Data, Information and Business Intelligence

It amazes me how often people use the words “data”, “information” and “Business Intelligence” as though they are all interchangeable. But to really escalate your database and data analysis skills to the highest level, it’s important to use the proper vocabulary.

Let’s start with the Data Maturity Lifecycle – the construct that describes the evolution of raw data (the lowest form of intellectual content) into business intelligence (the highest form of intellectual content). Data is a single characteristic such as someone’s gender. Data refers to a single quality of the main actor in a record. So, if we are tracking the gender of students in our school, each record would represent a student, the student would be the main actor and the Gender field in that record would refer to only that student.

Information – in its simplest form – is aggregated data. Aggregations are mathematic or statistical manipulations that deliver a value representative of an entire population of actors. In our school data example, total counts of all boys and girls in a specific grade and/or the percentage of males versus females enrolled in the school is a perfect example of information. Where data tells us something about one actor, information tells us something about an entire group (or Array) of actors.

That leads us to Intelligence (Business, Actionable, Predictive, or any other kind). Intelligence is information compared to some baseline metric such that we learn something about the population in a context larger than the population itself. Consider if we were to calculate information regarding the percentage of males versus females in the freshman class of our school and compare it to the same information about other schools in our city. By comparing our information to another informative metric, we now know something about our school in the context of all schools in our town.

So why does this all matter? Consider the very name of what we build everyday… they are DATAbases. The name tells us a lot about how we build our data models. When designing your Ninox application solutions, keep in mind that the primary goal is to collect and store as much raw data as necessary. Then, taking advantage of the rich Ninox language, we can rely on Ninox to calculate and deliver information and Intelligence (and don’t forget to check out the Ninox Reporting tools here).

Now that we’ve covered the Data Maturity Lifecycle, make sure to tune into The Eye next week when Databasics will look at Data Quality and how we measure it.

For now… Happy New Year and Happy Databasing to you all. And if you want to see The Data Maturity Lifecycle in a slightly different light, click here to read our previous article covering it.