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Data, Information, Knowledge, Wisdom

February 20, 2012

In the context of Information Systems. Nothing more.

Such systems contain Data. Such data is usually stored in tabular format, in relational databases. A query language is needed to extract this data e.g. SQL.

This data is gathered from a variety of places. Actuals from Point Of Sales or Invoices, Budgets from spread-sheets, and Forecasts from even smaller spread-sheets. Some of these source systems can be completely trusted, others not. 

Each piece of Data e.g. Price=2.99 is of no use on its own. It needs to be used in conjunction with something else. But, it needs to be valid. Thoughts which might lead to Discussions which might then lead to Decisions (and hence Actions) are not much use when based on, effectively, rubbish (that is, inaccurate) data. Data that is even a few decimal points out of place.

Consider the effect of storing exchange rates using the MONEY datatype instead of DECIMAL (19,6). 4 decimal places do not provide enough accuracy, even though it uses less space.  “Validation” plays a key part in the information gathering exercise. In other words, the absence of validation processes should be questioned. This includes “in-built validation”, such as, Referential Integrity constraints. 

Whenever you write a piece of code that gets data from somewhere else – consider whether it should be validated in some way. Place “unknown” data in clearly labelled buckets, which can then be correctly cleansed in due course.

Such systems grow to contain Information.  They combine several pieces of data (e.g. volume, price of a product in a location) to generate useful information (e.g. sales, margins, net profit). Taken together, such systems provide “big picture” information i.e. total sales of all products in all locations in each month over the past n years. Data that is analysed by using many dimensions requires a special kind of data storage – a multi-dimensional (aka “OLAP”) database.

Where the data volumes are small, it becomes possible to apply modelling functionality. “What-if” questions can be asked, with an almost immediate response. Modelling adds a lot of value to the data but it is still only information.

But, OLAP databases have their limitations. Once you get lots of dimensions and data it becomes more difficult to make good use of all of it. You need a lot of analysts constantly looking in different parts of your vast database. Databases that are many terabytes in size are very useful, but only with the appropriate tools. 

Data Mining tools could be said to be “knowledgeable“. They can be used in the automated extraction of relevant information from multi-terabyte databases. Book purchasing is a good example.

You buy a book. You are then informed on a regular basis about which similar books to buy. Up to a point. When you stop buying books after a time then you stop getting this unsolicited, though useful, advice. Presumably, if you value this advice then you need to buy another (similar) book. Was this intended?! Probably not.

In simple terms, Wisdom can be defined as making the best use of knowledge. I suppose Data Mining applications may be considered to be making best use of the knowledge available, albeit in a one-dimensional space.

The problem with “making best use knowledge” is that knowledge consists of discrete and disparate parts and it is not always possible to make the connections. The human brain usually makes a good fist of making the connections but its success largely depends on whether it possesses the knowledge in the first place. So, the key (but not sole) issue becomes making the best knowledge available.

Consider your medical history. But, not just your medical history – also your immediate family, your cousins and even the medical history of your ancestors. It is likely that some of the medical problems you encounter as you go through life would have been experienced by the others. You (and your physicians) could save a lot of time, expense and possibly suffering if you had easy access to that information.

Illnesses which are rare are more difficult to diagnose. But even these illnesses will have been researched in the past. Knowledge about them will exist in various medical journals as well as deep in the bowels of the great schools of medicine. Alongside this, almost in a parallel world, the giant pharmaceutical companies will also possess raw data, facts and information about these rare illnesses and the possible life-improving treatment thereof. And, finally, nutrition – that also plays an important part in our good health. Your 5-A-Day can be met by decent portions of veg and good smoothies!

Obviously, the Internet allows you to bring a lot of the above together. Providing you ask the right questions via the input of the correct keywords. The problem is that even if you did do that, some of that information may not be visible to you.

Organisations value and protect their data. And so they should. Their information systems contain valuable information, which has been gathered in vast quantities; then cleansed, transformed and summarised; safely stored; all of which has been achieved at great cost. But which gives them a competitive edge. (Needless to say, information systems on their own are useless without good people, ideas, products etc.) In short, they are unlikely to share it, even if they are “public bodies”. Unless it is in their interests to do so.

Vast “databases” already exist – data warehouses, social networking sites and medical records. Technology is not the constraint (limitation) anymore.  If permitted, these databases can be “searched constructively” with the Search Engines of today.

Wisdom is needed to create a “network” of “useful” databases, which can be “searched”, to make best use of knowledge. For each organisation’s own “selfish” interests. For the greater common good.

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