One of the things we see all too often is that the data stream of a project is left to the last moment. This causes all sorts of problems – when people are busy and stressed trying to do system testing they find they have no REAL data to test with. They get a few records of made up data that bears no resemblance to the actual data users will see in the production system – and means nothing to them in the context of their legacy system.

I show horses and in the horse show world you know that practice makes perfect. So why don’t more company’s train their staff to practice safe and good data habits. During show season I practice my horse just about every day of the week in preparation for shows on the weekend. It literally becomes a habit and I do get withdrawals when I don’t ride.

Isn't it funny how you get so used to something that's negative or ineffective that you simply don't notice anymore how annoying or useless it is? Unfortunately for most of us, this applies to the data at our companies that we try to use every day. Like it or not, data is at the cornerstone of everything we do. Data is the engine that drives our business success. We use data to help us justify our jobs, track metrics, communicate with our customers, vendors, and partners, raise or lower prices, and even to choose what we want to communicate, and when we should. (Yes, we looked at our data to decide when to post to this blog!)

Think about your next performance review – you want some decent metrics to show your bosses what a phenomenal job you've been doing, but can you really rely on the information at your fingertips to prove how fabulous you are?

Data rules rule! You cannot manage what you cannot measure. You cannot control what you cannot manage. If you have no framework for measurement (i.e., rules) you are out of control. Business fails. Pure anarchy.

Data rules, business rules … like rules of physics … they already exist, they’re everywhere – you just have to learn them, discover them. We don’t invent them. In the real world, we cannot escape the physical rules that operate – as we process through our daily lives. Some rules of physics are harder to understand than others. F=MA much easier than E=MC**2. The apple falls from a tree. Photons become energy at the speed of light and act both like particles and waves – who knew?

Users don't like the new system - why? Because they see the same information they had in the old system – but in a new format.

How many companies have spent millions of dollars on a new ERP system, then added a few more for a BI system, then added Balanced Score Cards and very pretty dashboards, then the CEO still gets reports from an Excel spreadsheet. Ever wonder why? There are many reasons; however one we find common is that when the new shiny ERP system was implemented no one cleaned the data from the old system(s) BEFORE it was loaded into the new system.

“Garbage In – Garbage Out!” I know we have all heard and used this term countless number of times! But are we really able to put the true meaning and implication of the phrase across to various clients/customers that we work with on various ERP implementation projects?

More commonly termed as GIGO, yes, “garbage in – garbage out” is what the result will equate to, if the quality of data is not good. Quite often, on projects we start off on a good note by executing detailed data profiling, generating reports on data quality for the customers. But during the course of a project, the data cleansing activities are not prioritized enough.

Crude oil and enterprise data share one common fact in that they are both intrinsically worthless. Crude oil on its own is a just a barrel of nasty gunk that really has no value or purpose unless it gets refined. It is the refining process that unlocks significant value as it yields many types of fuel and also several base components used in the chemical industry. It’s what you can do with crude oil that creates such value and power in today’s economy. Enterprise data shares this similar property to crude oil as the real value of data is created when it is processed with business applications to support operations, decision making and reporting within an organization. The organizations that can best process their enterprise data effectively with innovative applications will create value for their stakeholders and to the economy as a whole.

It’s a funny thing. A data blogger made a bold statement that there are no data rules, rather he made the argument that data exists in an enterprise to support the “business functions”** of that enterprise. Data rules, then were not required – only business rules were relevant.

It may sound like we are contradicting ourselves when we say we do, in fact, subscribe to his thinking regarding business process (or function) execution. But in our opinion, data without rules is like a house without a solid foundation. And in this dimension, apparently we cannot agree.