Data Modelling Tips From The Pros

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August 5, 2021 Programming

As the Internet becomes more integrated with business, data that was never available for decision making is now being generated by social media and e-commerce. The connections between business and data will only become stronger as these technologies develop and others are invented.

This is why it makes sense to invest in appropriate data modelling. Data modelling is essentially understanding:

What type of data your business generates

How is this data connected

Finding appropriate ways of storing, retrieving and visualising this data

What coding or programming languages would be necessary to create a database for the data

Creating the best data model to do all of this for your business requires that you take certain steps. Here are some pro tips that outline the steps you can take.

Creating the best data model for your business

1. Understand that you may need a less traditional data model

Traditional data models may not work with the data you are collecting now. That’s because more traditional data modelling could only express relatively limited relationships between data sets. Data collected from the sources mentioned in the introduction are more complex, requiring data models that can express the complexity of those relationships between the data sets.

One type of data the traditional relational models will not be able to handle is “big data”. Big data gets its name from how voluminous the amount of data is as well as the speed with which it arrives. If your company’s activities produce this type of data, trying to impose a relational data model on it will fail and the resulting database will be ineffectual.

2. Build a data model that will perform well for everyone

Many people will be involved in the building of a data model and many people will use the database that eventually results from it. Therefore, the data modeling techniques used should be simplified for non technical people. The people contributing the data sets that will make up the model and the eventual end users are likely to be non technical people.

One of the ways the complexity of a data model can be reduced is to consider this. The people who will use the data model will also use it to get relevant business results. No matter how technically sophisticated the model, it is only as useful as the business intelligence company employees can get out of it.

3. Reduce the number of “many to many” relationships in your data model

Relationships between data in a model traditionally are represented as one to one, one to many and many to many. In a one to one relationship, there is only one connection between the data sets. For example, a student may only have one History teacher or English teacher at a school. However, that History or English teacher will have many students in their class. That is a one to many relationship. One piece of data may be connected to many others.

A many to many relationship is where there are many relationships between the pieces of data. For example, in a school there are students and there are classes. There will be many connections between these two pieces of data as many students have classes and many classes have students.

You will want to avoid many to many relationships in your data model as they make them unnecessarily complex, which could be confusing to a non technical user.

4. Build a data model that can return predictable results

Your end users want data that can help them solve problems that occur in the operation of the business. To do that they need a data model that will return consistent and predictable results when they attempt to access and cross reference data.

Therefore, anything that will create data inconsistencies and anomalies during the process of building the data should be avoided. For example, creating multiple ways of collecting the same information will create data redundancies. This will result in poor results when people attempt to search the database for the information they need.

5. Regularly update the data model

Business needs will change over time. That means the type of data the model will need to produce will change over time as well. The data in the model will need to be updated to reflect the changes. The relationships between the various data sets may also need to be rethought.

Failure to update the data sets or the parameters under which relate to each other can result in the data inconsistencies and anomalies spoken about above when users attempt to enter queries to search for data results.

6. Ensure the data in the model is of high quality

In computer science, there is a saying, Garbage In, Garbage Out. The same can apply for data models. Businesses generate a wide array of data, but not all the data generated needs to be in the data model.

High quality data is that which is necessary for getting the business the type of search results it needs to find solutions for its business problems. Including information that does not need to be in the model degrades the overall quality of the data.

7. Scalability must be built into the data model

When someone says a data model is scalable, they mean the model can handle an increase in the volume of data being inputted, as well as an increase in the number of users and maintain the same level of performance.

Creating a data model that is scalable takes into account the operating system the eventual database will run in, the hardware the database software is installed on and the design of the database itself.

If the operational system was designed to handle relational data, but the business grows to include “big data” sources, then such a data model will not be easily scalable. Similarly, if the hardware specifications of the machine running the database cannot handle the increase either in data or users, then the data model will not be easily scalable.

Going forward, data is going to be a crucial element in making business decisions. Using these tips to set up your data model will ensure it can handle the demands made upon it.