You’ve probably heard about the “Information Age” or the “Digital Age.” All of these buzzwords are essentially talking about the same thing, but it’s already become commonplace and a thing of the past. The new age we are in now is bringing about different ways for technology to engage with data, and data modeling is one of them.
Today, data has reached stratospheric levels that are shocking even the most seasoned of data scientists. While data is a valuable asset for companies, it can also be their downfall.
If you don’t know how to refine and manipulate your data, you will be doomed to lose your most valuable asset: the customer.
Dealing with a large volume of data can be highly complex. That’s where data modeling comes into play.
Data Science and Data Modeling
In simple terms, data modeling automates data analysis to provide practical solutions for the user. This is a fundamental process within Data Science.
Having professionals trained to handle data within your company is essential. A Data Science-based strategy adds necessary value.
One of the aspects of Data Science is to extract useful information from data sets, finding patterns to map results. Data modeling makes it possible to:
- Better understand the market – as a whole and within your company’s sector
- Understand future customer needs
- Calculate risks and potential business opportunities
All this is done from historical databases and in real-time (past and present) to answer what is likely to happen in the future. Let’s better understand how data modeling works and how it can help your business.
What is data modeling?
Dealing with a large volume of data can be a complex and laborious task. To facilitate this, it’s necessary to have a team of competent specialists, a reliable database, and a tool suitable for your business objectives.
There’s no point in collecting strategic data just to put it on a platform or software haphazardly. If data isn’t refined and modeled, it will likely compromise your results.
Whether simple or complex, correct data modeling is essential to make your application more robust and efficient.
Data models are tools that allow you to determine how data structures will be built, how the data will be organized, and the relationships you intend to establish between them. Database normalization can help in this endeavor.
How data modeling works
1. Requirement analysis
The first step takes place between the modeler and the end-user. These two players need to be in direct contact so that the former can understand the existing business rules, as well as how data must be stored and retrieved.
2. Setting Rules
This step is critical. This is where all your rules and demands will be cataloged and documented to be used in later modeling.
A good tip is to involve all employees who are or will be part of the automation process. They will be able to help with crucial inputs that can assist in modeling.
3. The Conceptual Model
At this point, the structure that will make up the entire database system begins to take shape.
To create this design, developers should graphically describe all the actions that will be performed as well as who will be involved in each step.
4. Choice of tool
It’s essential to have a tool suitable to your company’s business objectives. This will directly impact the type of collection your tool needs to perform and the data modeling that will be developed.
5. Creation of the Physical Model
“You have arrived at your destination!”. This is the last step: creating the database, the physical model that will receive all the data generated and collected. This is the physical version of the conceptual storage system.
5. The Grand Finale
After all the rules have been raised and established, it’s time for technology to shine. At this point, implementation finally takes place in a database-specific language.
Data is increasingly becoming companies’ most valuable asset. To extract useful information from this data, you need direction, investment in infrastructure, and trained professionals.
But incorporating data modeling into your business is nowhere near the final stage of your journey. Models need to be continuously updated and improved, and employees need to actually use data in their daily tasks.
The next goal you should set your sights on is developing a data-driven culture within your organization. If you’re curious about that, why not take a look at some of our other material on the subject?
The importance of data modeling for digital transformation
Digital Transformation is an unstoppable force within the business world. It occurs regardless of corporate will, a true force of nature.
In structural terms, digital transformation is defined as the constant development of digital technologies that shape our economy and society in the long term.
This creates new habits and needs in private life and business. Among these transformations is Big Data.
As we’ve discussed several times in our other articles, Big Data has become a powerful weapon within corporate competition. But nothing is as simple as it sounds!
To dominate Big Data, professionals and companies need to invest in developing new skills. Data modeling is one of them, allowing businesses of any size to handle a wide range of data with varying volume, complexity, and value.
If you want to know more about how data-driven culture can help you get the most out of your data modeling or are just looking for a second opinion on your business challenges, why not get in touch with one of our experts? Remember, you don’t have to go it alone.