MDM Summit – Fall 2008 will take place on October 19 - 21 at the Hilton New York.

This year’s summit will feature case studies from companies like American Standard, Bank of America, Cisco, EMC, Ericsson, Starbucks, Vertex etc. and provide special focus on data governance and product information management.

The event organizers promise that the summit will provide best practice evaluation and implementation strategies from early adopters of customer data integration (CDI) and master data management (MDM) solutions and give the attendees a chance to interact with the CDI and MDM industry experts.

You can get all the necessary registration info over here.

Data federation and data integration. What’s the difference between the two?

I understand data federation as something that joins data from different sources distributed around the company without actually moving it from the original source. That is to say, data federation software creates a single repository that doesn’t contain the data itself, rather its metadata (information about the actual data/its location). This technology allows users to have a single standardized view of data displayed in a single data layer without having to deal with the variety of original data sources.

James Kobielus in his ZDNet blog explores the core difference between the enterprise data warehousing (EDW) and data federation.

Data federation generally seems outdated, compared to data warehousing, which at first looks like a more reasonable approach:

Federated environments are not optimized for heavy-hitting data matching, merging, transformation and cleansing, all of which are essential functions to deliver a “single version of the truth” for business intelligence (BI).

However, James also lists the benefits data federation may deliver in the company’s overall Business Intelligence strategy:

Data federation is an umbrella term for a wide range of operational BI topologies that provide decentralized, on-demand alternatives to the centralized, batch-oriented architectures characteristic of traditional EDW environments.

In the real world, data federation and EDW are not that mutually exclusive, and may very well target different markets, as data federation is better suited to near-real-time BI requirements than the batch-oriented EDWs deployed in many organizations.

So, where does data integration fit in the picture? Certain aspects of data integration intersect with both of the technologies discussed above. On the one hand, data integration may very well involve copying and moving data around, which is contrary to the definition of data federation, yet fits very well into the concept of data warehousing. On the other hand, data federation is in many aspects only a single instance of data integration in that the metadata it uses can be employed in the integration processes.

Last week Gartner published its Magic Quadrant for Data Integration Tools that you can access for free over here. I’m glad to see Apatar mentioned, although I wouldn’t quite call it a data federation tool.

As much as companies are talking of committing to Business Intelligence principles in their daily work, the concept of BI still seems too utopian and vague to be successfully implemented throughout an enterprise. It’s probably not that the definition is vague, it’s that the practical side will differ a bit depending on companies’ needs.

The one thing that is more or less universal and requires the utmost attention in all cases is data quality.

Whether your data is already ‘dirty’ and needs to be reviewed on regular basis, or whether there is no systematic process for checking it within your company, sooner or later you realize that something about your data needs to be fixed. Some companies prefer to conduct regular automatic check ups, others choose to apply filtering techniques before the information even enters internal databases, one way or another, enough solutions already exist to help you make that first step into the world of BI and make it right.

One of BEYE bloggers recently posted his list of the top ten things BI lacks. Aside from data quality he singles out such foundational aspects of BI as the problem of structured and unstructured data, valuation techniques, Predictive Analytics / Data Mining, technology limitations, simulations, on demand analytics, etc. The the list will vary slightly from one company to another, but making one and working towards perfecting your Business Intelligence strategy through it is certailny helpful. No one can tell you how rewarding it is, you can only feel it for yourself while gradually putting “taken care of” or “implemented” next to each item from the list.

Salesforce.com received two 2008 CRM Market Awards, for Midmarket Suite CRM and Sales Force Automation from CRM magazine. The company also lead the Enterprise Suite CRM and Small Business Suite CRM nominations.

The awards were presented at this month’s destinationCRM 2008 Conference in NY.

But that’s not all good news for Salesforce.com. Not too long ago the company was placed on Standard & Poor’s 500 index and became the first Software-as-a-Service company to enter that listing.

“The decision to add salesforce.com to the S&P 500 is a clear sign that cloud computing has arrived,” said Marc Benioff, chairman and CEO at salesforce.com. “Every day, more than 47,000 customers rely on salesforce.com to help run their businesses. Now that incredible record of success in cloud computing has a new home — the S&P 500.”

Although the adoption of Web-based CRM solutions is only increasing, the companies still face the same challenges they had when the era of on-demand only started. That is integration, security and support.

Here’s an article that takes a more detailed look at each of these challenges and provides tips on how to effectively manage them.

I recently came across Joe McKendrick’s blog post, where he talks about the phenomenon of mashups and discusses the problem of mashups not quite being there yet in terms of end-user experience. Well, a lot of mashups are designed specifically for corporate users to help solve real-world business problems. Lots of work stations, lots of issues…at this level it’s no wonder you’ll need a power user at some point.

Now, while that’s OK for the corporate world, this might be a problem when a user is trying to build his own, and more complicated application. The whole point of mashups is ease of use, your average user wouldn’t want to get too technical into the process when building an application for personal use.

Joe refers to Mozilla Foundation with their “Ubiquity” project, as an expample of a company trying to make its product easily accessible to any non-programmer out there. I quite like the idea they’re pushing through with Ubiquity, that of connecting the Web with language, thus having everything you might need at your disposal and not wasting time. Ugh, time! The one thing we’re always short of. =)

All the while it seemed like 2008 just started…until today, when I looked through Salesforce.com’s official blog. It turns out the salesforce.com Winter ‘09 release is just around the corner!

In a week - Sandbox Upgrade Window will start on September 16th, 2008 and last through October 11th, 2008.

See the official Salesforce blog to find out how you can participate in the Winter ‘09 Sandbox Preview.

A question was posed to a SearchDataManagement.com expert as to what metrics should be used for a data warehousing project.

The expert (William McKnight from Lucidity Consulting) recommended the following three as most valuable:

# Business return on investment (ROI) - Are you getting the bottom line success with your project?
# Data usage - Is your data used as intended by the users?
# Data gathering and availability - Is your data available to the extent it should be?

He also mentioned up time, cycle end times, successful loads and clean data levels as secondary technical metrics to pay attention to.

In short, you want to eliminate intolerable defects – as defined by the data stewards. These defects come in 10 different categories: referential integrity, uniqueness/deduplication, cardinality, subtype/supertype constructs, value domains/bounds, formatting errors, contingency conditions, calculations, correctness and conformance to “clean” set of values.