It’s safe to say that we live in the era of big data. Collecting, storing, and analyzing information has become a top priority for organizations, which means that companies are building and utilizing databases to handle all that data. In the ongoing effort to use big data, you may have come across the term “data normalization.” Understanding this term and knowing why it is so important to today's business operations can give a company a real advantage as they go further in-depth with big data in the future.
What is Data Normalization?
So what is normalized data in the first place? A data normalization definition isn’t hard to find, but settling on a specific one can be a bit tricky. Essentially, data normalization is a type of process wherein data within a database is reorganized in such a way so that users can properly utilize that database for further queries and analysis.
There are some goals in mind when undertaking the data normalization process. The first one is to get rid of any duplicate data that might appear within the data set. This involves going through the database and eliminating any redundancies that may occur. Redundancies can adversely affect analysis of data since there are values which aren’t exactly needed. Expunging them from the database helps to clean up the data, making it easier to analyze. The other goal is to logically group data together. In a database which has undergone data normalization, data that relates to each other will be stored together. If data is dependent on each other, they should be in close proximity within the data set.
With that general overview in mind, let’s take a closer look at the process itself. While the process can vary depending on the type of database you have and what type of information you collect, it usually involves several steps. One such step is eliminating duplicate data as discussed above. Another step is resolving any conflicting data. Sometimes, datasets will have information that conflicts with each other, so data normalization addresses this conflicting issue and solves it before continuing. A third step is formatting the data. This takes data and converts it into a format that allows further processing and analysis to be done. Finally, data normalization consolidates data, combining it into a much more organized structure.
Considering how much of today's big data consists of unstructured data, organizing it and turning it into a structured form through data normalization is needed now more than ever.
The Importance of Data Normalization
Now that you know the basics of what normalizing data is, you may wonder why it’s so important to do so. Put in simple terms, a properly designed and well-functioning database should undergo data normalization in order to be used successfully. Data normalization gets rid of a number of anomalies that can make analysis of the data more complicated. Some of those anomalies can crop up from deleting data, inserting more information, or updating existing information. Once those errors are worked out and removed from the system, further benefits can be gained through other uses of the data and data analytics.
Through data normalization, the information within a database can be formatted in such a way that it can be visualized and analyzed. Without it, a company can collect all the data it wants, but most of it will simply go unused, taking up space, without benefiting the organization in any meaningful way. When you consider how much money businesses are willing to invest in gathering data and designing databases, not making the most of that data can be a serious detriment.
More Benefits of Data Normalization
Simply being able to do data analysis more easily is reason enough for an organization to engage in data normalization. There are, however, many more highly beneficial reasons to perform this process. One of the most notable is the fact that data normalization allows databases to take up less space. A primary concern of collecting and using big data is the massive amount of memory needed to store it. While storage options have become bigger and more efficient with advances in technology, we now find ourselves in a time when gigabytes, terabytes, and larger simply aren’t cutting it anymore. As such, finding ways to decrease disk space is a priority, and data normalization can do that.
Taking up less disk space is great on its own, but that also has the effect of increasing performance. A database that isn’t bogged down by loads of unnecessary information means data analysis can happen more quickly and efficiently. If you’re struggling with your data analytics, you’ll definitely want to consider data normalization for your database.
The benefits of data normalization go beyond disk space and its related effects. By engaging in this process, you’ll find it easier to change and update data within your database, since the redundancies and errors are absent and the data is much cleaner.
Many organizations use the data in their database to look at how to improve their organization. This can become a complex task especially if the data they have comes from multiple sources. Perhaps a company has a question about sales numbers that relates to social media engagement with customers. The data comes from different sources, so cross-examining them can be challenging, but with data normalization, that process is easier. Now, you can answer the questions that you have more quickly and know that the data you’re working with is accurate.
That’s still only the beginning of the benefits of data normalization. If you use a variety of Software-as-a-Service applications, for example, you can consolidate and query data from those applications with ease. If you need to export your logs from a location, then you can do so without having any repeated data values. You can visualize data from any business intelligence tools you have along with reports and analytics platforms. The usefulness of data normalization can’t be understated.
To go along with those benefits, data normalization can also be of great use to a variety of people. If you happen to be heavily involved in gathering, managing, and organizing data, you’ll definitely want to take full advantage of data normalization. The same goes for those who need to perform statistical modeling for the data they have as part of their job. In other words, data scientists and business analysts have a lot to gain from using the data normalization process. Additionally, if you spend a lot of your time working with business models, you can benefit from this process as well. The same goes for those who work with database maintenance, ensuring everything is running smoothly on that front. In fact, pretty much anyone involved in data and analysis will find data normalization to be extremely useful.
Data normalization should not be overlooked if you have a database, which goes for almost every business out there at this point. It’s an important strategy that is almost necessary now as organizations collect and analyze data on a scale never seen before.
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