Posts Tagged ‘MDM’

In today’s business world, the major challenge that enterprises face is to have one source of business data (Master Data) which is non-redundant, more consistent, accurate, complete and error free. Master data of an enterprise could be information about its customers, products, employees, suppliers, business locations, etc. Master data plays an important role in making […]

Picking up from my earlier blog post, there are two kinds of MDM tool types, one targets specific domain (Customer and Product are the most common ones) and the others follow a multi-domain (Customer, Product, Location, Supplier etc. all in one) strategy. Most of the analysis I found are either for Customer Domain or Product […]

Today, Master Data Management (MDM)  is an accepted mainstream tool / program / application within corporate America. Large companies have multiple MDM solutions across business units, geographical units and even within the same business unit, they have different MDM for different domains. It is becoming a common sight to see MDM fully functioning and operating, […]

Last year I was involved in a Data Governance initiative where product vendors were hitting the client with Multi-domain MDM solution. Vendors were offering the software at various price points and simply put confusing the client. The fact of the matter is the client did have a huge MDM problem to solve. Their issue is […]

In my last blog post a few months back I introduced some key concepts of data governance and the necessity of healthcare organizations to establish a data governance program as a way to perform quality control on its data. We touched on the 10 universal components of data governance that the Data Governance Institute (DGI) […]

The post is the 2nd in the series which covers capabilities, use cases and product offerings for change data capture technology. The critical capabilities which are expected in a CDC tool Selective data replication and synchronization is a capability to synchronize data between multiple databases. It usually supports high volume and mission critical scenarios. An […]

Trying to stay ahead of the curve when it comes to IT issues is a challenging task. Emerging technology forces in the financial services industry are already impacting business. The convergence of these forces does present challenges; however, it also provides a window of opportunity for financial institutions to elevate business performance and gain a […]

Yesterday, we heard from data governance expert, Ben Leeson, about why financial services companies should be using a master data management (MDM) solution. In case you missed it, you can read part one of the MDM Q&A here. Today we’ll be talking more about the value of MDM in banking IT and how to overcome the elusive […]

Today, we have a Q&A guest post with one of Perficient’s Data Governance Architects, Ben Leeson. Ben has extensive experience working on master data and governance projects for today’s leading financial services companies.  He will also be a part of our  upcoming financial services webinar, “Creating a ‘Customer 360’ with MDM in Financial Services” where […]

Technology has challenged the financial services industry and will continue to play a vital role in supporting business growth and change.  With change comes new challenges for financial institutions.  Mobile, social and analytics strategies give the promise of improved customer service, reduced costs, effective marketing and banking innovation.  However, these strategies require a trusted view of their data – something […]

Today we are going to look at a widely used acronym in business intelligence called MDM, or Master Data Management. Sure you have heard it before, but it is often tossed in with a series of other acronyms without much context. So what is MDM in a practical context?

Teradata has been long been known for its powerful data systems and drive to push benchmarks for large data volumes. In fact, in 1992 Teradata built a first of its kind system for Wal-Mart, capable of  handling 1 terabyte of data. One of the main advantages to routinely working with very large data sizes is the exposure to integration, data […]