Data Normalization: The First Step Towards Actionable Insights

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?

To use some big words, data normalization is the process of organizing data in such a way as to fit it into a specific range or standard forms. It helps analysts acquire new insights, minimize data redundancy, get rid of duplicates, and make data easily digestible for further analysis.

However, such wording might be complicated and confusing, so let's boil it down to a simple and illustrative example.

Imagine you're a gardener harvesting apples. This year, you've managed to harvest 500 apples from 20 trees. However, your neighbor boasts of gathering 1,000 apples and calls you an awful gardener.

If you compare your 500 apples to your neighbor's 1,000 apples, it might seem that you are not a very skillful gardener. But your neighbor never told you that he planted a hundred apple trees to achieve such a yield.

If you normalize the data, it is revealed that your neighbor is in an unpleasant situation. With 500 apples from 20 trees, you harvest 25 apples per tree, while your neighbor gets only 10 apples per tree. So, who's an awful gardener now?

The analysis can get muddied by lots of unnormalized data, so you can't see the forest for the trees.

Here's another example using gun control data. It shows us just how easy it is to fall victim to a cognitive bias without data normalization.

Without normalized insights, it's almost impossible to build a comprehensive picture and make informed decisions on the researched topic.

Key takeaways:

Data normalization is a process of reorganizing data in a dataset.
This process simplifies further analysis and data operations.
Data normalization ensures you get a comprehensive view of the researched topic.

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Ok, but when do you need to normalize data?

Users experience different problems as a result of heterogeneous data (data with high variability of data types and formats). There are other use cases when you need to normalize data. Let's consider this issue from the perspective of marketing analysts.

First and foremost is the unification of naming conventions. For example, while gathering data from tens of marketing channels, analysts often encounter the same metrics under different names. As a result, analysts face difficulties when mapping data.

For example, here's how one Reddit user explains their problems with disparate data.

Another challenge is consolidating disparate data, such as currencies or timezones, into a single source of truth. You simply can't build an insightful dashboard while your ad spend is divided into dollars, euros, pounds, and a few other currencies.

Here's how another marketer describes this problem on Reddit.

So, when is it the right time to normalize your data?

When matching metrics in your data set have different naming conventions.
When you need to match heterogeneous data, such as currencies, time zones, date formats, etc.
When you notice some of your data is redundant, and you need to eliminate it.

Terms that are often confused: data normalization vs. standardization

In data transformation, standardization and normalization are two different terms that are often confused.

Normalization rescales the values of a data set to make them fall into a range of [0,1]. This process is useful when you need all parameters to be on a positive scale.

Standardization adjusts your data to have a mean of 0 and a standard deviation of 1. It doesn't have to fall into a specific range and is much less affected by outliers.

This information is more than enough for marketing analysts to stop confusing the terms. However, if you want to dive deeper into this topic, you can find all the differences between the two concepts here.

How to normalize data and get your insights together

At its core, data normalization requires you to create a standard data format for all records in your data set. Your algorithm should store all data in a unified format without regard to the input.

Here are some examples of data normalization:

Mister HOLmES should be stored as Mr. Holmes.
Fifth Avenue Sf should be stored as 5th Ave, San Francisco.
CTO should be stored as Chief Technical Officer.

Getting your data normalized goes hand in hand with database normalization. Let's get a brief overview of what that is.

Database normalization: a precondition for purified insights

Database normalization is the process of organizing tables and data rows inside a relational database.

The process includes creating and managing relationships between tables. While normalizing databases, analysts and data engineers rely on rules that help protect data and make it more flexible for further analysis.

All rules are declared by database normalization types or so-called “Normal Forms”.

There are seven Normal Forms in total (the first three are the most frequently used):

First Normal Form (1 NF)
Second Normal Form (2 NF)
Third Normal Form (3 NF)
Boyce Codd Normal Form (BCNF)
Fourth Normal Form (4 NF)
Fifth Normal Form (5 NF)
Sixth Normal Form (6 NF)

Let's go through all of these Normal Forms to learn how they help normalize your data.

First Normal Form (1NF)

The first Normal Form requires a data table to meet the following conditions:

Each table cell should have a single value.
Each record should be unique.
None of the columns should contain hidden values.

Let's review an example. Here we have two records from the employee table.

The Department_Name cells contain more than one parameter in a cell. Thus, they violate the first rule of the 1NF.

You have to split this table into two parts to normalize your table and remove repeating groups. The normalized tables will look like this:

The Second and Third Normal Forms revolve around dependencies between primary key columns and non-key columns.

Second Normal Form (2NF)

The main requirement of the Second Normal Form is that all of the table's attributes should depend on the primary key. In other words, all values in the secondary columns should have a dependency on the primary column.

Note: A primary key is a unique column value that helps to identify a database record. It has some restrictions and attributes:

A primary key value can't be NULL.
A primary key value should always be unique for each table.

Additionally, the table must already be in 1NF with all partial dependencies removed and placed in a separate table.

At this stage, a composite primary key becomes the most problematic issue.

Note: A composite primary key is a primary key made out of two or more data columns.

Let's imagine that you have a table that keeps track of the courses that your employees have taken. Different employees may enroll in the same course. That's why you'll need a composite key to identify the unique record. The Date column will be the additional parameter for your composite key. Take a look at the example:

However, the Description column is functionally dependent on the Name column. If you alter the course's name, you should also change the description. So, you'll need to create a separate table for the course description to comply with 2NF requirements.

That's how you can give the course description a separate key and get away from using a composite key.

Third Normal Form (3NF)

The third Normal Form requires all the non-key columns in your table to depend on the primary key directly. In other words, if you remove any of the non-key columns, the remaining columns should still provide a unique identifier for each record.

Here are the main requirements of the 3NF:

All tables comply with 2NF requirements.
Non-primary key columns should only depend on primary key columns.
Tables have no transitive functional dependency.

The main difference between 2NF and 3NF is that in 3NF, there are no transitive dependencies. A transitive dependency exists when the non-key column depends on another non-key column.

Note: A transitive dependency is an indirect relationship between values within the same table that causes a functional dependency. A functional dependency sets particular constraints between attributes. In this relationship, attribute A determines the value of attribute B, while B determines A's value.

Take a look at the example below:

Here, employee ID determines the department ID from our previous example, while the department ID determines the department name. Here's where an indirect dependency between employee ID and department name occurs.

To comply with 3NF requirements, we need to split the table into multiple pieces.

With this structure, all non-key columns depend solely on the primary key.

Even though there are seven Normal Forms, the database is considered to be normalized after complying with 3NF requirements. We'll do a quick overview of the remaining Normal Forms to cover the topic to the end.

Boyce-Codd Normal Form (BCNF)

It's a more robust version of the 3NF. A BCNF table should comply with all 3NF rules and have no multiple overlapping candidate keys.

Fourth Normal Form (4NF)

The database is considered a 4NF if any of its instances contain two or more independent and multivalued data entries.

Fifth Normal Form (5NF)

A table falls into the fifth Normal Form if it complies with 4NF requirements, and it can't be split into smaller tables without losing data.

Sixth Normal Form (6NF)

The sixth Normal Form is intended to decompose relation variables into irreducible components. It might be important when dealing with temporal variables or other interval data.

That's all for database normalization 101. Now that you know how everything works, you can better understand the benefits that data normalization brings to your insights.

Why is it important to normalize data?

As we've already mentioned, data normalization's major goal and benefit is to reduce data redundancy and inconsistencies in databases. The less duplication you have, the fewer errors and issues that can occur during data retrieval.

However, there are less obvious benefits that assist data analysts in their workflow.

Data mapping is no longer a time suck

If you've ever had to deal with unnormalized data, you know that the process of mapping data from multiple tables into one is pretty tedious.

It requires joining multiple tables, dealing with duplicates, and cleaning many empty data entries.

Of course, you can normalize data manually by writing SQL queries or Python scripts. However, data mapping tools with automated data normalization capabilities will speed up the process.

For example, Oracle Integration Cloud offers data mapping functionalities. After normalizing data in the cloud, the tool then builds metadata for source schemas and creates a one-to-one record for every data object in the target schema.

Analysts working with marketing insights have their own hidden gems. Improvado's MCDM (Marketing Common Data Model) is a Swiss army knife for marketing and sales data normalization. The tool unifies disparate naming conventions, normalizes your insights, and bridges the gap between data sources and your destination with no manual actions required.

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Use data storage more efficiently

With each passing day, companies collect more and more data that take up storage space. Whether you use cloud storage or an on-premises data warehouse, you have to use it effectively.

For example, 100 TB of data stored in AWS S3 will cost you $2,580 per month. What's more, Amazon will charge you for every query you perform on your data. Tens of gigabytes of redundant data will not only increase your invoice for storage services but also cause you to pay for the analysis of meaningless insights.

Scaling on-premises data warehouses is also expensive, so clearing out unnecessary information can help you reduce operational costs and TCO (Total Cost of Ownership).

Reduce your time-to-insights

Apart from cost reduction, analysts can also boost their analysis productivity.

Executing queries on terabytes of data takes time. While your system processes the query, you can feel free to drink a cup of coffee and discuss politics with your colleagues. But it's a pity when the query output is pointless because you had some inconsistencies in your data set.

With normalized data, you always get the expected output, but without surprises such as "N/A", "NaN", "NULL", etc. Moreover, the system carries out your queries faster when you parse only the relevant data. Who knows, maybe next time you'll get the output before your coffee machine makes your cappuccino!

Build dashboards that you can trust

Data visualization is the best way to build a comprehensive picture of your analysis efforts. However, dashboards lack value if you build them upon harsh data. The dashboard won't reflect the true state of affairs if you feed it duplicates.

That's why data normalization is a top priority if you want to explain complex concepts or performance indicators through the prism of colorful charts and bars.

How to normalize data in different environments

Since data analysts work with different tools, we will explain how to normalize data in the most in-demand environments in today's market.

How to normalize data in Python

Data scientists and analysts working with Python use several libraries to manipulate data and tidy it up. Here are the most popular among them:

We'll review some of the functionalities of these libraries that might help you speed up your data normalization process.

Dropping columns in your data set

Raw data often contains excessive or unnecessary categories. For example, you're working with a data set of marketing metrics that include impressions, CPC, CTR, ROAS, and conversions, but you only need conversions from this table.

If everything besides conversions isn't important for the analysis, you need to remove excessive columns. Pandas offers an easy way to remove columns from a data set with the drop() function.

First, you have to define the list of columns you want to drop. In our case, it will look like this:

column_drop_list = ['Impressions, 'CPC', 'CTR', 'ROAS']

Then, you need to execute the function:

dataframe_name.drop(column_drop_list, inplace=True, axis=1)

In this line of code, the first parameter stands for the name of our list of columns. Setting the inplace parameter to True means that Pandas will apply changes directly to your object. The third parameter indicates whether to drop labels or columns from the data frame ('0' stands for labels, '1' stands for columns).

After checking the data set again, you'll see that all redundant columns have been successfully removed.

Cleaning up data fields

Another step is to tidy up data fields. It helps to increase data consistency and get data into a standardized format.

The main problem here is that you can't be sure that the API of a marketing platform will transfer 100% accurate data. You could still encounter misplaced characters or misleading data down the line.

A single marketing campaign can only have one amount of impressions. That's why we need to separate valuable numbers from other characters.

Regular expressions (regex) can help you identify all of the digits inside your dataset. This regex generator will help you create a regular expression for your needs and test it right away.

Then, with the help of the str.extract() function, we can extract the required data from the data set as columns.

true_impressions = dataframe_name.str.extract(your_regex) , expand = False)

Finally, you might need to convert your column to a numerical version. Since all columns in the data frame have the object type, converting it to a numerical value will simplify further calculations. You can do this with the help of the pd.to_numeric() function.

Renaming columns in the data frame

Data sources often transfer columns with names analysts can't understand. For example, CTR might be called C_T_R_final for some reason.

Another problem is revealed when you merge data from different sources and analyze it as a whole. While the first data source refers to impressions as imps, another one calls it views. This makes it difficult to calculate and build a holistic picture across all data sources.

That's why you need to rename your columns to get everything structured.

First, create a dictionary with the future names of your columns. Let's assume that we have impressions from Google Ads and Facebook Ads with different naming conventions. In this case, our dictionary will look the following way:

new_clmn_names = {'Imps' : 'Google Ads Impressions',

'Views': 'Facebook Ads Impressions'}

Then, you should use the rename() function on your data frame:

dataframe_name.rename(columns=new_clmn_names, inplace=True)

Now, your columns will have names assigned in the dictionary.

Pandas has a lot more different functions that can help you normalize data. We recommend reading the official documentation to get a better grasp of other functionalities.

How to normalize data in Excel

Excel or Google Sheets is a powerful tool loved by many analysts due to its ease of use and broad capabilities. There's no doubt that programming languages, such as R or Python, have more features to offer, but spreadsheets do a great job of analyzing data.

However, your tables might contain heterogeneous data, and Excel provides a toolset to normalize insights.

Trimming extra spaces

Identifying excessive spaces in a large table is a waste of time when done manually. Fortunately, Excel and Google Sheets have a TRIM function that allows analysts to remove extra spaces in a data set with just one function. Take a look at the example below.

As you can see, the entry data has large whitespace between the words. With the TRIM function, data is put into the right format.

Removing empty data rows in the data set

Empty cells can escalate into a true nightmare during the analysis. That's why you should always deal with them beforehand. Here's how to do that.

Choose all cells and click on the "Data" tab in the toolbar.

Click on the "Sort range by column (Z to A)" button in the sort range menu.

Now you have all of the empty rows at the bottom of your table, so you can simply select and delete them.

Removing duplicates

Duplicate data entries are a common problem for analysts working in Excel or Google Sheets. That's why these tools have a dedicated feature to remove duplicates in a fast and easy way.

Google Sheets has a UNIQUE function that allows you to keep only unique data in your table.

Suppose you have this simple table with Name and Age columns that contain multiple duplicates.

You can get a clean table without any duplicate entries by feeding your data set to the UNIQUE function.

Text case normalization

After importing data from text files, you will often find inconsistent text cases in names or titles. You can easily fix your data in Excel or Google Sheets by using the following features:

LOWER() - Converts all text into lower case.
UPPER() - Converts all text into upper case.
PROPER() - Converts all text into proper case.

Depending on your particular use case, there are many ways to normalize Excel or Google Sheets data. These guides shed more light on Excel data normalization:

Automated data normalization tools

Programming languages offer a broad toolset to normalize your data. However, manual data normalization has its limitations.

First of all, analysts need strong engineering knowledge and hands-on experience with the required libraries. Data scientists and engineers are highly desirable talent, and their paychecks are often astronomical.

Moreover, coding takes time, and it's often prone to mistakes. So, a follow-up review of the analyzed dataset is a must-have. Eventually, the analysis process may take a lot more time than intended.

Automated tools save analysts' time and offer more precise results. You can streamline your data to the normalization tool and get purified insights in minutes, not days.

Let's consider the example of Improvado. Improvado is a revenue ETL platform that helps marketing analysts and salespeople align their disparate data and store insights in one place.

The platform gathers data from 300+ sources and helps analysts to normalize them with zero effort. Today's marketing and sales tools market is fragmented, and different platforms use different naming conventions for similar metrics.

Improvado's Marketing Common Data Model (MCDM) is a unified data model that provides automated cross-channel mapping, deduping, and UNIONing of popular data sources. Besides, it stitches and standardizes paid media sources together, automatically transferring analysis-ready insights to your data warehouse.

For example, Improvado can automatically merge Google Ads and Bing data into one table.

Furthermore, the platform can automatically parse data and convert it to a suitable format. For example, here's how Improvado splits Date into Day of Week, Month, and Year columns.

Improvado goes even further, allowing analysts to automatically parse Adobe Analytics tracking codes on your website. You can extract the embedded AdGroup ID from the tracking code without manual manipulations.

Then, analysts can combine internal spreadsheets with classifications and match them based on the network ID.

Eventually, analysts get all of the tables matched by AdGroup ID and combined into the final results table.

That was just one of many use cases where Improvado can normalize data and deliver insights in a digestible way for further research.

With all insights in one place, the platform can streamline them to any visualization tool of your choice. Purified and structured data makes building a comprehensive cross-channel dashboard much easier. For example, here's a Data Studio dashboard based on Improvado's insights:

Normalize marketing and sales data with Improvado

Data normalization takes time, but clear insights are always worth the effort. Why waste your time on data normalization if you can dive straight into analysis and dramatically cut your time-to-insight?

Improvado untangles your web of revenue data, reduces the time spent on manual data manipulations, and ensures the highest granularity of insights. With this ETL system, you can analyze trustworthy data and build real-time dashboards that demonstrate the effectiveness of your marketing dollars. Schedule a call to learn more.