Organizations today collect customer and prospect data from multiple touch points, and this data forms the basis for customer relationship management and strategic marketing and sales decisions. When it comes to Customer Relationship Management (CRM) applications, the fallacy that data quantity trumps quality still exists in many organizations. However, poor data quality flowing within the CRM or Enterprise Resource Management (ERP) has the capability to not only hamper customer relationships but also derail internal processes dependent on accurate information.
Background and Problem
Our client, a Global IT services company was looking at scaling their business through their marketing channels and leveraging their customer data to drive engagement and provide meaningful insights for robust decision-making. From a business perspective, their CRM was an invaluable platform for marketing and sales and their KPIs for lead nurturing and conversion relied heavily on it.
The client had recently migrated from a legacy CRM to the Dynamics 365 CE (Customer Engagement) platform and realized that their data did not meet their quality requirements as it included a significant percentage of duplicate and incomplete records. To use their data, their marketing team first needed to understand the extent of issues in their data, identify, and cleanse them.
Manual cleansing and deduplication of the data was an enormous task, which would require a lot of time and resources. Moreover, their CRM had multiple users – including non-technical marketing and sales personnel – responsible for the accounts allocated to them, and in the absence of a holistic overview of data quality, there were limitations to what could be achieved with manual cleansing.
They were looking for a product that could integrate with their D365 CE platform and cleanse the data within it, eliminating the need for data to be exported back and forth between the CRM and DQ solution. Also, they wanted a solution that could easily be used by their data stewards and marketing teams.
Adastra built and implemented a D365 DQ (Data Quality) plug-in that integrated with the client’s Dynamics 365 CE. The solution essentially extended the natural capability of their D365 platform, offering additional features and dashboards within their CRM environment and eliminated the need for separate DQ applications.
The solution was built using Microsoft Native Technology, and leveraged Power Apps, Power Automate, and Common Data Services to classify, track, and remediate the client’s data. It is easy to use and allows both data stewards and business users to immediately identify and correct issues as they occurred.
The entire process of planning and deploying the solution, including discussions with the management, current state assessment, selection of appropriate rules, solution implementation, and training of data stewards, can be completed in 7-10 days.
Adastra’s D365 DQ solution helped the client’s users:
Classify and Track Data Quality
The solution scans and validates all the records that exist in the CRM against built-in data quality rules and instantly classifies them as “Valid”, “Warning” or “Error” based on quality and the severity of issues. Every time a business user adds, accesses or modifies data in the CRM, they see the level of quality of the record and receive automated alerts for immediate data quality intervention (such as, missing email, duplicate record, etc.) and can make updates accordingly.
A DQ dashboard provides an aggregated data quality overview for key master entities, showing the overall percentage of errors and warnings in their data. The dashboard also allows users to drill down into specific issues and records to review and resolve the problems. When Adastra implemented the D365 DQ solution for the client, for the first time, they were able to see a clear, complete picture of the extent and type of data issues residing in their system. Their records included 9% duplication, with multiple accounts or contacts representing the same company, and 28% of their data was low quality with incomplete, inaccurate, misspelled, or invalid details.
Review and Remediate Issues
Once the issues have been identified, the solution allows business users and data stewards to review and fix the problems. Users can evaluate records with a “Warning” or “Error” classification and jump directly to the entry to correct issues. Alternatively, the data stewards also have the flexibility to override DQ rules and mark a record “Valid” if they deem the issue to be irrelevant or minor.
Customize Rules Based on their Specific Needs
Adastra’s solution is flexible and allows for customization at user-level. We built this feature keeping in mind that data requirements and the ensuing quality criteria vary from organization to organization. The solution comes with a number of built-in quality and duplication rules, and the end user can turn rules on or off, depending on their requirement.
The client can also modify rule severity at their end, allowing them to decide whether a particular data quality issue should be classified as an “Error” or “Warning”. For instance, a missing phone number might be a “Warning”, while a missing email address would be an “Error” for organizations that communicate with their customers primarily over email. The solution also allows users to edit rule descriptions that show on the screen.
While the above customizations can all be done by the data stewards without any development effort, the solution also offers the flexibility of creating new rules and modifying the functionality of existing ones with some technical intervention. In the client’s case, they were able to create new rules in under a week to accommodate some special requirements for their data, such as not classifying accounts with similar names as duplicates if they belong to the same hierarchy (Eg: Parent-Child relationships).
Excellent Quality Data: Implementation of Adastra’s D365 DQ solution greatly improved the usability of the client’s data. 100% of their D365 CE users leveraged the tool, allowing them to cleanse their existing records and keep them clean. As a result, they were able to eliminate all the “Errors” and 90% of the “Warnings”, achieving their goal of trusted and actionable data in their CRM.
Improved Decision-Making: With their data quality being managed by the solution, the client’s executives and business users can now trust their data and use it to extract insights for decision-making. The DQ dashboard provides the management with an aggregated view of the overall health of their CRM data and the steady improvement in data quality has made the management more confident about relying on their data to make business decisions.
Better Returns on Marketing Investments: The organization now executes their marketing campaigns and lead qualification based on reliable data, which has resulted in better quality leads and an increase in sales. They have also seen improvement in their email and digital marketing metrics as a direct outcome of increased data accuracy. By eliminating errors and warnings in their data, they can now target their campaigns more effectively, which has lowered their costs and improved marketing ROI. Moreover, the subscription cost of most marketing automation tools that integrate with the CRM (such as for email marketing) depends on the number of contacts in the database, and by removing incorrect and invalid records, the client stands to save on the cost of such tools. The Client’s EVP Marketing and Sales stated, “We were aware that are our data had quality issues and was not in great shape for our marketing and sales efforts but the extent of these issues was unknown to us. Adastra’s D365 DQ solution served as an eye-opener and for the first time, we could see where the challenges lay and could resolve them easily. Since its implementation, our customer data has become more robust and we have seen significantly better returns on our marketing campaigns. We can now confidently say that our strategic decisions are based on high quality data.”
Improved Sales Performance: Before the implementation of the D365 DQ solution, the client’s data was rife with quality issues and duplications. In several cases, the ownership of leads/accounts was unclear and often, one account was inadvertently assigned to multiple owners. This resulted in confusion and overlap in the follow up process, which was a huge challenge from a lead nurturing and sales perspective. This was annoying for the customers and created mistrust in their minds, lowering the chance of successful conversion. However, the solution resolved these issues and created clear demarcations in account ownership, so that the assigned sales personnel would have a single line of communication with customers and a better chance of closing sales.
Enhanced Customer Service: The implementation of the solution made it easier for the client to contact customers for issue resolution and follow ups. It also gave them access to better quality information about their customers so they could serve them better. This improved overall customer experience, allowing the client to share timely, relevant information with customers and work towards strengthening customer relationships.
Resource and time savings: By automating the data validation and cleansing process, the client eliminated the need for manual cleansing and custom querying of data to identify and resolve quality issues. Since in our client’s case, most of the CRM users were in the marketing and sales teams, they would likely have needed to bring in technical resources to undertake this time-consuming and cumbersome task. Even then, the process would have been prone to errors and many issues would have been missed. Our solution gives end-users the tools needed to quickly remediate issues at their end, without the need for development intervention.
Adastra’s D365 DQ solution ensured that the client’s data is governed, managed, mastered, understood, and trusted, so the client can use it to drive business results. Enhancing the overall data quality helped the client improve their marketing and sales performance and offer better customer service. Moreover, the solution provided them with the flexibility and tools to manage data quality rules that best suited their business and allowed for changes at the end-user level. According to Krasen Paskalev, EVP Delivery and Expertise at Adastra, “Enterprises realize that good and reliable data is at the heart of providing a good customer experience. However, improving the quality and consistency of customer data is a major challenge for a lot of our customers. Adastra’s D365 DQ application enforces best practices and empowers users to ensure D365 data is trusted and actionable.”
Achieving and maintain data quality is an ongoing process. As organizations keep adding data to their CRM and ERP systems, it is imperative that they have a clear picture of the quality of their data assets and can remediate issues as they arise. Adastra’s D365 DQ solution integrates seamlessly with the Microsoft Dynamics 365 platform to provide a foundation on which organizations can continue building processes and rules to keep improving the quality of their data and consequently, increase the reliability of decisions that are based on that data.
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