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Six Data Quality Management Issues and How to Solve Them
Thursday, February 29, 2024

After you have implemented your CRM system, how can you ensure that the data coming in is clean, correct and up-to-date? Not only that but how can you make sure it stays clean? Accurate data quality is crucial for any firm that relies on precise data to make informed decisions. However, so many firms struggle to adequately manage their data, causing serious challenges for attorneys and administrators.

The amount of missed opportunities to develop business with clients or the lack of ability to successfully plan and execute events will have any marketer or business development professional banging their head against a wall. But the good news is that data never stops flowing in, so you have forever to get better at it. Additionally, there are many proven data management strategies and processes you can implement to improve it.

1. Inconsistent Data Entry

One of the biggest issues firms deal with is inconsistent data entry. Inaccurate, incomplete, or misspelled data can lead to incorrect analysis of data and ultimately mislead the decision-making process. To address this issue, firms should implement data validation processes, like mandatory fields, dropdown lists, and input masks. Additionally, training courses and awareness programs should be regularly held to educate employees about the importance of accurate data entry.

2. Lack of Data Standardization

During a new CRM project, thousands, or even hundreds of thousands, of records flow into the CRM from various sources bringing in data entered by multiple individuals with inconsistencies in formatting and naming conventions. To create uniformity across the organization, firms should establish data standard guidelines. This can act as a sort of ‘data dictionary,’ defining naming conventions and implementing data profiling while standardizing data entry and improving its quality.

3. Data Duplication

Duplicate data not only takes up storage space but seriously impacts the decision-making process. By utilizing new technology, firms can implement algorithms that regularly check databases for duplicate records. Firms can also regularly conduct deduplication exercises amongst departments or pilot groups to tackle these data issues in an isolated manner. Additionally, implementing unique identifiers and data-matching techniques can help identify and merge duplicate records effectively.

4. Lack of Data Governance

The term ‘data governance’ refers to the establishment of policies, procedures and roles to ensure data quality and integrity. Without this framework, firms run the risk of losing the integrity of their data and can find themselves being uncompliant with certain data privacy regulations. To maintain healthy data and regulatory compliance, firms should designate data stewards to enforce data access controls and regularly audit the system for regulation violations.

5. Insufficient Data Integration

Many times, we see firms struggling with integrating data from disparate systems or departments, leading to fragmented or siloed data. To overcome data silos, businesses should invest in robust data integration tools that allow for the seamless transfer of data between systems. Implementing a master data management solution can also be helpful as it creates a unified and accurate view of data across the firm.

6. Inadequate Data Quality Monitoring

The most important thing to remember about data quality is that it is not a one-time effort; it requires continuous monitoring and improvement. Lack of proper data quality monitoring can lead to undetected errors and a build-up of dirty data. Dashboards should be set up within the CRM to monitor and track data quality metrics in real-time, like email deliverability, bounces, and spam reports. Regular data profiling, validation and cleaning processes should also be implemented to identify data issues proactively.

Maintaining high-quality data is a fundamental aspect of any successful business. By addressing these data management issues head-on, organizations can enhance their decision-making processes, gain a competitive advantage, and improve operational efficiencies. Through a robust data management strategy that validates incoming records, standardizes data entry, and has integrated solutions that continuously monitor data quality, you can pave the way for accurate, reliable data-driven insights. 

Remember, data quality is not a one-time fix, but an ongoing commitment that requires attention, resources, and a culture of data stewardship throughout the organization. Embrace these solutions, and your organization will be well-equipped to conquer data quality challenges and harness the full potential of its data assets.

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