Almost every business nowadays gathers data, which is usually utilized for purchase trend analysis or process improvement inside the business. Data normalization lets you measure things like performance and metrics, ensure success, and assist you in avoiding wasting resources while storing big swaths of data.
Although data normalization can appear very difficult, in modern times running any business depends mostly on it. We will briefly introduce the most often used techniques of data normalization below and discuss their importance.
Normalizing Data: An Elementary Definition
In general, data normalization is a method for arranging data to seem more coherent and similar. Clearly, this is a somewhat wide and perhaps somewhat circular description; hence, let’s examine more closely what data normalization really means.
Let us first dissect the definition of unstructured data vs. structured data. Collected using an established technique, structured data can be orderly arranged logically. Unstructured data lacks an organizing system or pre-defined form. Unstructured data is a risk since it makes data analysis and data pulling quite challenging, which can slow down many different corporate operations.
There are two basic objectives to data organization. First, arrange data such that it seems consistent across all fields and records. Second, raise the general cohesiveness of data entering methods. Data eliminates unstructured data and duplicates and generates a more logical – and, finally, more valuable – data storage system.
Once data is arranged, your business can maximize it since you will be able to more quickly review and cross-examine data. Data normalization will simplify your life whether you are compiling data for market research or asking questions from a SaaS application. From data redundancy to wasted memory to problems implementing software updates, unorganized data can cause a litany of problems.
Types of Data Normalization Agents
Fundamentally, data normalization begins with establishing a consistent form for all data throughout a whole firm. This reminds me somewhat closely of creating a style guide for a publication. Some basic guidelines for data normalization formatting could consist of:
- LA is usually spelled L.A.
- Phone numbers are entered without dashes, thus 123456789 rather than 123-456-789.
- Addresses are always shortened—that is, 176 Rowena Rd., not 176 Rowena Road.
Still, this is only extremely simple formatting. Most businesses will have to surpass this during data normalization. The three most often occurring normal forms are First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF).
First Normal Form (1NF)
This is the most fundamental kind of data normalization and guarantees groups free from recurring entries. Every entry just has one single value, hence every record has to be unique.
First normal form calls for:
- Every cell has just one value.
- Every column has the identical type of entries.
- Each record is uniquely identifiable, usually by some kind of main key (for example, college students are given unique student ID numbers to find them in a system rather than depending just on their names as it is conceivable more than one student is named something like Sarah Johnson).
The main key helps to uniquely identify every table record, while 1NF data normalization causes occasionally dependent problems. This thus makes the Second Normal Form necessary.
Second Normal Form (2NF)
Second Normal Form (2NF) indicates that the key drives every column. Should your key be customer ID, every column must be uniquely related to that customer ID. Though you will have to cut out partial dependencies, all the 1NF principles still apply.
In 2NF, your main key determines all properties in non-key columns. For instance, the main key for each customer you are entering into a database is their individual ID. Your columns document details including the phone number and address of the customers. Since this information depends on the particular client in question, it seems logical that the main key is the customer ID. There aren’t any problems here.
What would happen, though, if the database also contained item prices? Does price depend on the particular client in question? No; the pricing depends on the item. This results in partial dependency, so price is unduly dependent on customer ID.
You must break columns out and then create a junction column when you have attributes in columns unrelated to the main key of that database.
With its own main key, you can, for instance, relocate the item into its own table. The new main key becomes the type of item, which subsequently relates to the price. Now, greater rational sense dictates that the price depends on the name of the item rather than the client ID. Make a junction table then to link your two main keys—customer ID and item name.
Third Normal Form (3NF)
Everything in Third Normal Form (3NF) depends on the main key; nothing else determines anything.
Returning to the prior example, a 2NF table might include phone number, zip code, customer ID, purchase date, and state. Every item depends on the main key—the client ID. This scenario isn’t 3NF as, although everything depends on the consumer, there is really a double reliance. Why so? Additionally dependent on the state is the zip code. One can ascertain the state by either the customer ID or the zip code, which might result in duplication.
Keeping the zip code from your original table will help you to make this example 3NF by adding a second table including zip code and state.
Why Would You Normalize Your Data?
Minimize Duplicate Information
The main advantage of data normalization is most likely this one. It helps stop pointless duplication of data, which can consume enormous volumes of memory space. Normalizing your data greatly facilitates merging and matching of duplicate data. Your database has more room the fewer duplicates there are.
Segmentation of Marketing
Growing a company depends on effective segmentation led by you. Data normalization facilitates group classification depending on title, sector, status, and so on. This helps you to develop comprehensive lists depending on what is important to a particular lead, so enhancing the general flow of your marketing initiatives.
Measures of Performance and Accuracy
Analyzing and assessing data becomes an absolute horror in an unstructured database. Tracking metrics and performance takes less time analyzing when your data has a single consistent organization strategy. This gives your marketing team more accurate understanding of how campaigns are running and saves loads of time.
Normalizing Data: The Fundamental Rule
Although the material above could be a lot to absorb, as you grow used to standardizing corporate data it makes more logical sense. Regarding data normalization, there is undoubtedly a steep learning curve, but the advantages far exceed any negative effects. When done correctly, data normalization keeps important data coherent and orderly, therefore strengthening the business generally.
