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Product Information Management and Global Data Synchronization

The digital era has fostered information transfer between two systems but the communication flaws have led to information loss. For example; Product Information shared between manufacturers and retailers. Manufacturers often communicate about new Products or changes to existing Products, Price information changes to retailers manually and in an ad hoc manner, leading to the data quality and integrity issues in key retail systems. These problems result in revenue loss and dissatisfied consumers.

Considering these challenges in mind, GDSN (Global Data

MDM for Regulatory Compliance in the Banking Industry

Banking Regulations

Banking Regulations – Overview

Managing regulatory issues and risk has never been so complex. Regulatory expectations continue to rise with increased emphasis on the institution’s ability to respond to the next potential crisis. Financial Institutions continue to face challenges implementing a comprehensive enterprise-wide governance program that meets all current and future regulatory expectations. There has been a phenomenal rise in expectations related to data quality, risk analytics and regulatory reporting.

Define your Data Governance

Data Governance is the process of understanding, managing and making the critical data available with the goal to maximize its value and to ensure compliance.

InfoTrellis’ Data Governance Methodology follows a multi-phased iterative approach with 4 stages – Initiate, Define, Deploy and Optimize. This article is the second part of the Data Governance Methodology series by InfoTrellis. The first part of this series – Initiate your Data Governance – listed the essential foundations of successful Data Governance program.

‘Define’

Initiate your Data Governance

Data Governance is the process of understanding, managing and making the critical data available with the goal to maximize its value and to ensure compliance.

Data Governance is an important and imperative area for Enterprises that want to realize full value from the data available with them.  InfoTrellis’ Data Governance Methodology follows a multi-phased iterative approach with 4 stages – Initiate, Define, Deploy and Optimize. This article lists the important considerations that are part of the Initiate stage of our

Virtual and Physical MDM in the same box, best of both worlds!

With the introduction of IBM Master Data Management v11, IBM has created a new implementation style combining the strengths of both MDM Physical and Virtual editions. While MDM Physical is more suited to the “centralized” MDM style (system of record), and MDM Virtual is aligned with the “registry” MDM style (system of reference), MDM Hybrid uses a “coexistence” style to provide a mixed system of reference & record. This article will give an overview of the MDM Hybrid implementation style

Common Sense is very uncommon

Effort is important, but knowing where to make an effort makes all the difference!”

A few days ago, at the end of a very intense release, one of our long term clients asked what is the secret behind our team’s high quality testing effort, despite the very aggressive timelines and vast scope of work that she sets up for us. She was very much interested in understanding what we do different from the many large SI’s she has used in

IBM MDM BatchProcessor – Tips for better throughput

MDM BatchProcessor is a multi-threaded J2SE client application used in most of the MDM implementations to load large volumes of enterprise data into MDM during initial and delta loads. Oftentimes, processing large volumes of data might cause performance issues during the Batch Processing stage thus bringing down the TPS (Transactions per Second).

Poor performance of the batch processor often disrupts the data load process and impacts the go-live plans. Unfortunately, there is no panacea available for this common problem. Let

Managing MDM Projects II – Development, Testing and Deployment

Calvin: “You can’t just turn on creativity like a faucet. You have to be in the right mood.”
Hobbes: “What mood is that?”
Calvin: “
Last-minute panic.”

Okay, apologies for an unscheduled delay on the follow up post. Let’s get back to discussing how we manage our MDM Projects.

In my previous post, we talked about the first two stages of “InfoTrellis SMART MDM Methodology”, namely “Discovery and Assessment” and “Scope and Approach”. In these two stages, we spoke

© Phil Date | Dreamstime Stock Photos

The Christmas Shopping Big Data Use Case

© Phil Date | Dreamstime Stock Photos

© Phil Date | Dreamstime Stock Photos

There’s a metaphor I like to use about public washrooms. Have you ever been in a public washroom where the toilet flushes automatically, the soap dispenses automatically, and the water turns on and off automatically, but then the drier is manual, and it

Retailers’ Successes and Struggles with Big Data in 2014

Recent research by McKinsey and the Massachusetts Institute of Technology shows that companies that inject big data and analytics into their operations outperform their peers by 5% in productivity and 6% in profitability. Our experience suggests that for retail and CPG companies, the upside is at least as great, if not greater.”

Peter Breuer, director of McKinsey & Co.’s retail practice in Germany

With November half over and 2015 starting to peek at us over

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