Data Maintenance

What is data maintenance?

Data maintenance is a systematic approach to preserving information quality, accuracy, and relevance within customer relationship management (CRM) databases. This process includes updating customer profiles with current information, removing duplicate entries to avoid confusion and errors, and correcting inaccuracies or inconsistencies in data fields. 

Benefits of customer data maintenance in the modern digital landscape

Businesses that implement practical and effective strategies experience benefits like:

• Increased productivity: Data quality maintenance reduces time spent on manual data correction, ensures quick access to accurate information for decision-making, and streamlines productivity, allowing employees to focus on value-added tasks rather than data cleanup.
• Cost efficiency: Businesses increase cost efficiency by reducing expenses associated with data errors, minimizing the need for manual intervention, optimizing resource allocation, avoiding potential financial losses due to inaccuracies or inefficiencies, and saving on CRM storage costs.
• Improved marketing and sales efforts: Data maintenance ensures accurate customer information for targeted campaigns, enables personalized messaging based on reliable insights, enhances lead generation and conversion rates, and fosters stronger relationships through tailored communication strategies.
• Accurate sales and marketing forecasting: Effective data quality maintenance ensures sales and marketing teams have accurate and reliable information for more precise forecasting and strategic decision-making. 
• Reliable data security and regulatory compliance: Reliable data security not only safeguards sensitive customer information, reduces the risk of breaches, and ensures compliance with data protection regulations but also helps businesses avoid penalties, lawsuits, and reputational damage associated with noncompliance.
• Reliable data security: Reliable data security helps safeguard sensitive customer information, reduces the risk of data breaches or unauthorized access, and helps businesses comply with relevant data protection regulations.
• Effective analytics: Effective analytics rely on clean, accurate data to provide meaningful insights, identify trends, and make informed decisions. They enable businesses to optimize strategies, improve performance, and stay competitive.

Developing a practical and effective data maintenance strategy

Businesses develop data strategies by assessing their unique data needs, defining clear objectives, establishing data quality standards, and implementing the right tools for data cleansing. The six pillars of a practical and effective data management plan are:

Data quality

Data quality can make or break your business, which is why it’s at the top of the priority list for successful data maintenance. In general, your data can be considered high quality if it is:

• Accurate
• Complete
• Reliable
• Relevant
• Timely
• Valid

Depending on how your data ranks for each of these factors will determine whether your data serves your organization’s needs. If the data does not meet professional standards in one or more of these categories, your data maintenance efforts need to focus on improving the quality of your data.

Data cleaning

Data cleaning, also known as data cleansing, is a process that searches for errors. It finds incorrect, duplicative, missing, or misformatted data in your system and attempts to fix it.

It can be easy to confuse data maintenance and data cleaning, but the two refer to different ideas. While data maintenance is a consistently run strategy that aims to improve your data, data cleaning is a process that identifies, corrects, or removes errors.

Data cleaning should be performed regularly as part of your data maintenance strategy. Both are equally important and intrinsically tied to regular data maintenance, upkeep, and improvement.

Data deduplication

Working with any amount of data can increase the potential for redundancy, which can harm your data quality and fill up your storage space. Similarly, if your customer data is redundant, it will negatively impact your sales and marketing efforts.

Data deduplication is a process that removes extra or excessive copies of data in your data sets. After removal, only the singular piece of data remains in the master data.

This effective technique improves efficiency by reducing the chances of your team using redundant data, which can hurt any business process. It can increase data insight accuracy and enhance customer engagement and experience. It also allows for more data storage space and helps to improve your brand’s overall operations.

Data operations

Data operations—also known as DataOps—refers to the day-to-day processes of your business that ensure your database is as high-quality as possible.

Data operations work throughout the lifecycle to improve data quality and ensure teams can access and use data securely.

Data purging

Data purging works the way it sounds—it deletes your obsolete or unnecessary data. This process is a critical part of your data maintenance plan, as it improves the overall usability of your data.

The data purge process removes the clutter of data that is taking up space in your system, increasing space and access to quality data. Factors that data purging considers are the age of the data, the type of data that your system recognizes, regulatory compliance, and more.

Data monitoring and defining KPIs

Data monitoring identifies what issues need to be addressed in your CRM database. It’s a process that proactively reviews and evaluates your data to maintain standardized, high-quality data. The key to data monitoring is setting clear key performance indicators (KPIs) and analyzing the performance of your data. 

Are all fields populated with a value? Are all the fields correct? Do they follow a set format that your business can effectively use?

With defined KPIs, your data monitoring can ensure you have clean data that will help grow and improve profitability for your business.

How data quality maintenance impacts sales and marketing

Data maintenance impacts every level of your business, but it can make or break your sales and marketing efforts. Marketing relies on high-quality data to create accurate and effective strategies, from segmenting contacts to building targeted campaigns. If your data is substandard, it can spell out failure for your marketing strategies even before implementation. 

Meanwhile, your sales teams use data to properly connect with customers and present your company’s products to the market. If they’re working with poor data, your company won’t be able to stay ahead of the competition.

Below are three ways that data maintenance can impact sales and marketing:

• CRM segmentation: Marketing teams rely on data for customer segmentation, a crucial step in tailoring messages to different customer categories. Inconsistent data maintenance can lead to poor data quality, hindering CRM segmentation accuracy and campaign effectiveness. Effective data maintenance is essential for ensuring the right messages reach the intended targets, maximizing the success of marketing campaigns.
• Marketing personalization: Using data for personalized customer engagement is crucial for competitiveness and relationship-building, leading to shorter sales cycles and enhanced customer loyalty. Consistent, accurate data enables businesses to understand customer preferences, tailor messaging effectively, and foster quality engagement. Without proper data maintenance, reliance on assumptions rather than reliable information can jeopardize business performance in the long term.
• Customer experience: Regular data maintenance enables organizations to create impactful campaigns and messaging that enhance the customer experience. Inconsistent data maintenance can lead to unnecessary details or redundancies in customer data, potentially damaging your brand’s reputation and frustrating customers. A reliable data maintenance plan is essential to prevent customer experience issues and ensure high-quality data for effective engagement strategies.

Challenges associated with data maintenance

Navigating the complexities of maintaining a consistent and productive data maintenance process presents businesses with several challenges that can impact operational efficiency and decision-making. Addressing these challenges is crucial for ensuring data integrity and maximizing the value of organizational data assets.

Examples include:

• Data quality: Ensuring data accuracy, completeness, and consistency poses a significant concern for businesses, impacting the reliability and usefulness of data for decision-making, customer engagement, and operational efficiency.
• Resource constraints: Without the time and resources allocated to data maintenance, businesses may struggle to ensure data accuracy, completeness, and security, hindering their ability to derive valuable insights and make informed decisions.
• Technical complexity: Managing data across multiple platforms, ensuring data integration, and keeping pace with technological advancements require specialized expertise and resources. 

Essential tools for seamless data maintenance

Investing in efficient data tools that can guarantee regular data maintenance is the best solution to these challenges. Validity DemandTools^®   streamlines data cleaning and management processes, allowing organizations to maintain high-quality data with minimal effort. 

By automating tasks such as deduplication, normalization, and data enrichment, DemandTools saves time and resources while ensuring data accuracy and consistency. This efficiency empowers employees across various departments to focus on core responsibilities, make informed decisions based on reliable data, and drive profitability through enhanced productivity and streamlined operations.

Data maintenance best practices for success

Businesses can adopt several best practices to effectively address challenges in data maintenance. Conducting regular data audits and implementing data quality checks can help identify and rectify inconsistencies, inaccuracies, and duplicates in the database. Investing in employee training programs to enhance data management knowledge and awareness of best practices can improve overall data quality and consistency. 

Leveraging automation tools like DemandTools from Validity can streamline data cleaning processes, automate repetitive tasks such as deduplication and normalization, and increase efficiency in data maintenance efforts.

Learn more about data maintenance processes with Validity.

Take the next step in optimizing your data quality and operational efficiency by leveraging Validity’s expertise in data management. Learn how Validity can elevate your data maintenance strategy by providing insights into best practices and implementing solutions like DemandTools. Schedule a demo today to get started.