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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

Top Reasons an MDM Implementation Fails

I often become involved in an organization’s MDM program when they’ve reached out to InfoTrellis for help with cleaning up after a failed project or initiating attempt number X at achieving what, to some, is a real struggle. There can be a lot of reasons for a Master Data Management implementation failing, and none of them are due to the litany of blame game reasons that can be used in these scenarios.  Most failures arise from common problems that people

Big Data Enables CPG Companies to Gain an In-Depth, Personal Connection with the End User

This article was featured in the Q3 2014 edition of Loyalty 360‘s Loyalty Management magazine.

Consumer Packaged Goods (CPG) companies have accepted for many decades that the reality of the industry is that the customers are interacting with intermediaries like digital merchants and retail outlets, not directly with them. The store gets to develop the relationship with the customer and the CPG company has to bridge a bigger gap, targeting end-users with broad strokes like TV commercials or

Using External Data in MDM Systems

Let me start by saying that this is not an article about big data.  While the source of big data is external to your organization, it is a topic of its own.  Many of the concepts and approaches discussed will definitely apply to your big data initiatives, but that won’t be the focus of this article.

External data is information that is sourced from outside of your organization.  This could be information you purchase from a marketing or service organization,

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