Every business is built on its data. Regardless of how that data is created or what it represents, accurate, accessible, uniform, and consistent information is the keystone of a solid organization. A master data management (MDM) solution puts you firmly in control of the quality, accuracy, and governance of your organization’s data.
As compliance and regulatory standards get increasingly more complex over time, maintaining a single source of data truth becomes more and more critical. This is what master data management is built to do. MDM ensures data quality is maintained by establishing robust guidelines for data governance, access, and management.
With multiple decades of operational experience, Trianz is ideally placed to advise on the best MDM approach to meet your business needs. Here we look at common MDM implementation styles that can help you to decide which solutions most closely match the requirements of your organization.
Transactional master data management solutions maintain a centralized source of data that updates sources from its single authoritative record. Master data is updated by consistently publishing changes back to their original source.
Systems built using a transactional approach ensure the master record is always accurate, complete, and up to date. Perhaps one of the simplest and most effective master data management strategies, transactional systems simplify security policies and data verification.
Data consolidation relies on multiple sources within the hub to create a single version of data truth. In MDM this concept of sole authentic copy is known as the golden record. This record is stored in the central hub with updates and changes to master data being later applied to their source.
Consolidation enables organizations to pull master data from existing systems and bring them into a single managed MDM hub. Within the hub, data can be cleansed, integrated, and matched to maintain a single record of the master domain.
Some advantages of a consolidation style MDM approach are:
Fast and efficient system to provide enterprise-wide reporting
Inexpensive way to set up a master data management solution
Ideal for data analysis
Similar to consolidation, coexistence-style MDM enables the construction of a golden record that maintains a verified copy of data. However, coexistence maintains this golden record as only a part of the central MDM system. Application systems in a coexistent MDM solution are permitted to make changes to data at will.
Changes to data from application systems are later synchronized with the hub. The primary benefit to the coexistence style is the ability to maintain separate sources of data while still offering a single version of data truth.
Some other advantages of coexistence MDM:
Master data quality is improved by the addition of sources
Faster access to a single source
Reporting is simplified as all data attributes are maintained in a single place
Registry-style MDM never sends data back to the source systems. The system works by attributing unique identifiers to each record to index data and enable a single record of truth. A view of the source data can be accessed as required, governed by the golden record to ensure reliability.
The registry style establishes a single, authoritative source available to source systems. Data can be analyzed without the risk of being overwritten from source systems. These features can be ideal when prioritizing issues such as regulatory compliance.
Trianz is a world leader in master data management consulting. Depending on the challenges your business faces and the priorities your organization wants to establish, any one of these or other master data management solutions could be what sets you on the right track.
Our extensive knowledge, partnerships, and MDM best practices enable us to advise you with expert care on the optimal solution and services for your data.
Contact our master data management team today to ensure the foundations of your business are on solid footing.
Contact Us Today
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
A Winning Base for Successful Digital Transformations When it comes to developing a successful digital strategy, it is not just corporations planning to maximize the benefits of data assets and technology-focused initiatives. The Government of Western Australia recently unveiled four key priorities for digital reform in its new Digital Strategy for 2021-2025.Explore
Engage Your Workforce with a Modern Employee Intranet Solution The employee intranet has changed significantly since it was first introduced in the early 1990s. What started as HTML-based static portals have now evolved into intuitive communication tools complete with search engines, user profiles, blogs, event planners, and more. Today, many organizations are taking a second look at employee intranets to bridge gaps between teams, build company culture, centralize information, increase productivity, and improve workflow.Explore
Adopting emerging cloud technologies, consolidating resources, and improving processes is the key. “IT no longer just supports corporate operations as it traditionally has but is fully participating in business value delivery. Not only does this shift IT from a back-office role to the front of business, but it also changes the source of funding from an overhead expense that is maintained, monitored, and sometimes cut, to the thing that drives revenue,” said John-David Lovelock, research vice president at Gartner.Explore
Deliver Powerful Insights Instantaneously with Federated Queries - No Matter Where Your Data Resides The concept of federated queries isn’t new. Facebook PrestoDB popularized the idea of distributed structured query language (SQL) query engines in 2013. Over the years, AWS, Google, Microsoft, and many others in the industry have accelerated the adoption of a distributed query engine model within their products. For example, AWS developed Amazon Athena on top of the Presto code base, while Google’s BigQuery is based on Cloud SQL.Explore
What is Unstructured Data? Almost 80% of the data that enterprises and organizations collect is unstructured - data without a set record format or structure. Unstructured data includes data such as emails, web pages, PDFs, documents, customer feedback, in-app reviews, social media, video files, audio files, and images.Explore