This is an introductory week to the comp 602 and so I have been getting my feet wet on the course. I’ve been to other professor Dron’s class before, comp 650 and so I am already familiar with the course format, the landing site, and the social collaborative nature the course delivery method. I had enjoyed comp 650 so I really looked forward to this course.

Luckily for me database, DBMS, data model, etc. isn’t a foreign concept to me. I have mainly dealt with database systems in larger part of my career being a software developer who often had to analyse, design, implement and support systems that manage relational dataset. In my experience, it had always been assigned with the responsibility as a developer/analyst to perform data modeling and is the one who produce both the logical and physical ERDs and not the DBA. I think this dovetail really well with developer’s skillset and the toolkit which allows an integrated experience of solution implementation. Many modern IDE allows plugins to extend its functionalities to incorporate db management.

Having mostly done work on MS SQL Server and Oracle in my career, I am now exposed to yet another option in the world of DBMS – PostgreSQL. The ProstgreSQL installation went well on my primary desktop machine with enough resources to support the course work in the coming weeks.
Most of this week involves a lot of readings in topic related to DIKW (Data, Information, Knowledge and Wisdom). I think this is great starting point into understanding the underlying reasons of database management existence in organizations. Why do we need such systems and why are they important? The incremental transformation from data to information, information to knowledge, knowledge to wisdom had the same level of distinction in the IT systems we use and rely on. For example, we have systems that collect data (raw facts), process them into structured and formatted data called information which is the precursor to knowledge. From information we form knowledge and use systems such as DSS (Decision Support Systems) to support discussion makings. Ultimately we reached the layer of wisdom under the DIKW hierarchy with a predictive component supported by Expert Systems.

https://landing.athabascau.ca/pg/bookmarks/read/118111/week-1-task-3-the-dikw-hierarchy

We need systems like the ones mentioned to aid decision making process in many disciplinaries as well industries. Because we need to filter out the excess of data to make sense of what we need to do and therefore databases form the building blocks for the higher level functions.

Building systems that reliably behave the way we intended might not be an easy task. Evidently shown in our shared experiences https://landing.athabascau.ca/pg/pages/view/114230/when-the-computer-says-no How many of these are design mistakes, program bugs or simply misuses? Perhaps the data is incorrect to start with that undermine the rest of the system? How many times I was told “This is a data problem, bad data in that table”. Is there such thing as bad data or should be called bad information given the definition we learnt this week? If data is raw fact it can’t be bad? If it is in the table it would have to be information because the data have been organized and normalized.

Dickson