Tag Archives: data cleansing
Gartner recently released their 2011 Magic Quadrant for Data Quality Tools and I’m happy to announce that Informatica is positioned in the Leaders’ quadrant. We believe our position is a testament to the fact that customers like Station Casinos and U.S. Xpress continue to turn to Informatica to solve their most critical data quality challenges.
The publishing of the Magic Quadrant is often a great opportunity to reflect on the state of the data quality market. It should come as no surprise that data quality as a business imperative isn’t going away any time soon. We are continuing to see customers looking for help and expertise in solving a wide range of data quality problems, largely associated with data governance initiatives, master data management (MDM), business intelligence and application modernization. And the association of data quality in these areas is only getting stronger. (more…)
In my last post, I introduced the notion that the persistence and/or use of location data as attribution is becoming ubiquitous. Whether we look at the stand-alone GPS devices that decorate the array of windshields across the parking lot or the capabilities typically embedded within our mobile telephones, precise location is, to some extent, overtaking the concept of “address.”
And perhaps there is some value to increasing the use of dimensional coordinates in lieu of an address. As I commented in my previous post, there are still many situations where errors or flaws in location information lead to business impacts. A common example involves marketing or sales mailings – incorrect delivery addresses increase costs when mailings or catalogs are sent to nondeliverable or incorrect addresses. (more…)
The current hot topic in the MDM space is multidomain master data management. And rightly so, as multidomain MDM has the potential to drive far more value for companies than limited single-domain MDM initiatives (aka CDI, PIM) that focus on a specific class of data such as customer or product.
We’ve heard interesting discussions that multidomain MDM is just about storing the multiple domains within the data model. That is a major misinterpretation. While it’s certainly true that you need to have a data model that’s flexible enough to accommodate multiple data domains (e.g. product, customer, supplier), the data model itself is not the be-all and end-all of multidomain MDM. It’s a requisite starting point, sure, but you need to be able to do so much more. For instance, the ability to match and merge data across various domains is extremely important. Same goes for data cleansing.
Think of it this way: you’re using a dedicated PIM system. It does a great job of matching data fields in ways that are very valuable in addressing problems with product-centric aspects of your business: supply chain, inventory management, etc. Can this system do a good job matching and merging data fields from multiple domains? Can it provide the kind of data cleansing capabilities you’d need if you wanted to incorporate customer data?
A true multidomain MDM hub will provide out-of-the-box capability to:
- model any data domains
- cleanse, correct, standardize, and enrich all types of data
- match the different types of data and merge them into a single source of truth
- relate across the different types data: customer-to-product, vendor-to-material, contact-to-organization, employee-to-location, etc.
To top it all, the data governance application should support the creation, consumption, management, and monitoring of all these types of data.
So to realize the promised value of multidomain MDM, you’ll need a proven multidomain MDM hub and a data governance application that supports all these capabilities.