By Elaine Hanley,
“I am always doing that which I cannot do, in order that I may learn how to do it.”
Taking on a data warehouse project is not for the fainthearted. It involves marrying the views of disparate parts of an organization, where not only are the business objectives driven by different needs, but the language spoken is at odds. A data warehouse project includes the daunting task of integrating the silos that exist in virtually every organization, nominally bringing together the information from multiple systems, and of addressing organizational misalignment and differences by trying to seek agreement in the overall approach towards measuring the business operations.
What tools can such organizations use to reduce the risk of failure? What can help them to accelerate their projects? How can they ensure that they are not paralyzed by indecision and the unknown?
IBM Industry Data Models can help:
- Learn from the experienced
Our approach to acquiring knowledge has always been to avoid starting from nothing, and we have turned to the experience or output of others, so that we can reuse and modify information to suit our own needs.
- Speak the same language
The ability to explain what is required when measuring the business and the translation of that specification into the systems and analytics that can help the organization to navigate through risks and spot opportunities relies on a communication system that interconnects different parts of the organization. When we speak of a “customer”, do we mean an active customer or a prospective customer, and is “client” the same thing?
- Build small, build iteratively
One of the biggest risks in any project is to be overambitious, but this risk can be mitigated by considering a cross-enterprise initiative such as a data warehouse. How can we deliver a focused set of analytics, while ensuring the solution will serve future needs.
“By three methods we may learn wisdom: First, by reflection, which is noblest; Second, by imitation, which is easiest; and third by experience, which is the bitterest.”
IBM Industry Models address these and other issues and challenges. IBM Industry Models encapsulate the experience gained in the Banking, Insurance, Healthcare, Telecommunications and Retail industries and translate this experience into template analytics and methodologies for defining and building data warehouses.
The models are the subject of a paper by McKnight Associates:
“IBM Industry Data Models can jump-start an organization towards a comprehensive analytics environment by applying proven best practices in data modeling to self-contained units of business functionality.”
For more information, go to https://www14.software.ibm.com/webapp/iwm/web/signup.do?source=sw-infomgt&S_PKG=ov28366
About Elaine Hanley,
Elaine is the Product Manager for Banking, Financial Markets and Insurance Industry Models, and has been involved in software and design for data warehousing at IBM for over 20 years. Elaine has worked in a variety of roles, including software development, consulting, project management, technical sales and product management. Elaine holds a BAI (Bachelor of Arts in Engineering) from Trinity College, Dublin and a Master’s degree in Computer Applications from Dublin City University. You can follow Elaine on Twitter at @ElaineHanley.