Data Validation: Challenges, Significance, and Implementation for Maximizing Sales Success in 2021 and Beyond

Sales | 3 minutes to read

15th Sep, 2021

Data validation is crucial for B2B organizations to maintain accurately, refreshed, and relevant customer databases. From verifying contactability matrix to optimizing sales and marketing campaigns, data validation has far-reaching benefits in the B2B ecosystem.

Snapshot:

    • Data validation is a form of data cleansing that involves verifying the data in terms of its contactability and relevance

    • Data validation helps enterprises weed out the inaccuracies and duplicate data from their customer database

    • B2B enterprises face challenges of data standardization, absence of automated tools, and minimal expertise in database management

    • Enterprises can develop a robust data validation program leveraging AI and machine learning algorithms

    As more B2B organizations adopt data-oriented practices to increase revenue and improve operational efficiencies, the quality of data becomes even more critical. Lack of confidence in business data obtained from multiple sources can derail an organization’s efforts to meet key business objectives. Organizations are often burdened with the sheer volume of data. They face challenges with respect to data standardization, disparate data systems, lack of data governance, manual processes, etc.

    Businesses collect data about their customers- demographic, technographic, firmographic, financials, through internal functions as well as external interactions. However, the collected data are often raw and replete with inaccuracies that do not offer actionable insights. As such data cannot be relied upon, there arises a need for data validation. Therefore, enterprises must implement permanent, organization-wide data quality programs to standardize and validate the information in their databases. Data validation makes it easier for enterprises to trust their data.

    What is data validation?

    Data validation is the process of examining the accuracy and quality of data performed before importing and processing it within the CRM. It involves processes and techniques that help enterprises uphold their data integrity. Data validation is a form of data cleansing that entails verifying the data in terms of its contactability and relevance. The process ensures that the data is complete, unique, and consistent with the expected range of values without any duplications. Data validation is a part of the ongoing maintenance program to make sure that the corporate database is enriched with relevant information.

    Challenges of database validation 

    An accurate and compliant database helps avoid pitfalls, personalize customer journeys, and streamline sales and marketing campaigns. However, maintaining database integrity for the sales and marketing team can be challenging for multiple reasons. Delving deep into the challenges of data validation and quantifying its impacts can help organizations improve their bottom line. Let’s look at some of the recurring challenges that B2B enterprises face with their data validation processes:

    Lack of data standardization and deduplication: One of the key issues with B2B organizations is that they lack data standardization and uniform data storage guidelines. Data comes from a variety of sources and gets integrated into the CRM. This is further exacerbated since data lives in silos. Often enough, the companies don’t have a robust deduplication engine, leading to duplicate data being stored in the system.

    Absence of automated tools: Another critical challenge with B2B data validation is the non-implementation of AI-based data validation engines. B2B database requires automated tools to validate database with Natural Language Processing capabilities to keep the data refreshed and relevant. Manual validation of B2B data leads to errors and repetitions within the database.

    Lack of expertise in database management system: One of the fundamental challenges of b2b database validation is that companies don’t have expertise in data maintenance and management processes. They leave their marketing and sales team to shoulder the responsibility of data validation manually. This leads to storage of irrelevant and stale in the system. Often, teams within the organization don’t understand the business use case of data validation and struggle to associate a business cost with their data quality programs. It becomes difficult to prove a return on investment to business leaders.

    Watch our webinar on “The Curse of Data Decay on Sales Prospecting” to learn from the experts about the adverse impacts of inaccurate data on the bottom line and missed opportunities.

    Significance of database validation for B2B enterprises 

    B2B businesses rely on high-quality data to make critical business decisions. The information obtained from the different datasets offers insights about the changing market trends, consumer behaviour and purchasing habits, and predicting future trends. Data validation is crucial for data enrichment and segmentation with technographic, demographic, firmographic, and financial information. It helps organizations in strategic planning and navigating the challenges at different stages of the pipeline. Data validation has multi-fold benefits when it comes to B2B sales and marketing. With a regular stream of new data getting in, there are 4 key benefits of systematic data validation:

    Preventing wasted time and efforts: Validating the database early on weeds out inaccuracies and duplicate data. This prevents the sales and marketing team from using incorrect B2B datasets for their campaigns. Suppose a sales team is calling on disconnected numbers or shooting mails at dead email addresses. In that case, it will result in wastage of time and efforts hurting the company’s overall revenue. Effective data validation along with data enrichment helps overcome these challenges.

    Accurate results: Validating the data for anomalies and inaccuracies helps maintain an updated and refreshed set of B2B databases. It helps enterprises establish their messaging and product placement with potential customers. More than 50% of B2B buyers choose the vendor that contacts them first. Hence, having valid contact and financial information will give more accurate results in maximizing the bottom line.

    Optimize marketing budget: B2B marketing has a higher upfront cost which comes with a higher engagement potential. Data validation allows enterprises to invest their resources in relevant prospects. It becomes easier for enterprises to build a targeted campaign when they are confident of whom they are contacting.

    Maximize customer satisfaction: Data validation is an effective way to prevent data decay. Validating a b2b list of database for contactability and other customer information increases a company’s ability to effectively communicate and support its customer needs. Furthermore, it also increases the potential to cross-sell and upsell with the existing customers.

    Read our blog “How to Build a Marketing Database and Configure it for Success in 2021 & Beyond?” to get more insights on building a future-forward marketing database.

    Implementing data validation and data hygiene practices 

    Database validation is an ongoing process that requires subject matter expertise. Deriving correct and relevant information from a variety of data sources is difficult and complex. It is often seen that enterprises rely on third-party B2B database service providers to gather actionable data. However, enterprises must integrate data validation and hygiene programs with their marketing outreach programs. It is crucial that enterprises develop a robust data validation program that leverages AI and machine learning algorithms to verify and clean their contactable database as part of the program.

    Additionally, enterprises must collaborate with their database service providers to continuously renew and enrich their existing database. They must also follow stringent data governance guidelines to ensure the privacy and protection of customer data. With the help of a reliable b2b database provider, companies can implement cross-reference validation. As part of the process, they must correlate new data with existing datasets, identify and eliminate the inconsistencies between data, and perform range checking to fill the gaps.

    Conclusion

    Data validation goes beyond the repetitive standardization process. It is a big leap forward. It opens up new avenues to develop a great customer and value proposition. Data validation helps enterprises gain trust and confidence in their data. Sales and marketing teams get reliable and actionable intelligence to make meaningful decisions to improve the pipeline and maximize ROI. There are some challenges associated with the data validation process, but nothing cannot be overcome using cutting-edge AI and machine learning tools.

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