Build Enterprise Information Systems
  1. Define classes that act as descriptions of what data there is.
  2. Add associations between classes to get descriptions of aggregated things.
  3. Add state machines to reflect the business rules of data change.
  4. Try out your classes with data by creating objects that adhere to the descriptions.
  5. Refine views of data to solve specific needs or use cases.
  6. Choose a modern technology for deploy and execution.
  7. As Users give feedback, new needs refine your classes and views.
  8. Iterate.

You can do all this with MDriven in a few hours or spend months doing it the old-fashioned way – by hand.

Doing This By Hand

Steps 1-3 are first done by business developers. Then steps 1-3 are implemented by backend developers and then again by database administrators.

Step 4 is probably only done with mockups and fantasy.

Step 5 is done by front-end developers, web designers, and usability experts.

Step 6 is done by a corporate Enterprise architecture function – or whatever the backend developers think is cool at the time.

Step 7 and 8 is the maintenance and governance process – and is usually limited by budgets governed by project owners set by steering committees.

All this is a great deal of work and often very time-consuming.

If the governance process is slow or limited by a strict budget, intended system users may give up and find another way that is less opportune for the company long term.

Doing This With MDriven

MDriven allows you to do all these things with a small team or on your own. You can often remove or reduce the need for a steering committee since the money involved is much less. The whole process is sped up. You can gain user acceptance and deliver on digitalization promises like never before.

Our open secret is that MDriven has implemented all the patterns and best practices needed in code. This code takes the requirements expressed in the available standard UML format. Having the specification in machine-readable form - and having a machine that can read and implement what it has read - removes the need for traditional time-consuming backend and front-end work.

A machine does in milliseconds things that take developers hours to do. The whole game changes when this kind of efficiency gain is available. You no longer have to think hard about a business case to get funds to implement something – not if it is cheaper to implement and try with real users than creating a business case.

This page was edited 5 days ago on 02/26/2024. What links here