Data warehouse vs MDM: the choice confronting data leaders

What Should Data Leaders Consider – Data Warehouse or Master Data Management?

Today’s organizations look to adopt the new wave of emerging technologies that include artificial intelligence, automation, the internet of things, and much more. But their success in adopting these technologies depends much on getting the data management right. Poor quality data continues to be the primary concern for data leaders across the globe. You, of course, don’t want the “poor quality data” from entering the IT ecosystem. To combat data inconsistency, greater chances of errors, data replication, higher costs related to bad quality data, and lack of data governance, businesses can either rely on an enterprise data warehouse or master data management. Or is there any other method or approach to follow for effective data management and business data analytics?

Before you decide on the right data management approach for your business, let us do a deep dive into key concepts.

What is a Data Warehouse (DW)?

A data warehouse (DW) can be defined as a storage architecture designed to hold data extracted from transaction systems, external sources, and operational data stores. The warehouse then integrates that data in an aggregated, summary form appropriate for enterprise-wide data analysis and reporting to meet predefined business requirements.

How Does a Data Warehouse Help?

A DW lends you the ultimate flexibility in the way you store and later query your data. The analyses and reports you run in data warehouses may comprise elements from all the integrated data sources. With all your data accessible to you in a single place, it becomes easy to run queries directly in your warehouse or via a business intelligence (BI) tool. You cannot only analyze data gathered through your mobile app and website, but that captured via other platforms.

A few of the primary benefits of data warehousing include:

  • High data quality and consistency,
  • Timely access to data,
  • Enhanced business intelligence,
  • Non-volatility of data,
  • Increased productivity of corporate decision-makers, and
  • Removal of the information loading and processing from transactional databases.

What Should Your Top Considerations for a Data Warehouse be?

It would be best if you reflected on a DW for your data needs when:

  • You have a central location for all your business-critical data.
  • You must offer multiple people access to the same data set at the same time.
  • It will help if you analyze your mobile, web, CRM, and other applications – all together in the same place.
  • You must query raw data with SQL and thus go deeper than traditional analytics tools.

Challenges With Enterprise Data Warehouse

Fails to Alter Source Data

A DW supports downstream BI reporting as well as business data analytics. It, however, fails to support upstream operational processes and applications, like enterprise resource planning (ERP) systems, thus creating an issue when you expect DW to create a master list that supports ERP systems. ERP systems require a master data management (MDM) repository to achieve an enterprise role. ERP vendors can thus begin to build MDM solutions of their own, specific solutions to their products, and other software firms can develop their standalone solutions.

There is a Scarcity of Tools

Another challenge to the successful implementation of an MDM solution employing a DW has been the assumption that technology will resolve the master data management problems without significantly involving people, processes, and the State. MDM needs data governance processes to be a part of an enterprise information management initiative to update and maintain data definitions actively. In simple words, data governance is required to design, deploy, and sustain an MDM solution successfully.

Just Consolidates Data

Data is captured from multiple data sources and integrated into a DW. Here, data is processed further to prepare it for BI. In a data warehouse, integration processes are pretty good to achieve as consistent data as possible within the data quality limitations. While MDM demands more robust and extensive integration processes that capture, cleanse, conform, reconcile, validate, authorize, and thus publish the agreed-upon master data. Processes of this kind call for more extensive tools than extract, transform, and load (ETL) tools, typically used to load a data warehouse.

What is Master Data Management?

Master data management is a technology-enabled discipline wherein business and IT work in unison to ensure the accuracy, uniformity, semantic consistency, stewardship, and accountability of the enterprise’s official shared master data assets. Master data refers to the uniform and consistent set of identifiers and extended attributes, elucidating the enterprise’s core entities that comprise prospects, citizens, customers, suppliers, hierarchies, sites, and charts of accounts.

How Does Master Data Management Help?

An effective MDM data analytics solution manages data from varied sources to offer contextual capabilities and actionable insights. A central MDM platform enables business users to access accurate and consistent information across all customer touchpoints. Through MDM, you can identify the most crucial information within your organization and create a unique source. This unique or single source of truth enables you to empower your business processes. Comprising data integration, business process management, and data quality, MDM alleviates IT processing in environments with multiple system architectures, applications, and platforms. Besides, master data management solution streamlines data sharing at the service and staff levels.

Benefits of Master Data Management

  • It acts as a single source of truth.
  • It helps to eliminate poor-quality data.
  • It enhances efficiency by distributing trusted data.
  • It prioritizes data effectively.
  • It improves decision-making with data standardization.
  • It ensures superior regulatory compliance and effective risk management with domain-specific data definitions.
  • It guarantees cost-optimization and faster return-on-investment (ROI).

Small and medium-sized businesses (SMBs) are transitioning towards adopting a master data management strategy to organize, categorize, and localize their master data, following their business processes and scale of operations.


Challenges With Master Data Management

Cost of Implementation

At the organizational level, MDM costs rise as it demands you to build a framework for aligning multiple business divisions across siloed processes. On the technology front, costs add up due to support, maintenance, software licenses, professional services, and in-house IT resource requirements.

Maintaining Multi-Domain MDM

Organizations strategize multi-domain capabilities to build highly connected data networks for improved accuracy and completeness of data. With a multi-domain master data management solution in place, an organization can rest assured that it can overcome challenges of different roles within the same domain or relationship mapping of multiple domains.

MDM Not Just About Technology

Though the crux of MDM, i.e., master data management, is technology, implementing the master data management process extends beyond the software to include application infrastructure and data. In master data management, IT and business entities need to work together to ensure the accuracy, uniformity, and accountability of an organization’s master data. The questions related to data ownership, data centralization, and reference data location are essential to adhere to GDPR and HIPPA norms.

Data Warehouse vs. Master Data Management – An Ideal Approach

Now that we know about data management solutions, i.e., data warehouse and master data management processes, in detail, you can take the right data management approach for your business. Remember, while data warehouses are well suited to report high volumes of transaction data, master data management solutions cannot process transactional data, aggregate data, or BI applications. However, the ideal approach is to set up an MDM solution and then build the data warehouse. Master data management serves as the best source for the data warehouse. With proper integration of MDM with data warehouse, master data services (MDS) significantly improve data quality, reducing the scope, size, and complexity of data warehouse architectures.

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