The Next Step: Enhancing the Master Data Via Data Warehouse Consolidation

Apr 30, 2013 by David Loshin

Last time we looked at how the tools and methods incorporated within a master data management system can contribute to ensuring the satisfaction of the success criteria for data warehouse consolidation. MDM provides some valuable capabilities that will simplify the consolidation processes.

Facing Maturity – Where Has The Gang Gone?

Apr 03, 2012 by Joyce Norris-Montanari

Times are changing again.  Some of my friends are doing the semi-retiring thing, some are totally retiring, and some are just changing jobs.  I am seeing movement in the industry for specific Information Technology (IT) positions.  Especially, positions surrounding data modeling, data integration, data management, master data management, and data warehousing.  For those of us that are being left behind, make sure you understand the following:

How Neat are Your Circles?

Jan 26, 2012 by Phil Simon

On her popular blog, my friend Jill Dyché recently wrote about Big Data Governance. From the post:

Often clients explain that they need to treat transaction data differently than they need to treat, say, customer master data. Fewer business rules, more history, that kind of thing.  That’s when we start the work of classifying different…

Holistic Data Management (Part 3)

Sep 29, 2010 by Jim Harris

In the series introduction, I used a brain metaphor to compare and contrast operational and analytical data management as two half-brains designed to work together as a single, integrated whole in one complete data management brain.

In Part 1, I discussed operational data management, which focuses on the upstream systems where data is created by the business and technical processes that support daily activities.

In Part 2, I discussed analytical information management, which focuses on the downstream systems where data is used to make the business decisions that drive tactical and strategic initiatives.

In Part 3, the series concludes by discussing the need for a holistic approach that synchronizes operational data management and analytical information management.

The Data Management Brain

To download this diagram, click on the following link: Holistic Data Management

Holistic Data Management (Part 2)

Sep 22, 2010 by Jim Harris

In the series introduction, I used a brain metaphor to compare and contrast operational and analytical data management as two half-brains designed to work together as a single, integrated whole in one complete data management brain.

In Part 1, I discussed operational data management, which focuses on the upstream systems where data is created by the business and technical processes that support daily activities.

In Part 2, I will discuss analytical data management, which focuses on the downstream systems where data is used to make the business decisions that drive tactical and strategic initiatives.

The Data-Information Bridge

In Part 1, the connection between the operational and analytical hemispheres of the data management brain was described as a data exchange.

Data cleansing, transformation and enrichment occurs during this data exchange, the result of which is often passed downstream without being used to update the upstream source systems where the operational data originated.

The Fourth Law of Data Quality explained that data quality standards include both objective data quality and subjective information quality.

Data’s quality is determined by evaluating its fitness for the purpose of business use.  However, in the vast majority of cases, data has multiple business uses, and data of sufficient quality for one use may not be for other, and perhaps unintended, uses.

I define information as customized data and information quality as fitness for the purpose of a specific business use, which meets the subjective needs of a particular business unit and/or a particular tactical or strategic initiative.

Therefore, I like to refer to the data exchange between the operational and analytical hemispheres of the data management brain as the Data-Information Bridge.

By passing over this bridge, the operational data becomes the analytical information used to make the business decisions that drive tactical and strategic initiatives.

Some of the most important activities of enterprise data management actually occur on the Data-Information Bridge, where preventing critical disconnects between operational and analytical data is essential for success.

I will discuss these aspects in more detail in Part 3 of this series.

For now, let’s focus on the analytical hemisphere of the data management brain.

Holistic Data Management (Part 1)

Sep 15, 2010 by Jim Harris

In three weeks, most of the Community of Experts will be attending the DataFlux IDEAS 2010 conference being held October 4-6 in Palm Springs, California.

The conference provides a chance to see how the data management landscape is changing, and features a keynote address by industry thought-leader Jill Dyché of Baseline Consulting, and sessions led by experienced data management practitioners and DataFlux experts. 

In addition, conference attendees will:

• Get an in-depth look at the new DataFlux Data Management Platform features
• Hear from real-world customers as they share their data quality, data integration and MDM success stories
• Learn about data management trends and issues
• See best-practice solutions first-hand from the people who work with data every day
• Take advantage of one-on-one personalized consulting appointments with DataFlux experts

David Loshin will be conducting a data quality workshop.  Joyce Norris-Montanari will be moderating two customer panels, one of which will feature Rich Murnane.

Phil Simon and I will be on an expert panel with Steve Schutter, the Managing Partner of Teradata Americas Enterprise Data Management Center of Excellence.

The moderator will be Ron Powell, the Associate Publisher and Editorial Director of BeyeNETWORK TechTarget.  The expert panel discussion topic will be:

Data Management in Operational vs. Analytical Environments:

Finding Common Ground

For years, organizations performed data quality and data integration techniques to manage data as it entered a data warehouse to support analytical efforts.

Now, companies are also investing time, energy and resources into similar efforts on the operational side.  In this panel, we will discuss techniques to make each side more efficient — and how to replicate success across the IT environment.

Potato Chips and The Myth of the Data Warehouse

Jun 24, 2010 by Phil Simon

A friend of mine (call him Bruce) recently accepted a job as an HRIS manager for a mid-sized company. During the interview process, he asked questions about the company’s current systems’ infrastructure and architecture. Bruce was told that there were “challenges”, to put it euphemistically. One of his chief objectives would be to attempt to simplify what had become a very complicated albatross of financial, HR, and payroll systems. Suffice it to say, this would be a Herculean endeavor.

Upon starting his position, Bruce did what most professionals would do in this situation: he talked to people about what they liked, didn’t like, and expected from their applications, systems, and data. Among his findings, his colleagues wanted the following in the near future:

• better reporting
• less redundancy among systems
• improved access to data
• simplified versions of the truth

Now, a few of the financial and HR folks threw out a potentially dangerous term: data warehouse. Why do I qualify data warehouse with the phrase “potentially dangerous?” Because it’s one of those terms that laypeople often mistake as silver bullet.