Table of contents
- What is customer master data?
- Typical data quality issues with customer master data.
- What is the general opinion on fixing customer master data?
- Criticisms of above approach.
- Main tasks to address data quality issues.
- Typical timeline to address customer master data
- Roles and responsibilities
- What software tools are available to correct and maintain customer master data records?
- Breakdown of data domains in relation to customer master data and their key attributes.
Effective customer master data management (CMDM) is essential for organizations to gain a complete and accurate understanding of their customers, enhance customer engagement, and drive business growth. This article explores the importance of CMDM and provides an overview of the key strategies and best practices for implementing an effective CMDM program. From data quality and governance to integration and automation, this article covers the essential components of a successful CMDM program that can help organizations achieve their customer-centric goals and drive long-term success.
What is customer master data?
Customer master data refers to the essential information that a business maintains about its customers. It includes details such as the customer’s name, address, phone number, email address, and other contact information, as well as information related to their purchasing history and financial transactions with the business. This data is typically stored in a centralized database or system and is used by various departments within the organization, including sales, marketing, customer service, and accounting.
Customer master data serves as a foundational element for a variety of business processes, such as sales order processing, invoicing, and customer relationship management (CRM). It helps businesses understand their customer’s preferences and behavior, allowing them to tailor their products and services to better meet their needs. Accurate and up-to-date customer master data is essential for effective decision-making and can also help to reduce errors and streamline business operations.
Typical data quality issues with customer master data.
There are several common data quality issues that businesses may encounter when dealing with customer master data. Some of the typical issues include:
- Incomplete data: Missing or incomplete information about customers can cause problems for businesses, such as difficulty reaching them, sending incorrect invoices, or providing poor customer service.
- Inaccurate data: Incorrect information such as wrong addresses or phone numbers can lead to delivery issues, delayed or failed transactions, and poor customer experience.
- Duplicate data: Duplicate entries of the same customer can cause confusion and make it challenging to maintain accurate records, leading to inconsistent data and wasted resources.
- Outdated data: Customer data that has not been updated can result in outdated customer information, leading to lost sales opportunities or missed interactions with customers.
- Non-standardized data: Inconsistent formats and data quality across different systems can lead to difficulty in integrating, analyzing, and interpreting customer data.
- Poorly managed data: Lack of data governance policies, poor data management practices, and ineffective data cleansing procedures can lead to data quality issues and data inconsistencies.
Addressing these data quality issues requires a systematic approach that involves data cleansing, data profiling, and data standardization processes to ensure that the customer master data is accurate, complete, and up-to-date. This will ensure that businesses can rely on the data to make informed decisions and provide quality customer service.
What is the general opinion on fixing customer master data?
The general opinion is that fixing customer master data is essential for organizations to operate efficiently and effectively. Poor quality customer data can lead to a variety of negative consequences, such as lost sales, increased costs, compliance risks, and damaged reputation. By improving the quality of customer master data, organizations can enhance customer satisfaction, improve decision-making, and increase revenue.
However, fixing customer master data can be a complex and time-consuming process that requires a significant investment of resources. It requires a coordinated effort across multiple departments, and often involves the use of specialized software tools and the development of data governance policies and procedures. Despite these challenges, most organizations recognize the importance of fixing customer master data and view it as a strategic priority.
Criticisms of above approach.
|Improves the accuracy and completeness of customer data||Requires significant investment of resources, including time, money, and personnel|
|Enhances the efficiency and effectiveness of business operations||Can be a complex and time-consuming process that requires a coordinated effort across multiple departments|
|Improves customer satisfaction and loyalty||Can be disruptive to business processes, particularly during the data profiling and cleansing phase|
|Helps to identify opportunities for cross-selling and upselling||Can be difficult to maintain data quality over time, particularly if data governance policies and procedures are not established and enforced consistently|
|Reduces the risk of compliance violations and associated fines or penalties||May require the use of specialized software tools that are expensive to license and may require significant training to use effectively|
|Enables more informed decision-making and supports business strategy||Requires ongoing effort and attention to maintain data quality, particularly as customer data changes over time|
|Can increase revenue and profitability by identifying new sales opportunities and improving sales conversion rates||May encounter resistance from employees who are resistant to change or who do not see the value in investing in data quality|
Main tasks to address data quality issues.
To address customer master data quality issues, the following tasks can be performed:
- Data profiling: This involves analyzing the customer master data to identify any data quality issues such as duplicate entries, missing data, and inconsistencies. Data profiling can help to understand the quality of the data and identify areas that require improvement.
- Data cleansing: Data cleansing is the process of removing or correcting inaccurate, incomplete, or inconsistent data. This process may involve identifying and merging duplicate entries, filling in missing data, and standardizing data formats.
- Data standardization: Standardizing the format of customer master data ensures that data is entered in a consistent manner, making it easier to manage and analyze. Standardizing data formats can include setting naming conventions, defining data types, and establishing data validation rules.
- Data governance: Developing data governance policies and procedures can help ensure that data is managed effectively and efficiently. Data governance involves defining roles and responsibilities, establishing data quality standards, and creating processes to maintain data quality.
- Data enrichment: Data enrichment involves adding additional data to the customer master data to enhance its value. This can include appending data such as demographic information or purchasing history to better understand customers and improve customer service.
- Ongoing monitoring: Regularly monitoring the customer master data can help to identify new data quality issues and ensure that the data remains accurate and up-to-date.
By performing these tasks, businesses can improve the quality of their customer master data, leading to improved customer service, better decision-making, and increased operational efficiency.
Typical timeline to address customer master data
Sure, here is a table with recommended templates for each main task:
|Phase||Main Tasks||Recommended Templates||Roles Involved||Software Tool Options||Typical Timeline|
|Assessment and Planning||1. Identify and define the scope of the project||Project Charter<br>Project Plan||1. Data Steward|
2. IT staff
3. Business Analysts
4. Data Quality Team
|2. Analyze existing customer master data using data profiling software (e.g., Informatica Data Quality, Talend Data Quality, Trillium Discovery, IBM InfoSphere Information Analyzer)||Data Profiling Report Template||Informatica Data Quality, Talend Data Quality, Trillium Discovery, IBM InfoSphere Information Analyzer|
|3. Identify data quality issues and their causes||Data Quality Issue Log Template|
|4. Develop a data quality plan||Data Quality Plan Template|
|Data Profiling and Cleansing||1. Use data quality software (e.g., Informatica Data Quality, Talend Data Quality, Trillium Discovery, IBM InfoSphere Information Analyzer) to profile and identify data quality issues||Data Profiling Report Template||1. Data Steward|
2. IT staff
3. Data Quality Team
|Informatica Data Quality, Talend Data Quality, Trillium Discovery, IBM InfoSphere Information Analyzer||4-8 weeks|
|2. Use data quality software (e.g., Informatica Data Quality, Talend Data Quality, Trillium Discovery, IBM InfoSphere Information Analyzer) to cleanse and standardize data formats||Data Cleansing Plan Template||Informatica Data Quality, Talend Data Quality, Trillium Discovery, IBM InfoSphere Information Analyzer|
|3. Remove duplicate data using MDM software (e.g., SAP Master Data Governance, Informatica MDM, IBM MDM, Talend MDM)||MDM Data Cleansing Template||SAP Master Data Governance, Informatica MDM, IBM MDM, Talend MDM|
|Data Enrichment||1. Append additional data to customer master data using data enrichment software (e.g., Clearbit, ZoomInfo, InsideView, Experian)||Data Enrichment Template||1. Business Analysts|
2. Data Quality Team
|Clearbit, ZoomInfo, InsideView, Experian||4-8 weeks|
|2. Enhance customer master data with demographic information using CRM software (e.g., Salesforce, Microsoft Dynamics 365, HubSpot, Zoho CRM)||Customer Demographic Information Template||Salesforce, Microsoft Dynamics 365, HubSpot, Zoho CRM|
|3. Add purchasing history to customer master data using sales data software (e.g., SAP Sales Cloud, Oracle Sales Cloud, HubSpot Sales Hub)||Sales Data Template||SAP Sales Cloud, Oracle Sales Cloud, HubSpot Sales Hub|
|Data Governance||1. Establish data governance|
Roles and responsibilities
Improving the quality of customer master data records requires the involvement of various roles and responsibilities within an organization. Here are some of the roles and responsibilities that are required to improve the quality of customer master data:
- Data Steward: The data steward is responsible for overseeing the quality and integrity of customer master data. They ensure that data is accurate, complete, and up-to-date, and develop policies and procedures for maintaining data quality.
- IT staff: IT staff are responsible for implementing and maintaining the software tools used to manage customer master data. They ensure that data is stored securely, that systems are integrated effectively, and that data quality is monitored and maintained.
- Business analysts: Business analysts are responsible for analyzing and interpreting customer master data to identify patterns, trends, and insights. They use this information to make informed decisions and recommendations that can help improve customer service and business operations.
- Customer service representatives: Customer service representatives use customer master data to provide personalized service and support to customers. They ensure that customer information is accurate and up-to-date and that customers are satisfied with their interactions with the business.
- Marketing staff: Marketing staff use customer master data to develop targeted marketing campaigns and promotions. They analyze customer behavior and preferences to develop messaging that resonates with customers and improves engagement.
- Sales staff: Sales staff use customer master data to identify sales opportunities and to personalize their approach to each customer. They use the data to build relationships with customers and to increase sales and revenue.
- Data quality team: A data quality team is responsible for identifying and correcting data quality issues within customer master data. They use data quality software tools to profile, cleanse, and standardize customer data, ensuring that it is accurate and complete.
Improving the quality of customer master data is a collaborative effort that requires the involvement of various roles and responsibilities within an organization. By working together, these roles can ensure that customer data is accurate, complete, and up-to-date, leading to improved customer service, better decision-making, and increased operational efficiency.
What software tools are available to correct and maintain customer master data records?
Here is an outline for each category with examples of software tools and their explanations:
- Data quality software:
- Example tools: Informatica, Talend, Trillium, IBM InfoSphere Information Server
- Explanation: Data quality software is used to profile, cleanse, and standardize customer master data. These tools identify and correct data quality issues, such as incomplete or inaccurate data, through data profiling, data cleansing, and data standardization capabilities.
- Master data management (MDM) software:
- Example tools: SAP Master Data Governance, Informatica MDM, IBM MDM, Talend MDM
- Explanation: MDM software provides a centralized repository for customer master data and helps ensure that the data is accurate, complete, and up-to-date. These tools can also manage data across multiple systems and applications, ensuring consistency and integrity of customer data.
- Customer relationship management (CRM) software:
- Example tools: Salesforce, Microsoft Dynamics 365, HubSpot, Zoho CRM
- Explanation: CRM software manages customer interactions and transactions, providing a comprehensive view of the customer. These tools can help identify data quality issues, such as incomplete or inaccurate customer data, and provide functionality for managing customer relationships and improving customer service.
- Data integration software:
- Example tools: Dell Boomi, Informatica PowerCenter, Talend Data Integration, IBM InfoSphere DataStage
- Explanation: Data integration software is used to integrate data from multiple systems and applications. These tools can ensure that customer master data is consistent and up-to-date across different systems and applications, helping to improve data quality.
- Data enrichment software:
- Example tools: Clearbit, ZoomInfo, InsideView, Experian
- Explanation: Data enrichment software appends additional data to customer master data, such as demographic information or purchasing history, to enhance its value. These tools can help improve the accuracy and completeness of customer data.
- Data quality monitoring software:
- Example tools: Talend Data Quality, SAP Information Steward, Trillium Discovery, IBM InfoSphere Information Analyzer
- Explanation: Data quality monitoring software provides ongoing monitoring of customer master data, helping to identify and correct new data quality issues as they arise. These tools can help ensure that customer master data remains accurate and up to date over time.
Breakdown of data domains in relation to customer master data and their key attributes.
Here is a breakdown of data domains in relation to customer master data and their attributes separated by commas in a table format:
|Identity Data||First Name, Last Name, Middle Name, Suffix, Prefix, Address Line 1, Address Line 2, City, State/Province, Zip/Postal Code, Country, Phone Number, Email Address, Date of Birth, Social Security Number, Driver’s License Number, Passport Number|
|Demographic Data||Age, Gender, Ethnicity, Education Level, Occupation, Income, Marital Status, Family Size, Home Ownership|
|Transaction Data||Purchase Date, Purchase Amount, Order Number, Payment Type, Payment Amount, Account Balance, Shipping Address, Billing Address, Product Name, Product SKU, Product Category|
|Interaction Data||Customer Service Inquiry Details, Complaint Details, Feedback Details, Marketing Response Details, Website Browsing History, Social Media Activity, Chat and Email Correspondence|
|Behavioral Data||Search Queries, Browsing History, Clickstream Data, Social Media Activity, Preferences and Interests|
|Geographic Data||Country, Region/State/Province, City, Postal Code, Time Zone|
|Psychographic Data||Personality Traits, Values, Attitudes, Interests, Lifestyle|
|Firmographic Data||Industry, Company Size, Revenue, Location, Key Contacts, Company Structure, Partnerships and Alliances|
|Relationship Data||Family Members, Business Partners, Affiliates, Relationships with Other Customers|
|Consent Data||Opt-in or Opt-out Status for Marketing Communications, Opt-in or Opt-out Status for Data Sharing, Opt-in or Opt-out Status for Data Processing|
In conclusion, customer master data management is an essential practice for organizations that wish to better understand their customers and improve their engagement with them. By implementing the best practices outlined in this article, such as establishing a data governance framework, leveraging data integration and automation, and ensuring data quality and accuracy, organizations can create a single, reliable source of customer information that can be used to drive business growth and success. With the right CMDM program in place, organizations can gain a competitive edge in the marketplace and achieve their customer-centric goals.