Data is the foundation of your marketing strategy, and you should be aware of the opportunities and challenges tied to data quality. Having data that is accurate, high-quality, and easy to use is a must for driving growth.
Today, B2B marketers have vast amounts of data leading to some of their biggest opportunities and challenges. Our new research with the Winterberry Group outlines that many are struggling with data collection and integration.
By focusing on data hygiene, you can combat bad data and drive success this quarter and beyond.
What is data hygiene and why it matters
Data hygiene refers to the processes and practices used to improve and maintain the quality of data assets. This includes detecting and correcting errors, inaccuracies, duplication, and incompleteness.
Oftentimes, the data you collect will be unstructured data and may have quality issues that need to be fixed before you can use it. As an example, data that’s collected from social media is unstructured data that needs to be processed before it’s used for analysis or to make business decisions.
Data quality issues touch every aspect of business, but marketers in particular need to address bad data issues in order to get results. The good news is there are several tried and true data hygiene tactics that can be used to improve data quality.
Proper data hygiene leads to:
- Increased efficiency
- Improved data analytics
- Better-informed business decisions
- Higher-quality customer experiences
- Greater ROI from data initiatives
On the other hand, poor data hygiene results in:
- Duplicative and outdated data
- Inaccurate reporting
- Flawed analysis and insights
- Missed opportunities and lost revenue
- Poor customer experiences
- Increased costs
3 common signs you need to address your data hygiene
1. Reduced customer engagement: Creating effective engagement with customers becomes extremely difficult if customer data is inaccurate, inconsistent, and outdated. A combination of wrong insights, incomplete buyers’ personas, and other misleading information can negatively affect business outcomes, customer experience, and ultimately, cause reputational damage.
2. Increased loss of sales: Because of changes in jobs, roles, and several other factors, data can quickly become outdated and you won’t be able to use it to reach leads.
Furthermore, it isn’t uncommon to get bad contact data—including invalid email addresses, stale or duplicate information, missing fields, and improper formatting that requires human correction. When inaccurate information is added to a company’s database, it often results in flawed lead generation, missed sales opportunities, lost time, and customer dissatisfaction.
3. Downtrend in revenue: According to Gartner, poor data quality costs companies an average of $12.9 million in losses yearly. Having incorrect data can make it hard to target the right audiences, understand your buyers’ needs, and ultimately make data-driven decisions about your marketing and business strategies as a whole.
3 proven data hygiene tactics for fixing bad data problems
It’s safe to say that there are many challenges when it comes to data and its use, but fortunately, there are several effective ways to address and fix various data challenges. This often results in creating data that helps optimize a company’s marketing and sales efforts better.
- Identify and fix duplicate data
Duplicate data causes several issues and interferes with the marketing automation process. To successfully track the flow of duplicate records from multiple sources and to prevent duplicate records from flowing into your existing database, consider doing the following:
- Set up trigger alerts to automatically be notified about duplicate data
- Analyze and refine your database to catch any existing duplicates
- Investigate sources and processes that generate duplicate records
- Introduce a smooth data capture strategy
A data capture process enables a company’s sales and marketing team to collect accurate lead information and discover more about leads. To create a smooth data capture process, you should do the following:
- Track wrong email addresses or contact information within the customer database
- Remove unnecessary fields from web forms, and leverage restricted values, field validation, or field pre-population
- Employ automated forms to auto-fill certain fields to make it more convenient for prospects
- Normalize your data
For an organization’s data to be useful, it must be precise, consistent, and recent. Organizations should be able to read, search, and use each segment the same way across all records in their customer database. Data normalization can help in this area by standardizing the formats of fields and records within a database. It also minimizes the cost and time associated with managing a database, locating missing information, and analyzing it for decision-making.
Core data hygiene practices
Fixing challenges that pop up due to bad data is a priority, but it’s also important to get into a routine data hygiene process to keep your data accurate and usable. Following core data hygiene best practices is key to maintaining high-quality data:
1. Develop data governance policies
Data governance provides the policies, guidelines, processes, and tools to manage data effectively. This includes establishing:
- Data principles - Guidelines for how data should be managed at an organization
- Data quality standards - Rules and metrics to define what constitutes good quality data
- Stewardship model - Define data owners accountable for implementing governance
- Issue escalation processes - Procedures for reporting and resolving data quality issues
2. Profile and monitor data
Profiling data involves analyzing it to identify patterns, relationships, errors, and anomalies. This provides insights to formulate data quality rules and standards.
Ongoing monitoring then tracks data quality KPIs over time to quickly detect issues. This includes:
- Validity - Data conforms to syntax and domain rules
- Accuracy - Data reflects the real-world entity
- Completeness - Required attributes are populated
- Consistency - Uniform format and values across systems
- Duplicate data - Redundant or repeated records
3. Detect and resolve data errors
Despite your best efforts, some bad data will enter systems. Performing periodic data audits to detect errors is key. This allows you to pinpoint issues and focus data cleansing efforts.
Common data cleansing techniques include:
- Standardization - Transform data into consistent formats
- Deduplication - Merge, delete or mark duplicate records
- Error correction - Fix formatting, spelling, syntax issues
- Missing value imputation - Replace missing data based on valid values
- Data enrichment - Append external data to add context
4. Delete obsolete data
Outdated, obsolete data should be archived or deleted altogether. This improves productivity by removing the need to manage stale data.
- Flag records based on validity periods
- Archive data after set retention periods
- Seek business user input to identify obsolete datasets
- Delete unused, old application databases and files
Data hygiene best practices
Follow these top data hygiene best practices to maximize business value:
- Institute data governance early – Don’t underestimate the value of governance for long-term data quality
- Involve business stakeholders – Continually seek input to identify pain points and obsolete data
- Fix problems at the source – Address bad data during collection when possible
- Know your metrics – Measure validity, accuracy, and completeness to expose problems
- Make it sustainable – Ingraining hygiene into workflows avoids a one-off project mentality
- Keep it simple – Start small, focus on high-impact areas first before expanding efforts
- Make it a shared responsibility – Foster a data-driven culture with shared accountability
Remember that it’s not too late to tackle any data challenges you’re facing. While maintaining pristine data hygiene may seem like a massive undertaking, starting with an incremental approach can yield major benefits.
Data hygiene is foundational to getting maximum value from data for B2B marketing. Following best practices pays off through improved efficiency, cost savings, informed decisions, and customer experiences.