In a recent meeting I attended, where corporations were learning from a vendor about information management, the vendor asked what the term "Information Management" meant to the participants. So, I took it upon myself to make a list of the terms that "Information Management" encompasses – but without any order significance (so get ready):
Metadata, Data Discovery, Data Profiling, Data Classification, Data Verification, Data Quality, Mapping / Movement, Integration / Canonical Form, Data Governance & Corresponding Stewardship, Analytics, Data Services, Web Data Services, Organization, Modeling, Architecture, Security, SDLC – Development Lifecycle, Data Lifecycle (Decommissioning, Archiving Purging), Change Management, Requirements / Traceability, Industry Standards (XML, …), Customization, Privacy (HIPPA), Compliance (SOX), Reference Data, Master Data, Redundancy Management, Data Migration, Third-Party Information Integration (i.e.: Mashups, list integration, address validation, …), Corporate Acquisitions Integration, Data Enrichment, Data Interchange (EDI).
This list kept getting longer the more I thought about it. No wonder "Information Management" (as a discipline) is now one of the number one priorities of CIOs!
Many organizations are now formalizing the discipline within the organization globally as a direct report to the CIO – bravo! I guess the corporations are learning the value of the Chief ‘Information’ Officer really means what the acronym implies!
One of the many ways to integrate information across the enterprise is known as Canonical Modeling – creating a standardized representation of information that the can be the integration point or source of shared information.
In a recent POC project for a financial services firm and working with a major healthcare organization, the primary goal of a short term project was to determine the canonical model that will support the integration of master data.
Whether the master data is "Customer," "Product," or "Vendor" – the first step is usually research into the source systems to learn about the various forms and types that the data is found in (let’s call this Discovery). Next is an understanding of the quality of the information contained within these systems being considered (lets call this Profiling). Next is the grouping of information into taxonomies (Global, Business, Industry, and other domains (let’s call this Classification). We would next move on to:
- Verification
- Data Quality Analysis (utilizing metrics)
- Data Cleansing (including de-duplication, …)
- Mapping (including lineage, data flow analysis, schema mapping, …)
- Movement (including canonical modeling, XML conversion, …)
- Integration (including matching and integration of assets, …)
- Data Governance & Stewardship
- And Finally – Analysis (visualization/metrics to maintain the new environment)
DAMA has created a great publication entitled "DMBOK – Data Management Body Of Knowledge" which encapsulated over 20 years of Information Management terms and practices used and is a must investment for those starting and information integration or "Information Management" program.
Managing (or should I say mastering) complex information environments takes both structured methodologies and toolsets to be successful. A statement I remember well from a previous employer was – "A fool with a tool is still a fool."
It is often a best practice not to ‘boil the ocean’, but to look at a few of the "key" systems and integrate these first.
Above is a starting point for those CIOs interested in getting a program like this off the ground. In my next blog I will talk to another very hot topic: "Data Services & Virtualization" along with addressing some of the many comments that have been added to this blog…
Thank you to everyone at Information Management and also to the IM community and keep the comments coming!
The two takeaways for me from this article for a successful MDM project are (1) importance of data governance for, and (2) the ability to start small and scale as business needs grow. On the first point, data governance is about people, process, and policies. And technology is an enabler. Here's an article on that - http://bit.ly/bNerPs. On the second point, you start small by identifying the most pressing business problem to solve, reap the ROI, and then expand the solution the broader enterprise, or scale to solve the next business problem.
Looking forward to your next blog on Data Services.