1. Organizations integrate every aspect of their enterprise systems.
  2. Data managers fail to identify customer data priorities. 
  3. A data system lacks contextual analytics.
  4. Going ahead with implementation only after the completion of a data model.
  5. Ignoring the long-term impact of unnecessary data. 

A Rookie Mistake

This is a guest post by Bill Inmon, an American computer scientist. Many industry leaders recognize him as the father of the data warehouse. Inmon authored the first book, held the first conference, and wrote the first magazine column on data warehousing. He currently focuses on developing the revolutionary technology known as textual ETL. 

The Concept of Rookie Mistakes 

In sports, a rookie mistake is an error made by someone who has just started playing the game. In football, it could be an offside penalty. In baseball, dropped fly balls may occur. In basketball, it might be a missed dunk shot.

There are all kinds of rookie mistakes. Once players become regulars, they don’t make rookie mistakes again.

The other day I attended a data modeling exercise. The data modelers were building a data model for the purpose of creating an enterprise vision, which structured organizational data and technology. I congratulated them on not just building a data model for the sake of building a data model. That was one rookie mistake that they deftly avoided.

Upon  Closer Inspection 

But then I looked at what they were doing. They were trying to include every data element of the corporation into the data model built for the purpose of integration. At the rate they were going, they would probably finish by the next millennium. They were NEVER going to complete the data model. They were trying to gather and compile EVERY scrap of data found in the corporation into their data model.

The problem was their interpretation of the contents of an enterprise model. Big rookie mistake! The truth of the matter is that the enterprise data model should only contain relevant data that corporations need to integrate across the company, and not every data that exists.

By including only the components required in data integration within the corporation, they could finish the model in a short and practical time frame.

Narrowing it Down

But how does the data modeler know what data needs to be included in the data model? The simple answer is, by asking the end-user what data they need. An experienced end-user analyst can tell you in a flash what is needed. It is as simple as that.

You might wonder,  “what if you don’t include an element of data that was needed in the model?” Simple answer – you go back, retrieve, and add the required data element to your data model.  It is worth noting that data models are not cast in concrete. Business circumstances change and organizational data models should adapt to the cadence of the business. Specifically, effective data models are not cast in concrete.

Of course, you should aim to create the best possible data model at the outset. But perfection is the enemy of progress (an old saying from General George Patton, who was known for his data models and tanks). If you get the model 95% complete, it is probably good enough to allow you to proceed to the implementation process and gain a competitive data advantage. 

Making Sense of Data 

When it comes to the world of data, a rookie mistake translates to collating every bit of information without considering their organizational purpose. Scattered and unstructured data do nothing for an organization, even if they come by the truckload. Modern machine learning (ML) advances let companies sort and categorize their data efficiently and accurately. 

So by running ML-based textual ETL technology, you can easily create a relational database from distributed data sources. You will never have to worry about swimming (or drowning) in worthless data that impede your business operations. 

Integrate.io and Textual Data 

Integrate.io is an ETL platform with reverse ETL capabilities. The software solution enables you to transfer unstructured data to textual or traditional data warehouses with ease. Functioning as an ultra-fast CDC platform, Integrate.io offers specific features that will accelerate and refine the data management for your e-commerce business. 

Schedule a call for a 7-day trial with Integrate.io and experience the transformative results for your online businesses today!

About the Author

Bill Inmon the father of the data warehouse has authored 65 books and was named by Computerworld as one of the ten most influential people in the history of computing. Bill’s company – Forest Rim technology is a Castle Rock, Colorado company. Bill Inmon and Forest Rim Technology provide a service to companies in helping companies hear the voice of their customer. See more at www.forestrimtech.com.