Quality Over Quantity: 5 Steps to Cleaner, More Effective Data

Quality Over Quantity: 5 Steps to Cleaner, More Effective Data

Elevate your data quality to turn analytical clutter into clarity.

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Managing vast amounts of data is daunting. With millions of records to handle, maintaining high data quality, compliance with regulations, and streamlining processes can seem overwhelming. Yet these tasks are essential for any organization striving to stay competitive and efficient. In fact, data quality is a top priority for analytics and IT leaders.

Every company aspires for the best data quality, which includes the retention and use of relevant, clean data while also following key regulatory requirements. But ensuring data quality in an easy and automated fashion is extremely difficult. With new regulations being introduced at an increasing rate alongside the scale of customer data, companies need to automate processes - including the removal of stale data.

What is data quality and why is it important?

We know data quality is critical for effective data management and decision-making, but what exactly are we talking about when we use the term "data quality"?

According to the Data Management Association (DAMA), data quality is defined as "the degree to which data is accurate, complete, reliable, and relevant to the purpose for which it is used."

This definition highlights the essential dimensions of data quality that organizations must have to derive meaningful insights and make informed choices. And it's no secret that good data quality is the foundation of any successful business. In simpler terms: bad data in, bad results out.

Speaking of bad data, it's more than just a nuisance - it comes with a hefty price tag. Poor data quality is estimated to cost organizations an average of $12.9 million, underscoring the significant financial burden poor data quality poses on organizations. Salesforce research also indicates that 80% of business leaders consider data essential in decision-making, positioning it as a critical driver in strategic choices.

Additionally, customer trust is closely tied to how data is utilized; 71% of customers are more likely to trust companies that clearly communicate their data usage practices. Together, these statistics highlight the essential role of high-quality data in driving business success and elevating customer relationships.

Help your customer success team shine with a stellar data quality approach

Data quality plays a pivotal role in allowing customer success teams to perform at their best. Having the best data empowers the best experiences for these teams, including:

• Time savings: Your teams are no longer searching for what they need, because it's already at their fingertips
• Increased efficiency: Achieve better resource utilization and operational efficiency when teams aren't having to sift through unnecessary data
• Improved compliance: Make sure data practices meet regulatory standards
• Enhanced performance: Optimize data management to improve overall organizational performance

5 steps to elevate your data quality

Step 1. Establish a data governance framework

Like any successful project, enhancing data quality starts with a well-structured plan. Establishing a data governance framework sets your teams and organization up for long-term success. This framework helps maintain accountability, while also ensuring that everyone understands data handling protocols.

Start by defining your objectives - improving data quality is likely at the top of the list.

Next, form a cross-functional governance team that includes IT, legal, compliance, and any other relevant departments. Together, you can figure out the current state of your data quality and identify areas for improvement.

Once you have a good understanding of what potential problems you're facing, decide on the best policies for data management. Finally, clarify roles and responsibilities for maintaining data quality, outlining what each team member needs to do to uphold these standards. Consider this a cyclical process where you continue to iterate and fine-tune your framework to better serve your business.

Step 2. Standardize data entry processes

We've said it before and we'll say it again: bad data in, bad results out. Now is the time to shape a standardized process for data entry that your team can leverage.

Effective data entry processes typically use consistent formats and validation rules for data entry. One of the best ways to reduce human error and improve uniformity across datasets is by implementing dropdown menus and templates to take the guesswork out of data input.

Step 3. Conduct regular data profiling and cleansing

When your focus is good data quality, it's essential to regularly assess data for inaccuracies and inconsistencies. Using data profiling tools can automate the process of identifying issues and automating cleansing processes to correct errors and remove duplicates.

Tools like Privacy Center effectively manage the surplus of data created by multiple sources, particularly when it comes to duplicate records. For instance, Privacy Center has empowered one of Salesforce's largest internal customer support teams to do the following:

• Easily configure policies that automate the identification and daily deletion of unwanted records
• Quickly troubleshoot errors through detailed logging
• Create multiple, tailored policies for different team requirements
• Navigate the complexities of organizational configurations
• Reduce outdated data as they prepare for AI processes

Step 4. Train staff on data management practices

Better, better, never done is our motto around here - and we're always looking for ways to improve our processes and data quality.

By providing ongoing training to employees about the importance of data quality and data handling techniques, they're more likely to recognize and address data quality issues proactively. Using Trailhead to dive deeper into data management best practices is a great place for teams to skill up together.

Step 5. Implement continuous monitoring

Establish metrics and processes to continuously monitor data quality. Regular audits and feedback loops allow for the quick identification of issues, facilitating ongoing improvement. A proactive approach to monitoring your data allows you to address potential problems early, making sure they don't impact decision-making.

Creating a culture of continuous improvement is also vital. Once you train your employees on best practices for data management, they can help iteratively refine your processes and enhance overall data quality. Adopting a comprehensive approach for data quality helps maintain high standards, promotes accountability, and encourages a commitment to data excellence.

Clean data quality for a polished future

Ensuring high data quality is not just a technical necessity; it serves as a fundamental driver of business success. As the volume and complexity of data continues to grow, prioritizing data quality will empower businesses to make informed decisions, enhance operational efficiency, and build stronger relationships with customers - all while adhering to key regulatory requirements.

By implementing robust data governance practices, standardizing data entry processes, regularly cleansing data, and creating a culture of accountability and continuous monitoring, you can unlock the full potential of your data.

Ultimately, to create a truly data-driven organization, it's vital to maintain clean, reliable data - so shift from data chaos to clarity to help your business sparkle.

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