Category Archives: Master Data Management
Master Data Consolidation Versus Master Data Sharing: Modeling Matters! – Part 3
In my last post: Master Data Modelers, I alluded to a fundamental issue with the way that some organizations drive their master data management project plan, and that a fundamental issue influences the modeling approach that is taken. It centers on what should be a very simple question: is MDM about data consolidation or data sharing? (more…)
30 MDM Customer Use Cases (Master Data Management in Action)
Master Data Management (MDM) has been used by companies for more than eight years to address the challenge of fragmented and inconsistent data across systems. Over the years we’ve compiled quite a cadre of uses cases across industries and strategic initiatives. I thought this outline of the 30 most common MDM initiatives may be of interest to those of you who are just getting started on your MDM journey. (more…)
Is Your Approach to Multidomain MDM Upside Down?
That’s the question I asked in two of my blogs about a couple of years back – Why The “Upside-Down” Approach Doesn’t Work For Multidomain MDM and Do The Math: Platform Approach Adds Up As The Superior Alternative To Multidomain MDM. A detailed version of this content was later published in Database Trends and Applications. Also, you can download this content as a whitepaper from the Informatica Resources webpage. (more…)
Master Data Model Alternatives – Part 2
Last time I introduced two different approaches for master data models and thought it would be worth examining the differences in greater detail.
The first approach is to use pre-packaged core models provided by a vendor as part of an overall MDM suite of tools. Often these types of products evolved out of industry applications in which a common information model was used to support specific types of enterprise applications. For example, a vendor might have analyzed the property and casualty insurance industry and developed core data models for customer, policy, claim, service, financial products, etc. A set of application layers may have been developed on top of these models to implement common workflows (customer risk rating for establishing premium rates, or initiating a claim). However, there is a perception that aspects of those industry-oriented models can be segregated into a more universal format, which can become the starting point for a prepackaged master domain. (more…)
Structure, Semantics and Master Data Models – Part 1
Looking back at some of my Informatica Perspectives posts over the past year or so, I reflected on some common themes about data management and data governance, especially in the context of master data management and particularly, master data models. As both the tools and the practices around MDM mature, we have seen some disillusionment in attempts to deploy an MDM solution, with our customers noting that they continue to hit bumps in the road in the technical implementation associated with both master data consolidation and then with publication of shared master data.
Almost every issue we see can be characterized into one of three buckets: (more…)
Opportunities for Healthcare Organizations to Move Data Forward
In this video, Richard Cramer, chief healthcare strategist, Informatica, talks about the opportunities for healthcare organizations to move data forward. He touches on relationship analytics, master data management (MDM), data quality and Complex Event Processing (CEP). He specifically answers the following questions:
- What are some of the major opportunities for healthcare organizations to move data forward?
- What technology is most poised to deliver benefits to healthcare organizations today?
- How can Informatica help healthcare organizations in their quest to deliver proactive medicine?
Social Media Monitoring with CEP, pt. 2: Context As Important As Sentiment
When I last wrote about social media monitoring, I made a case for using a technology like Complex Event Processing (“CEP”) to detect rapidly growing and geospatially-oriented social media mentions that can provide early warning detection for the public good (Social Media Monitoring for Early Warning of Public Safety Issues, Oct. 27, 2011).
A recent article by Chris Matyszczyk of CNET highlights the often conflicting and confusing nature of monitoring social media. A 26-year old British citizen, Leigh Van Bryan, gearing up for a holiday of partying in Los Angeles, California (USA), tweeted in British slang his intention to have a good time: “Free this week, for quick gossip/prep before I go and destroy America.” Since I’m not too far removed the culture of youth, I did take this to mean partying, cutting loose, having a good time (and other not-so-current definitions.) (more…)
Classifying Types of Data Management Issues
Coincidentally, my company is involved with a number of different customers who are reviewing the quality criteria associated with addresses. Each scenario has different motivations for assessing address data quality. One use case focuses on administrative management – ensuring that things that need to happen at a particular location have an accurate and valid address. A different use case considers one aspect of regulatory compliance regarding protection of private information (since mail delivered to the wrong address is a potential exposure of the private information contained within the envelope). Another compliance use case looks at timely delivery of hard copy notifications as part of a legal process, requiring the correct address. (more…)
ANNOUNCING! The 2012 Data Virtualization Architect-to-Architect & Business Value Program
Today, agility and timely visibility are critical to the business. No wonder CIO.com, states that business intelligence (BI) will be the top technology priority for CIOs in 2012. However, is your data architecture agile enough to handle these exacting demands?
In his blog Top 10 Business Intelligence Predictions For 2012, Boris Evelson of Forrester Research, Inc., states that traditional BI approaches often fall short for the two following reasons (among many others):
- BI hasn’t fully empowered information workers, who still largely depend on IT
- BI platforms, tools and applications aren’t agile enough (more…)
What it Takes to Be a Leader in Data Virtualization!
If you haven’t already, I think you should read The Forrester Wave™: Data Virtualization, Q1 2012. For several reasons – one, to truly understand the space, and two, to understand the critical capabilities required to be a solution that solves real data integration problems.
At the very outset, let’s clearly define Data Virtualization. Simply put, Data Virtualization is foundational to Data Integration. It enables fast and direct access to the critical data and reports that the business needs and trusts. It is not to be confused with simple, traditional Data Federation. Instead, think of it as a superset which must complement existing data architectures to support BI agility, MDM and SOA. (more…)
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