Filed under: Business Intelligence, Data Analysis, Data Governance, Data Profiling, Data Quality, Master Data, Metadata
We are pleased to announce that our teaming partner CORMAC has been awarded the Data Management (DM) five year IDIQ contract by Centers for Medicare and Medicaid Services (CMS), an agency under Health and Human Services (HHS). As a teaming partner, Knowledge Integrity will work with CORMAC in support of data administration task orders. The total contract value is $350 million.
The purpose of this contract is to provide the following services for CMS:
1. Data Administration
2. Database Administration
3. Middleware and Message Queuing Administration
4. Business Intelligence (BI) and Extract-Transformation-Load (ETL) Tool Administration and Production Support
5. Data and Information Products Production Support
6. BI Tool, Data and Information Products, and BI-Related ETL Build and Deploy Services
7. Training/User Support
8. EDG Legacy Application and Database Maintenance Services
9. IDR ETL Development, View Development and Database Administration
I have been participating in a series of events sponsored by DataFlux on strategies for long-term success for enterprise master data management projects. We are about halfway through the series, and so far I have noticed two common threads among the questions posed by the attendees. The first thread involves justifying the value of MDM knowing that there is significant upfront effort that might not lead to the commonly-noted benefits. The second is about herding the business managers together to have them discuss (and hopefully agree) about the impacts of replicated records and inconsistent semantics.
Filed under: Business Impacts, Data Quality, Master Data, Metadata
A few months back I shared a post about proper scoping of a master data management activity to focus specifically on a smaller subset of business activities that (a) are impacted by the absence of a unified view of data or (b) can be measurably improved through the facilitation of a unified view of data. Part of the actualization of that unified view involves selecting the right business activities and then getting a better understanding of the data needs of those business activities.
Filed under: Business Intelligence, Data Analysis, Master Data, Metadata, Performance Measures
I recently updated a white paper I did for IBM called “The Analytics Revolution – Optimizing Reporting and Analytics to Make Actionable Intelligence Pervasive.” Click here to download this revised masterpiece.
Filed under: Business Impacts, Data Quality, Identity Resolution, Master Data
Yesterday I shared some thoughts about the differences between data validity and data correctness, and why validity is a good start but ultimately is not the right measure for quality. Today I am still ruminating about what data correctness or accuracy really means.
For example, I have been thinking for a long time about the existence (or more accurately, nonexistence) of benchmarks for data quality methods and tools, especially when it comes to data accuracy. On the one hand, I often see both vendors and their customers reporting “accuracy percentages” (e.g. “our customer data is 99% accurate”) and I wonder what is meant by accuracy and how those percentages are both calculated and verified.