Snowflake Master Data Management: Strategies & Use Cases

• Master data is the core data that impacts all your important business transactions and operations. It includes information on customers, products, employees, suppliers, location, and finances of your business organization. Master data is considered the best source of truth but varies for different industries. 
• Managing master data is essential for continuously accessing standardized and accurate data. Through master data management, you can make better decisions, improve your customer experience, and make your business operations efficient.
• There are various data warehouses that provide built-in features for master data management solutions. Snowflake is a data warehouse service you can use to manage your master data. 

This article gives information about Snowflake master data management, which you can use to enhance your business revenue.

What is Master Data Management?

Master data management (MDM) uses various tools and techniques to organize and structure master data in a standardized format. It combines data management practices such as data ingestion, integration, modeling, or governance for data management.

These practices help you strategically reduce a dataset’s size by removing irrelevant information and streamlining all your business data.

Some of the reasons that make master data management necessary for your business are:

• Through master data management, you can improve data quality by ensuring the accuracy of identifiers and critical data elements for all attributes. This helps reduce operational errors in the enterprise dataset. 
• Data consolidation is an integral part of master data management. By consolidating data, you create a unified source from which you can easily retrieve data. This ease of access accelerates data processing and helps you achieve your business goals faster. 
• Master data management ensures compliance with data regulation measures such as GDPR (General Data Regulation Protection). 
• It helps you to implement an effective data governance framework by ensuring consistency, accessibility, and improved data quality. 

What is Master Data Management in Snowflake?

Snowflake is a cloud-based data warehouse solution with several built-in features for effective data management. You can integrate Snowflake and MDM systems to improve master data management. 

Some of the features that make Snowflake suitable for master data management are as follows:

• Snowflake offers highly scalable data storage to handle large volumes of data. 
• It can act as a centralized repository for master data, simplifying accessibility across your organization. 
• Snowflake also facilitates secure data sharing and integration with various source systems. It offers compelling security features such as access control and encryption that ensure data governance and management of your master data. 
• It integrates various analytics and BI tools to enable you to conduct efficient data analytics. So, you can leverage these benefits of Snowflake MDM solution to ace master data management for your business growth. 

Different Architecture Options for Snowflake Master Data Management Systems 

The different types of Snowflake MDM architectures are as follows:

1. Registry Architecture

In this model, you can send the data to a staging area in Snowflake instead of directly transferring it from various sources. Then, you must run cleansing and matching algorithms on this master data. These algorithms help you to spot and join duplicate values through cross-referencing. After matching, you can consolidate this data into a unified record. 

You may assign unique IDs to this data to ensure that you have only one record per customer. You can also set primary and foreign keys to establish transparent relationships between various data entities. These changes are made to the staging area data, not the original data source. When querying, you are provided a read-only view of cleaned and updated information.

For example, you are querying customer information. Snowflake will ingest the data from the relevant source systems to a staging area. You can process this customer data to get clean and consolidated records. The platform will provide you with a unified view of all the data without physically copying it into its central repository.

2. Consolidated Architecture

In consolidated architecture, you need to physically copy and transfer master data to a centralized repository in Snowflake. It enables you to clean and process this master data to create a single source of truth, known as a golden record. You can use these master data records to perform enterprise analytics and prepare reports for better decision-making. 

For instance, you have different databases for inventory, e-commerce, and supplier data. You can consolidate this data by transferring it to a Snowflake repository. Snowflake enables you to access and process the master data and use it for enterprise data analytics. 

3. Transactional Architecture

In transaction architecture, the centralized repository receives real-time updates of changes in the master data at the source. It enables you to process the data according to the changes at source instantaneously. Through this, you can receive updated information on any changes in your source datasets. 

For example, you manage inventory across multiple retail stores and online platforms for a company. You can implement transactional architecture to instantly receive changes in inventory after selling any product. When a product is sold in a store or online, your inventory master data gets updated immediately in Snowflake. It helps you take actions such as restocking to keep your inventory up-to-date. 

How to Implement Master Data Management in Snowflake 

You can choose any of the earlier-mentioned architectures to streamline master data management with Snowflake. It involves various steps, which are described as follows:

1. Identify your Master Data

You should first identify the data entities that affect the primary functions of your business. Once this is done, search the sources containing this master data. Also, different organizations desire varied outcomes from master data management. To understand this, you should identify whether you want to improve the consistency, accessibility, or quality of your data. Based on this, you can clearly define your objectives for master data management. 

2. Choose a Master Data Management Architecture

You need to choose a master data management architecture beforehand that meets all your objectives. You can select between the registry, consolidation, or transactional architectures. When choosing a master data management architecture, you should consider the volume and complexity of your data along with your latency requirements. 

3. Set up the Snowflake Account

Create a Snowflake account. You can opt for an initial free trial to explore the platform. Then, assign roles and permissions for different domain teams of your company in Snowflake. You can also define the database and schema structure to accommodate master data. 

4. Data Modeling

You should define the relationships and hierarchies within different master data types for effective Snowflake master data management. This will help you design a better database schema. For instance, a customer can place many orders for different products. You can define a relationship between products and orders through foreign keys. You can also define primary keys to identify each data record in the table uniquely. 

5. Data Ingestion and Transformation

In this step, you must ingest data from various sources into the Snowflake repository or staging areas. To achieve this, you can opt for a third-party data integration tool like Hevo Data. It is an efficient data integration tool that can aid you in ingesting data into Snowflake and transforming it for master data management. 

6. Data Quality Management

After integration, improving data quality should be your top priority. For this, you can leverage Snowflake’s various built-in data quality features. It includes Snowsight, access history, object tagging, auditing, and COPY HISTORY or QUERY HISTORY commands. You can also use Hevo’s data quality-improvement capabilities, such as a robust error-handling mechanism, to enhance the integrity of your data. 

7. Data Governance Framework

You should also develop a streamlined data governance framework for Snowflake master data management. Snowflake features such as data classification, object tag-based masking, row-level access, and column-level security can help with data governance. 

After completing all the steps, you can visualize this master data using BI tools like Power BI or Tableau for effective data analytics.

Challenges of Snowflake Master Data Management

Some of the challenges associated with the Snowflake master data management solution are:

• Choosing the Right MDM Architecture: Choosing the right MDM architecture can be daunting. You may often find that no architecture perfectly aligns with your objectives and resources. 
• Complexity: Master data management in Snowflake includes several complex processes, such as data integration, cleaning, or governance. For effective master data management, you should have in-depth knowledge of these processes. 
• Scalability: Deploying master data management practices for large data volumes is challenging. Also, Snowflake’s usage charges are high for large data volumes, which may result in high expenditures.  
• Data Governance: If your organization is large, managing data governance with master data management can be challenging. Data governance requires collaboration between different teams, which makes it difficult to set clear ownership roles to ensure data accessibility. 
• Organizational Constraints: It can be challenging to convince all senior authorities, stakeholders, and your teams about the importance of master data management. Limitations of resources such as budget or time also create hurdles in effective master data management. 

What are the Comprehensive MDM Strategies for Using Snowflake? 

You can follow the below-mentioned best practices to develop comprehensive MDM strategies using Snowflake: 

• Understand Your Data: It is essential to understand your enterprise data well to identify potential master data types. You should clearly categorize and define the relationship between customers, products, suppliers, locations, and any other master data. 
• Streamline Data Ingestion: Before ingestion, you should ensure that the source data is in a standardized format according to the data types supported by Snowflake. You can opt for real-time data ingestion to reduce the latency and usage costs of Snowflake’s compute resources. 
• Optimize Data Storage: Snowflake’s clustering keys can help you organize data based on usage patterns. They also optimize query performance and storage. You can opt for manual compression solutions or use Snowflake’s automatic compression feature to reduce the usage of storage resources.
• Prioritize Data Security: You should implement Snowflake’s role-based access controls and encryption features to prevent data breaches. Its auditing features also allow you to comply with various data regulatory mechanisms. 

Practical Use Cases of Snowflake Master Data Management

Here are some of the practical applications of Snowflake Master Data Management:

• Improving Customer Experience: Snowflake Master data management gives a unified view of customer data records, such as personal information, preferences, or purchase history. It acts as a single source of truth to provide insights into customer behavior. This understanding helps you optimize your product and marketing strategy according to customers’ needs and market trends. 
• Supply Chain Management: Snowflake Master data management can provide accurate and consistent information about product data. You can use it to track the real-time status of inventories to avoid overstocking or understocking. 
• Maintaining Finance Records: By standardizing financial data, master data management enables you to create error-free financial reports. It allows you to maintain finance data records efficiently. 

Conclusion

This blog gives you a complete overview of Snowflake master data management. It provides a comprehensive Snowflake MDM integration guide and best practices. To ensure effective master data management, you should use automated tools like Hevo Data for data ingestion. It provides a vast library of connectors that you can use to load data in Snowflake. The platform lets you set ingestion and loading frequency to facilitate real-time data analytics. You can schedule a demo today to take advantage of these features!

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