Data is at the heart of all business decisions. It surrounds us at every turn. Unfortunately, the information you get directly from data sources is often unstructured, fragmented, and misleading. You are probably sitting on a pile of dull data that could help you attract leads, improve your ROI, and increase revenue. Data normalization will turn your raw numbers into actionable insights that drive value.

What is Data Normalization?

Simply put, data normalization is just cleaning up the collected data to make it more clear and machine-readable.

Often the data collected by different systems are in different formats, there may be duplicates, and there may be unnecessary data as well. And you won’t get good results or insights if you try to visualize or analyze the data. Messy or cluttered data is difficult to understand and data redundancy creates unnecessary storage costs.

In data normalization, the data is made consistent, and the duplicates or other errors are removed and brought together in a similar format so that it’s easier to interpret.

data normalization

How does it work?

Now that we have a rough idea of what data normalization is, it’s time to dig deeper into how it works in practice. Even though the process might differ a bit depending on the type of database and collected information itself, some key steps are often involved.

As mentioned, data normalization starts by removing duplicates. Then, it continues with solving all the issues in case any conflicting data appears before moving forward. Third, formatting follows up, turning the data into easy-to-process information. Eventually, data gains a way more organized structure after it’s consolidated.

Digging into the specifics, there are three primary forms of data normalization, namely the first, second, and third normal form (NF). Each of them defines how to put entity types into a series to grow the level of data normalization.

  • First normal form (1NF): 1NF is a fundamental part of data normalization, which guarantees no recurring entries in a group. To qualify as 1NF, each cell must contain a single value, and each record must be unique.
  • Second normal form (2NF): 2NF is the second step of eliminating data redundancy. After data apply a whole set of 1NF requirements, you must ensure that information has one primary key by placing all data subsets in multiple rows to separate tables. Finally, you would be able to create relationships through new foreign key labels.
  • Third normal form (3NF): When all 2NF requirements are applied, data can appear in the 3NF rule. Following that, data in a table must depend on a primary key. You should move all data affected by a change in the primary key to a new table.

The given guidelines will become more apparent as you better understand the normalization forms, and dividing your data into tables and levels will turn out to be straightforward. These tables will thereby make it simple for anybody in an organization to collect data and guarantee that it’s accurate and not duplicated.

Who needs Data Normalization?

Every business that wishes to run successfully and grow needs to regularly perform data normalization. It is one of the most important things you can do to get rid of errors that make running information analysis complicated. Such errors often sneak up when changing, adding, or removing system information. When data input error is removed, an organization will be left with a well-functioning system that is full of usable, beneficial data.

With normalization, an organization can make the most of its data as well as invest in data gathering at a greater, more efficient level. Looking at data to improve how a company is run becomes a less challenging task, especially when cross-examining. For those who regularly consolidate and query data from Software-as-a-Service applications as well as for those who gather data from a variety of sources like social media, digital sites, and more, data normalization becomes an invaluable process that saves time, space, and money.

data normalization

Benefits of Data Normalization

As data becomes more and more valuable to any type of business, data normalization is more than just reorganizing the data in a database. Here are some of its major benefits:

  • Reduces redundant data
  • Provides data consistency within the database
  • More flexible database design
  • Higher database security
  • Better and quicker execution
  • Greater overall database organization

A company can collect all the data it wants from any source. However, without data normalization, most of it will simply go unused and not benefit the organization in any meaningful way.

What are the limitations of Data Normalization?

  • As information is dispersed over more tables, the necessity to join tables grows, making the task more time-consuming. In addition, the database becomes more exciting to recognize.
  • Tables will include codes rather than true information since rehashed data will be stored as lines of codes rather than genuine data. As a result, there is always a requirement to visit the query table.
  • The information model is built for applications, not for impromptu questioning, hence the data model turns out to be extremely difficult to ask against (An impromptu question cannot be answered before the question is asked). It is made up of a SQL that is built up over time and is usually done by work area cordial question devices). As a result, displaying the knowledge base without knowing what the client wants is problematic.
  • The exhibition becomes increasingly slow as the typical structure type advances.
  • To carry out the standardization cycle effectively, accurate information on the many conventional structures is required. Unscrupulous use can result in an abysmal plan full of substantial anomalies and data irregularities.

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Conclusion

In all, data normalization is an essential part of business for all those dealing with large datasets. Not only is it important to obtain quality data, but it is also important to maintain it through normalization. Analysts, recruiters, and investors alike will benefit from data normalization.