Troubleshooting data challenges isn’t the most pleasant job around — but someone has to do it when and if a crisis arises.
But remember: Not all challenges are created equal. Some data hiccups are worse than others, so it’s best to be prepared for the worst. Your association’s reputation and security depend on it.
Let’s take a look at why data governance is important, then examine common CRM data issues and solutions that help guide you through the storm.
[Related: Association Management Systems (AMS): Benefits and Options]
Data Governance: What Is It, and Why Is It Important?
One way to take (and maintain) hold of your association’s important information is proper data governance. It’s vital when managing and resolving data challenges.
But what does data governance entail? Well, it includes strategies for maintaining data quality and integrity. In sum, it refers to your data’s overall availability, usability, integrity and security.
Implementing sound policies and strong standards is key to managing your data effectively — especially to comply with relevant regulatory requirements.
So, what’s the goal of data governance?
Simply remember that your main data governance goal is to ensure your members and other organizations can:
- Can trust your data
- Can trust you to manage their data
To do so, make the most informed decisions possible and mitigate as many risks as you can when managing your data.
Data Governance Efforts To Consider
Here are some key actions to consider when making decisions and mitigating risks:
- Data policies. Establish rules and guidelines for the collection, storage and use of data within your organization.
- Data quality management. Ensure the accuracy, consistency and reliability of data through processes such as data validation, cleansing and profiling.
- Data security. Implement measures to protect sensitive data from unauthorized access, which ensures confidentiality, integrity and availability.
- Data stewardship. Assign responsibility for the management and oversight of specific data assets to people or teams within your organization.
- Data life cycle management. Manage the data’s entire life cycle, including its creation, storage, usage and deletion, systematically and methodically.
- Compliance and regulatory alignment. Ensure your data governance practices align with legal and regulatory requirements governing data privacy and security.
- Data cataloging and metadata management. Create an inventory of data assets and maintain metadata to facilitate easier data discovery, understanding and usage.
- Data access and authorization. Define and enforce access controls to ensure members have appropriate permissions to access and modify data.
Adhering to these efforts helps you derive value from your data sets (and their assets) while keeping your clients’ trust.
CRM Data Issues
Now that we know what data governance is and how to uphold quality standards, let’s explore some common issues related to customer relationship management (CRM) data.
Lack of Data Governance
As you might expect, we’re putting data governance at the top of this list.
Not implementing policies leads to poorly defined data governance. In turn, you’ll experience a variety of data quality issues that hold back your association because you can’t effectively manage your data.
Data Inaccuracy
Data inaccuracy involves a couple of concepts. The presence of duplicate records is one of them — duplicate data leads to confusion and inaccurate reporting.
Another manifestation of data inaccuracy is outdated information. You must regularly update your CRM data, or your member profiles will be incorrect.
Incomplete Data
If your data is missing contact details or other essential information, your communication with clients will inevitably suffer. Member profiles need to be complete and current.
Inconsistent Data
One way to ensure your data is accurate is maintaining consistent formatting. This goes for names, addresses and other fields to align with your analysis and reporting standards.
Another way is following standardization practices for data entry processes. Lack of clear protocols will result in variations in how your association records information.
Data Integration Challenges
Make sure your systems are connected!
If you can’t integrate your CRM data with other systems, you’ll end up with fragmented insights and siloed info. Both can hurt any association.
Data Security and Privacy Concerns
We all know to avoid unauthorized access. Who wants just anyone scouring their data? With inadequate access controls, you’re bound to encounter unauthorized users and potential changes to sensitive client data.
Compliance issues are another concern. Failing to comply with data protection regulations leads to both legal risks and reputational damage.
Data Entry Errors
You should never overlook the possibility of human error. Unfortunately, it’s always present.
Mistakes that employees make during manual data entry processes introduce information gaps and inconsistencies. This leads to the need for entire content/data audits and extra work that no one’s eager to take on. That additional labor also costs you time and money that you could have easily saved.
Ensuring your employees have adequate training on CRM systems helps prevent entry mistakes and more within your system(s). You can explore plenty of user adoption training courses — HubSpot has some of the most comprehensive ones on the market.
Data Migration Challenges
When switching to or adopting a new system at your association, it’s not uncommon for data migration problems to arise. This may lead to data loss (hopefully not entire data sets) and even data corruption.
Overreliance on Automated Processes
Automated processes are great time-savers — they speed up tasks, saving you ample time to handle other tasks. It often feels like you’re successfully knocking out one project after another.
Still, automation errors are a real possibility if you don’t properly configure or monitor your system processes.
Addressing these CRM data issues requires regular data quality assessments to ensure your CRM system provides accurate (and valuable) client information.
Real-World Example: How Amazon Prevents Data Fraud
If you’re a living, breathing human, it’s virtually impossible that you haven’t heard of Amazon — the multinational technology-based company, that is.
What started as an online book-selling platform now has an array of focuses and revenue streams:
- E-commerce
- Cloud computing (Amazon Web Services)
- Digital streaming
- Artificial intelligence (AI)
Due to the above business areas, Amazon collects about 1 exabyte of data spanning more than 1.44 million servers. And despite its constant and consistent innovation when it comes to managing that data, problems arise.
It’s a customer-centric company, so maintaining trust is important — and that means protecting all its data to the fullest extent.
Fraud Detection
One way Amazon maintains sound data governance is through security. Fraud detection is particularly high on its security list.
Amazon is and will likely remain at high risk for retail fraud. As a preemptive measure, it collects both real-time and historical data for every order its customers place. Machine learning algorithms search and record potentially fraudulent transactions to maintain data security.
So although overreliance on automation can be a CRM problem area, using AI to detect fraud is nothing less than vital. And Amazon sees definite benefits as a result:
- Customers deal with less fraud.
- The company maintains customers’ trust in purchasing through Amazon.
- Amazon earns more money by continued customer transactions.
- It saves money due to fewer product returns.
Clearly, fraud detection AI works for Amazon.
[Related: How AI Marketing Tools Could Benefit Your Association’s Strategies]
Tools and Solutions for Proper Data Management
Many tools and solutions are available for troubleshooting data challenges.
But the tool, solution or platform you choose depends on the nature of the data issues you face. Here are some tools and solutions that can help your association manage its data:
Duplicate Management
Whether in CRM, data warehouses or somewhere else, it’s not uncommon for duplicates to arise from manual errors, imports and glitches. That’s where duplicate management comes in — here are its primary objectives:
- Identify duplicates records
- Prevent the creation of duplicate records through unique constraints and validation rules
- Offer options to remove or merge duplicate records
- Provide options for ongoing duplicate detection
- Notify users when duplicate records are detected
All of this is crucial for maintaining the reliability of data and improving data analysis and decision-making processes.
Data Validation
Highlight the importance of validating data for accuracy and completeness. This may include techniques or tools used for data validation to ensure the data is fit for its intended use.
Data validation involves checking data for accuracy, consistency and completeness based on predefined criteria. However, the specific methods to do so vary depending on the context and nature of the data being validated.
Here are some common approaches to data validation:
- Type check: Ensure that the data type (e.g., text, number, date) matches the expected type for a given field.
- Format check: Verify that the data follows a specified format, such as date formats, phone numbers, or email addresses.
- Range check: Validate that numerical data falls within an acceptable range or that dates are within a valid time frame.
- Presence check: Confirm that mandatory fields are not left empty or null.
- Pattern matching: Check data against predefined patterns or regular expressions to ensure it meets specific criteria.
- Consistency check: Verify that data is consistent across related fields or records.
- Cross-Field Validation: Check relationships between different fields to ensure their coherence and conformity.
- Referential integrity: Ensure that foreign key relationships between tables are maintained.
- Uniqueness check: Confirm that data is unique within a specified context, preventing duplicate entries.
- Business rule validation: Apply custom business rules to ensure data conforms to specific business requirements.
Validation rules are typically defined during system design. So when users enter or update data, the rules are enforced, ensuring the integrity and quality of your data.
When validation errors occur, users often receive feedback about the issues that need correction, which prevents the entry of inaccurate or inconsistent data. Melissa Data and Pitney Bowes are great tools to handle data cleanup and validation.
Now let’s explore some other tools that help you troubleshoot specific data challenges.
Data Governance Platforms
Collibra offers a comprehensive data governance solution, including data cataloging, policy management and lineage tracking (the process of tracking data overtime to provide a clear idea of where it originated, how it’s changed and where it’s going.)
Alation focuses on data cataloging and collaboration to improve data discovery and understanding.
Data Quality Management
Informatica Data Quality is a comprehensive data quality management tool that helps profile, cleanse and monitor data.
Trifacta enables data organizing and cleaning through a user-friendly interface.
Data Quality Monitoring
DataRobot Data Quality Monitoring oversees data quality in real time and alerts users of potential issues.
Ataccama ONE is a data quality management platform with monitoring and profiling capabilities.
Data Security and Privacy
Varonis focuses on data security analytics, which protects sensitive information from unauthorized access.
Symantec Data Loss Prevention (DLP) helps prevent data breaches and secures sensitive data.
Data Integration and Extraction, Transformation and Loading (ETL)
Talend is an open-source data integration tool that facilitates data ETL — the process of combining data from several sources into a central data warehouse.
Apache NiFi is an open-source data integration tool that automates data movement among systems.
Master Data Management (MDM)
IBM InfoSphere MDM provides a unified view of master data across an organization, which reduces duplication and inconsistency.
Reltio is a cloud-based MDM platform with data consolidation and mastering features. Data mastering involves creating a single, comprehensive version of an organization’s data spanning various sources. This ensures data remains accurate and consistent when referenced during any decision-making process.
Collaboration and Communication
Slack facilitates communication among teams and employees responsible for data management and quality.
Microsoft Teams provides a collaborative environment for discussing and resolving data issues.
Business Intelligence
Tableau enables visual data analysis, which helps identify patterns and trends that may indicate data issues.
Power BI is Microsoft’s business analytics service for interactive visualizations and business intelligence.
Data Profiling
Oracle Data Profiling and Quality allows users to analyze and understand data’s structure and content.
SAS Data Quality offers data profiling and cleansing capabilities.
Data Cataloging
Amazon Glue DataBrew helps users clean and transform data for analytics and machine learning.
Collibra Catalog provides a central catalog for managing and discovering data assets.
Before implementing any tool or solution, thoroughly assess your association’s specific data challenges and requirements. And if you want a successful adoption, don’t put training and documentation on the backburner. High-quality data management relies on them!
[Related: Why Your Organization Needs a Learning Management System]
Current Trends: How Large Data Breaches Affect Business
Security researcher Jeremiah Fowler discovered New York-based Real Estate Wealth Network (REWN) had an unprotected database, holding records related to millions of people’s property ownership data. Logging information was what revealed the records belonged to REWN.
As of 2023, REWN’s data breach was one of the largest in December with more than 1.5 billion records exposed.
The exposed data included sensitive information in key areas:
- Property owners
- Sellers
- Investors
- Internal user logging data
This massive breach left contact information and crucial details viewable:
- Names
- Street addresses
- Purchase prices
- Dates
- Mortgage companies and loan amounts
- Taxes owed, paid and due
This created a significant hurdle in terms of trust between customers and clients working with Real Estate Wealth Network. The data sets were later secured, but the damage was done.
This is one of many data breaches that serve as a lesson for associations to properly manage their data and pursue the highest-possible security levels.
[Related: Data Governance 101: The Basics of Creating Your Association’s Data Policies and Procedures]
Contact Dennison & Associates for Top-Quality Data Management Guidance
Managing your data and troubleshooting data challenges don’t have to be so difficult — that’s where we come in.
Dennison & Associates will help you find and stay up to date with the latest advancements to protect and maintain your association’s data integrity.
With secure customer data and seamless internal operations, your members will remain satisfied with your organization and the protection of their privacy.
Review our services to see what we can do for your association. Contact us today to discuss the possibilities!
Featured image via Pixabay
