When I first entered into the world of data modeling, after several years of being a programmer and computer operator, there was a paradigm shift required to embrace the concepts behind structuring data to minimize the impact of change.  The adoption of disciplined approaches to the structuring of data, including the use of third normal […]

I am fascinated by the wide variety of database solutions in place across all industries that are referred to as data warehouses. It seems that any time data is placed in a database, whether from one source system or from multiple sources, it can be referred to as a data warehouse. In healthcare in particular, the concept of a replicated data store is a common occurrence (unfortunately, few actually call it this) – this is probably due to the complexity and depth of healthcare data in combination with the massive number of source systems. These complexities make true integration of data a significant challenge and providing a Replicated Data Store on one platform at least provides a one stop shop. Replicated data stores can be invaluable for any number of purposes, but they should definitely not be confused with a data warehouse. In this article we are going to investigate some of the characteristics and values of each of these architectural approaches.

The term “Intellectual Capital” collectively refers to all resources that determine the value of an organization, and the competitiveness of an enterprise. Understandably, the term “intellectual capital” from a human resources perspective is not easily translatable into financial terms. For all other assets of a company, there exists standard criteria for expressing their value. Perhaps, this term could more appropriately termed a “non-financial asset.” In an article written by Paolo Magrassi titled “Taxonomy of Intellectual Capital”, 2002, Mr. Magrassi defines human capital as “the knowledge and competencies residing with the company’s employees” and defines organizational intellectual capital as “the collective know-how, even beyond the capabilities of individual employees, that contributes to an organization.”

This is the third in a series of articles on the Semantic Web and EIM. Click here for the second installment. In this article I will discuss how the Semantic Web and semantic technologies can be used for meta data management and meta data analysis.

Whenever you are embarking on a lengthy adventure to a location you haven’t been, it always starts with some high level planning. When planning my vacations, once I have an idea of where I want to go and whether it is feasible, I typically start planning the various approaches to travel, the high level events I am excited about, and then factor in costs/budgets. This exercise usually starts with 15 or 20 things on a list and ends up with about 6 – 8. Those 6 or 8 are the events I can actually attend.

Many organizations have begun to explore the need for an enterprise data management strategy to leverage the value of the data and information they collect, store, use and propagate. In addition, executives have started to see the increased compliance and regulatory requirements as a call for more (or better understood) data within the organization.

Over the past five-ten years, most business leaders have seen that data is a critical asset, but they have struggled to implement effective management of this asset; valuation of it has been even more elusive. Without oversight and valuation, it becomes extremely difficult for an organization to use their data to make informed decisions. Data that does not conform to any standards of quality, consistency or sharability is not valuable, and decisions made based on ungoverned data can be problematic.

This column is being addressed to you, the analyst so let’s first define the role of an analyst. An analyst looks at data and produces information – hopefully actionable – running queries and reports, of ten using powerful and sophisticated tools such as Cognos, Business Objects, Microstrategy and SAS. The analyst can report to IT or report directly to a business sponsor.

Across healthcare, the demand and pressures for access to data are now overwhelming organizations. In many organizations, the only access to structured data for performing analysis was being conducted by the resources in Finance. More recently, the demands for providing external reporting of core measures and demonstration of meaningful use have inundated organizations while the financial pressures facing the world have forced organizations to look at business operations for process improvement. In organizations where research has been conducted, access to data has historically often been finding it any way you can, exposing security and privacy gaps.

In this first of a series of articles on the Semantic Web and EIM (of which, this is the first), I will explore how the Semantic Web and semantic technologies will impact the different components of EIM. In this article I will discuss how the Semantic Web and semantic technologies can help enable perhaps the most critical aspect of EIM: Data Governance and Stewardship.