entry procedures and the importance of accuracy
Addressing Incomplete Data
  • Make Key Fields Mandatory: Require essential information to be filled before submission
  • Use Default Values: where appropriate, set default values for fields that are often left blank
  • Implement Data Completion Tools: use software that can fill in missing information based on existing data, HubSpot just recently bought ClearBit, so we're excited to see how they use that data within the CRM
  • Collect Data In Stages: break down data collection into manageable steps to ensure completeness
  • Regular Data Updates: establish processes to periodically update and complete partial records
Eliminating Duplicate Data
  • Use Unique Identifiers: assign unique IDs to each record to prevent duplication
  • Implement Deduplication Tools: utilize software that can automatically detect and merge duplicate records
  • Standardize Data Formats: ensure consistent formatting across all data sources to make duplicates easier to spot
  • Create A Centralized Database: Maintain a single source of truth to reduce the likelihood of duplicates, like HubSpot
  • Regular Data Cleansing: Schedule routine deduplication processes to keep your database clean

By implementing these strategies, organizations can significantly improve their data quality, leading to more accurate analyses and better decision-making. Remember, maintaining data hygiene is an ongoing process that requires consistent effort and attention.

Next Chapters In The Guide

In the next chapter of this guide, discover how to enrich your contact and account data so that you can better segment your customer lists and create better personalized customer journeys to increase conversions and revenue.

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Moving From Hygiene To Data Enrichment

Data enrichment is the process of enhancing raw data by combining it with additional information from various sources to improve its accuracy, completeness, and overall value.

Read The Next Chapter →

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