The DataFlux Community of Experts is a forum for industry thought leaders to provide perspective and insight and engage in discussions on issues surrounding data governance, data quality and data integration. Our regular contributors include David Loshin (president, Knowledge Integrity, Inc.), Joyce Norris-Montanari (president, DBTech Solutions), Mike Ferguson (managing director, Intelligent Business Strategies) and Dylan Jones (founder of Data Quality Pro and Data Migration Pro).
Data migration and the optimistic project manager
Posted by Dylan Jones in data integration on June 30th, 2009
In my last community of experts post I talked about the need for a dedicated “quantity surveyor” on data migration projects.
Without a skilled individual to help quantify tasks, resources and ultimately costs, there is little hope that a migration will come in on time and budget.
One caveat I need to add is that when it comes to data migration projects, you line yourself up for failure by planning too far into the future.
A problem I commonly see is well intentioned project managers trying to plan out the entire project in detail from day 1 to completion.
Contact Mechanisms
Posted by David Loshin in master data management on June 25th, 2009
Is a contact mechanism a core master object? I think so, since it embodies a relational representation of the multiple channels through which an organization interacts with its constituents. When limitations are placed on proactive communication with a customer base (e.g., “do not call” lists), understanding the potential interaction channels becomes much more important, especially in terms of how the channel is used, when it is used, effectiveness of communication, who initiates the contact, and under what circumstances. Read the rest of this entry »
Scorecards!
Posted by Joyce Norris-Montanari in data integration on June 23rd, 2009
You know everyone has a scorecard! Really! We have scorecards for data quality, security, compliance, job performance, etc. A scorecard is defined as a card used to report the scores of a sports contest. Hmmm how do I use that in information technology? We could say that a score card is used to report a rating. That would apply to all the scorecards listed above. So what is a balanced scorecard? A balanced scorecard is an analysis technique to translate goals and strategy into something that is quantifiable. You could go so far as to say that these scores could be compared to industry standards or industry best practices. So creating the scorecard or comparison mechanism once seems to be a pretty organized and comprehensive approach to compare a company in a specific vertical. But, how do I compare them over time?
Read the rest of this entry »
Simple Counts for Small Businesses
Posted by David Loshin in customer data integration, data quality, master data management on June 18th, 2009
Before I can analyze my customers, I have to know who my customers are. I need to know a few things for this:
- Who is a customer?
- Where is customer data stored?
- How many customers do I have?
Of course, there are a lot more things I would ultimately need to know, but from first principles, I cannot analyze anything that I can’t count. So what other simple counts do I need? Products? Sales? Orders? Employees? Supplies? Inventory? Without a doubt, you need this data to run your business, whether from a revenue standpoint or an operational management standpoint.
TDWI Best Practice Awards
Posted by Joyce Norris-Montanari in data integration on June 16th, 2009
I just completed the review for best practice awards for The Data Warehouse Institute (TDWI). There were so many submissions, that it took quite a while to review them properly. I found a common theme on some of the entries where they did not discuss the data integration or data quality issues that they faced during implementation. Was there any Return On Investment (ROI) for the integration efforts? How would you calculate the fact that you didn’t have to compile and join twelve (12) tables to get a report on customer churn? How much is it worth to have that information at your fingertips?
Read the rest of this entry »
Master Data Concepts and Master Data Objects
Posted by David Loshin in master data management on June 11th, 2009
I have heard a lot of talk about “customer masters” and “product masters,” but what are other data concepts that warrant mastering? Is it worth cataloguing a list of master data concepts? I would think so, especially when you might want to seek out existing model archetypes that can be adapted to the enterprise environment. One could start with a list of base data concepts and then consider the different ways those concepts can be qualified in relation to multiple usage scenarios across the organization. Read the rest of this entry »
Data Integration – Where Does it Fit in an Organization?
Posted by Joyce Norris-Montanari in data integration on June 9th, 2009
In the last couple of blogs, I have concentrated on Data Integration as a practice or discipline. I also talked about the need for Data Integration in an organization. Data Integration is just part of an Enterprise Information Management (EIM) practice at your organization. EIM has the following components:
IT Challenges for Enterprise Data Quality
Posted by David Loshin in data governance, data quality on June 4th, 2009
There are many IT challenges for implementing enterprise data quality, but here are a few as food for thought.
- Managing usage scenarios – First is the orientation of the processes for requirements analysis for systems along functional lines without considering the multiple uses of data across multiple lines of business or for both operational and analytic needs. Because individuals typically only consider immediate data usage scenarios, they do not incorporate the needs of other data consumers downstream. Once those needs are identified, it is difficult to determine whose responsibility it is for addressing them.
Problems with Data Integration
Posted by Joyce Norris-Montanari in data integration on June 2nd, 2009
Data integration issues are not a new problem. In the 1960s companies started adopting database management systems instead of flat file systems as a corporate repository for information. This thought naturally led to the integration of data into one place for the following reasons:
- One source of data to maintain for the corporation
- One platform to maintain for the corporation
- Less security issues – every system had the same levels of security applied during the application
Does the quality of your information delight or satisfy customers?
Posted by Dylan Jones in customer data integration, data quality on May 26th, 2009
In a down economy there is no place for mediocrity. Consumers are even more price sensitive and demand greater value.
A simple equation for customer value is: Value = Benefits/Cost
In a down economy, most organisations focus on the right hand side of that equation by dropping the price of their products and services.
Everywhere we look, widgets are coming down in price. Invariably this hits the profit performance so cuts in labour and other overheads are made to protect the margins.
I’ve recently spoken to a member of our community on Data Quality Pro who has lost her job as a data quality analyst. Cutbacks meant they couldn’t sustain the project and her contract was terminated.