As part of MIKE2.0, we believe we are presenting a unique perspective in the area of standards development. Our approach is to create a collaborative community for the development of standards for Information Management, including those that apply to Capital Markets.
Some interesting work around open source and open standards is developing in relation to market data:
- Market Data Definition Language (MDDL) is an extensible Markup Language (XML) derived specification, which facilitates the interchange of information about financial instruments used throughout the world’s markets. A community is build around MDDL, including a wiki-based development environment.
- FAST (FIX Adapted for Streaming data) protocol is emerging standard used for the distribution of market data. Some of the steps being discussed with the open sourcing of FAST could be seen be particularly beneficial to this standard as it continues to evolve.
With open content and collaborative technologies, it’s easy for these projects to work together and we’ve starting doing this through MIKE2.0 with references to these projects.
April 29th, 2008 at 11:13 pm
Many similarities exist between producing quality data and manufacturing quality products. Similarities such as quality measures, conformity to specifications, low defect rate, and improved user satisfaction.
If we go back to the development of the machine and the industrial mass production, we will find similarities to the advancement of the data processing power and the mass production of large volume of data (stored in data warehouses) that is widely distributed and easily accessed. Faced by the reality of advancing data processing power, several organizations are opted or forced to manage large volume of data.
In reality, the standards, specifications, the engineering methods, and the personnel with the know-how are not advancing in parallel. This situation led to chaos in the quality of data presented to the data consumers in specially in the business or government operation paradigm.
I would like to start a discussion on data quality within the context of enterprise application integration, which has played a big role to uncover data quality chaos.
April 30th, 2008 at 2:46 am
Salah,
Definitely agree - I think the concept of Data Profiling and Data Re-Engineering and in relation to the quanitative discovery of issues and systematically addressing them is a really key concept.
In terms of DQ in the context of EAI, you are right once again. When we try and bring this information together from distributed systems (designed indepently from one another and w/o requisite standards) the problems really come to the forefront.
We though that IM was fundamentally immature as lacked these engineering methods. Our goal is a community-based approach to developing these standards. You’ve summed up the drivers we’ve had quite in creating the MIKE2.0 approach quite well!
If you do a search on data quality from the main site, you’ll find a number of articles. You may want to drive some specific discussions on these articles’ talk pages.