MASTER DATA MANAGEMENT TOOLS: Best Data Management Tools

Master Data Management Tools
Photo Credit: HubSpot Blog

Data and the insights gained from it are at the heart of today’s most successful enterprises. Indeed, data has become so important to success that many firms regularly monitor the quality and utility of their data. Organizations must prioritize data management as the volume of data collected and consumed grows (and so does its importance). This includes maintaining it for cleanliness, correctness, and consistency throughout the company, as well as managing access to it and its action-ability. To assist you in this process, we conducted preliminary research by assembling and comparing some of the best master data management tools on the market today. We also looked into more details about master data management. Relax and read!

Overview

Many businesses are relying on the best master data management tools to supplement and assist with their data management and quality activities. The best master data management tools may assist teams with data management success. This is done by increasing business alignment, data governance, data delivery, use, measurement, organization, and other areas.

After determining the need for the best master data management tools, you must select the appropriate solution for your firm. However, with the variety of master data management solutions available today, this can be a daunting process in and of itself.

What Are the Best Master Data Management Tools?

The best Master Data Management tools are those unique tools that assist to ensure the consistency and accuracy of the organization’s common data, often known as master data. Most businesses today run a variety of different systems, all of which include significant data on customers, the business, or other critical business KPIs, such as CRMs, ERPs, and so on. This results in data silos, redundant data, missing data, and, as a result, a fragmented perspective of the business. 

Because data is spread across multiple locations and languages, addressing simple business questions like “What services did our customers use the most last quarter?” or “Who is our most lucrative customer?” becomes challenging.

Master Data Management must be a collaborative effort that is ongoing to be successful. Larger firms would typically elect a group of people to define and implement data quality best practices. As a result, executive buy-in and support are important to the success of any master data management initiative.

The Four Types of Master Data Management (MDM)

Master data management involves four main types of implementation styles. These styles include:

#1. The Registry style

We’ll start with the Registry style, which is mostly used to detect duplication by running cleansing and matching algorithms on data from your various source systems. It assigns distinct global identities to match records to aid in the identification of a single version of the truth.

Because this style does not communicate data back to the source systems, updates to master data are still made through the existing source systems. Instead, it cleans and matches the cross-referenced identifying information and assumes the source system can control the quality of its data.

#2. The Consolidation style

Let’s take a look at the Consolidation style next. Consolidation styles often integrate master data from numerous sources in the hub to establish a single version of the truth, also known as the golden record.

The central hub stores a golden record that is utilized for reporting and reference. Any changes made to the master data, on the other hand, are applied to the sources.

#3. The Coexistence style

A Coexistence style allows you to build a golden record in the same manner that a Consolidation style does. Your master data is kept in a central Master Data Management system and is updated in its source systems. The Coexistence style may be more expensive to deploy when compared to the Consolidation model.

#4. Transaction/Centralized style

The Transaction style saves and keeps master data properties by enhancing the data with connecting, cleansing, matching, and enrichment algorithms. The improved data can then be published back to its original system.

To ensure perfect consistency, the hub facilitates the merging of master records, and source systems can subscribe to updates broadcast by the central system. However, for the two-way interactions, this technique necessitates intrusion into the source systems.

Are the Best Master Data Management Tools ETL Tools? 

A complete Enterprise Information Management (EIM) architecture includes Master Data Management (MDM) and Data Integration or Extract, Transform, Load (DI or ETL) technologies. However, there is some confusion and twist about how they relate to one another. 

The best master data management tools and systems create rules for data cleansing, record matching and merging, data enrichment, and other data-related tasks. In the event of an anomaly, the MDM will pass the offending records to a data steward for manual intervention and approval. 

The domain expertise concerning business data is held by the data steward rather than IT. When automatic cleansing defers to manual intervention, the amount of the confidence threshold determines. 

Data stewards are subject matter experts who work in the business. They will interact directly with the MDM via its user interface, tying data quality responsibilities to the business. As a result, data management is significantly more agile.

But how does data enter the MDM? How does it complete the round-trip to the source systems? This component of the solution will be provided by ETL tools.

So, once your information management infrastructure is suitable for master data management, think about which MDM product to add to your existing combination of operating systems, databases, and ETL tools.

List of the Best Master Data Management Tools

Below is a list of a few of the best master data management tools you can find in 2022. They include:

#1. Ataccama 

This provides an augmented data management platform with capabilities such as data discovery and profiling, metadata management, and a data catalog. It also provides data quality management, master and reference data management, and big data processing and integration. 

The product is fully integrated while remaining adaptable to accommodate any data, user, domain, or deployment method. Text analytics and machine learning are also included in Ataccama, as is data enrichment with external sources and data lake profiling.

#2. IBM InfoSphere Master Data Management 

This is one of the best master data management that maintains all aspects of key company data, regardless of system or model. It makes it available to application users through a unified view. 

The solution ensures that data governance rules and policies are followed, and it includes a flexible framework that supports hybrid cloud settings. InfoSphere MDM is available in two editions (standard and advanced), both on-premises and as fully-managed cloud services.

#3. Informatica 

Informatica provides a modular MDM solution with a unified view of data. Users can use the product to build an authoritative view of business-critical data from disparate, duplicate, and contradictory sources. 

Informatica MDM contains AI and machine learning capabilities, as well as data quality, data integration, business process management, and data security features. These enable you to effortlessly enrich master data records with data from external suppliers. The MDM features from Informatica can be installed on-premises or in the cloud.

#4. Synchronos

The Synchronos platform is a business MDM solution that can be utilized for operational or analytical purposes. The product is available for on-premises, cloud, or hybrid deployment. 

Synchronos includes data profiling, discovery, and monitoring capabilities, as well as a 360-degree perspective. This perspective allows users to discover information regarding extended relationships within data. 

Also, customers can use workflow management to create and change workflows, while hierarchy management provides a graphical representation and navigational capabilities.

#5. Semarchy 

The Semarchy provides xDM, a master data management solution. Machine learning techniques are used in the platform to enable stewardship and advanced matching, survivorship, curation, and classification. The tool has a native data model that allows for visible lineage, audibility, and governance. 

To combine the data hub with current applications and business processes, xDM can integrate any data source via real-time and batch APIs. Semarchy provides a 30-day license key free trial of xDM for on-premises and cloud deployments.

Benefits Of The Best Master Data Management Tools And Systems

The best master data management tools and systems can provide big benefits when enterprise data is kept healthy across systems and departments. Some of these benefits include:

#1. Lowering the Total Operating Costs

Consider all elements of your organization that rely on healthy data to function well, elements like:

  • Every application and its dependencies
  • Employee operations, ranging from manufacturing to human resource events
  • Data storage, comprising both hot (working) and cold (archival) data
  • Inventory plans, supply chain logistics, ordering procedures, and more

Even the smallest difference in any of this data from one system or department to another can set off a chain reaction. This can quickly affect all connected information, raising operating costs, and compromising the organization’s profitability. And this bad data frequently causes more damage than it appears. 

#2.Reduction in Architectural Bloat 

MDM influences the bottom line in ways other than reducing lost business. The quantity of resources consumed has a direct impact on the cost of running and managing network infrastructure, whether on-premises, hybrid, or in the cloud. Storage space, processing time, and network throughput are all included.

#3. Deliveries Are Made Faster

MDM is an important concern for current development methods such as continuous delivery, DevOps, robust DevOps, and other design architectures that demand shared and accurate data.

With a strong data reservoir feeding development teams, apps and enhancements move more quickly through the delivery pipeline. This means that MDM discoveries made today might be included in software right away, rather than after a lengthy review and recoding process.

#4. There Are Simplified Compliance

Compliance is a very important concern in today’s digital corporate world. HIPAA, PCI, CCPA, GDPR, and other regulatory frameworks provide constantly changing criteria that businesses must achieve to remain compliant. Compliance may (and often is) be a full-time job in larger firms.

#5. Improvement in Customer Experience

Time is money, as the adage goes. This has never been truer in a digital world that moves at the speed of modern business, especially when it comes to your audience’s time. MDM enables you to communicate with your customers at every stage of the transaction process and improve your performance based on real-time feedback. 

It does this by reducing inconsistencies and errors that affect product delivery, from the first app interaction through shipment, delivery, and feedback.

#6. A 360-Degree View

A modern, cloud-based MDM approach provides a complete, real-time view of each customer. MDM creates a “golden record” of information for marketers to use for segmentation, online personalization, and a deeper understanding of the customer lifecycle.

#7. It Provides Actionable Business Intelligence

Creating a clear and current image of all business activities allows decision makers to zoom in on troubled areas in business processes. It also allows them to pull back to a satellite perspective to understand how national or global trends are affecting an organization.

What Is the Relationship Between MDM and Data Quality?

Consider data quality to be the atoms that make up a life form—your MDM program. You won’t get much use out of MDM if the data isn’t fit for purpose. Data cleansing and standardization can help you reduce rework by preventing poor-quality data from entering your system. 

Furthermore, putting data quality first may shift your MDM goals by uncovering underlying issues and primary causes. As a result, data quality is a crucial component of communicating the value of MDM.

Can Small Firms Use MDM?

While comprehensive, stack solutions are typically acquired for enterprise-level data management, SMBs can find solutions that can be purchased in pieces and fitted together, beginning with data quality. You can begin with simple, focused activities, such as locating solutions for data purification and data profiling.

Conclusion

Data-driven firms will become more nimble in their data management. The best master data management tools and systems market demand is evolving toward where and how they bring value to the business.

As a result, the emphasis will be on more intelligent and outcome-based MDM implementation to enable stronger revenue growth and profits, cost optimization and efficiency, regulatory compliance, and risk management. 

Master Data Management Tools FAQs

How can MDM benefit my organization?

Your organization most likely includes several distinct, overlapping systems that are incompatible with one another. MDM allows you to share master data across applications while maintaining consistency.

Is MDM the same as Single Customer View (SCV)?

One possible result of MDM is a single customer view (SCV). While MDM projects may apply to connecting internal relationships, SCV is concerned with giving a unified, consistent view of data relevant to individual customers across your organization. 

Reference

  1. tibco.com
  2. solutionsreview.com
  3. talend.com
  4. semarchy.com
  5. stibosystems.com
  6. pimcore.com
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