1. Define classes that act as descriptions on 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. Tryout your classes with data by creating objects that adheres 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 feedback new needs refine your classes and views
8. Iterate

Do all this with MDriven in a few hours or spend months on doing it the old fashioned way – by hand.

Doing this by hand

Step 1-3 is first done by business developers – then step 1-3 is 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 it is usually limited by budgets governed by project owners set by steering committees.

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

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

Doing this with MDriven

It is actually rather impressive that 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 and you can gain user acceptance and actually 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 open standard format UML. Having the specification in machine readable form – and having a machine that can read it and implement what it has read removes the need for traditional time consuming backend and frontend work.

Things that took a developer hours is done by the machine in milliseconds. The whole game changes when this kind of efficiency gains are available. We no longer have to think really 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 the business case.