Why normalize

You can apply only one normalization method at a time using this module. Therefore, the same normalization method is applied to all columns that you select.

To use different normalization methods, use a second instance of Normalize Data. Add the Normalize Data module to your experiment. Use the Column Selector to choose the numeric columns to normalize.

If you don't choose individual columns, by default all numeric type columns in the input are included, and the same normalization process is applied to all selected columns. This can lead to strange results if you include numeric columns that shouldn't be normalized! Always check the columns carefully. If no numeric columns are detected, check the column metadata to verify that the data type of the column is a supported numeric type.

To ensure that columns of a specific type are provided as input, try using the Select Columns in Dataset module before Normalize Data. Use 0 for constant columns when checked : Select this option when any numeric column contains a single unchanging value.

This ensures that such columns are not used in normalization operations. From the Transformation method dropdown list, choose a single mathematical functions to apply to all selected columns. Mean and standard deviation are computed for each column separately. Population standard deviation is used. MinMax : The min-max normalizer linearly rescales every feature to the [0,1] interval.

Rescaling to the [0,1] interval is done by shifting the values of each feature so that the minimal value is 0, and then dividing by the new maximal value which is the difference between the original maximal and minimal values.

Run the experiment, or double-click the Normalize Data module and select Run Selected. To view the transformed values, right-click the module, select Transformed dataset , and click Visualize. By default, values are transformed in place. If you want to compare the transformed values to the original values, use the Add Columns module to recombine the datasets and view the columns side-by-side.

To save the transformation so that you can apply the same normalization method to another similar dataset, right-click the module, select Transformation function , and click Save as Transform. You can then load the saved transformations from the Transforms group of the left navigation pane and apply it to a dataset with the same schema by using Apply Transformation. Without normalized data, it makes it very difficult to even gain a full understanding of how many data errors are within your customer database.

In companies dealing with big data, it is almost impossible. Data normalization is the process of structuring your relational customer database, following a series of normal forms. This improves the accuracy and integrity of your data while ensuring that your database is easier to navigate. Put simply, data normalization ensures that your data looks, reads, and can be utilized the same way across all of the records in your customer database.

This is done by standardizing the formats of specific fields and records within your customer database. Normalization includes the process of standardizing specific fields. In a customer database, these might include fields like first names, company names, addresses, phone numbers, and job titles. There are many ways that each of these records could potentially be expressed in a data set.

These are all pretty standard examples of the type of fields and customer data that needs to be normalized to make the most of it. Every company has different criteria when it comes to normalizing their data. Normalized data is critical for the systems that use that data, including marketing automation systems, sales systems, and reporting systems. The Complete Guide to Data Cleaning.

Now the big question — why? Why should I spend all of the time and effort to normalize my data? Well, the answer to that is simple. Most of the negative effects of low-quality data fly under the radar. In companies that rely on big data, it can really hurt them over time. When you have low-quality data, your marketing teams are scared to inject more data-based personalization into your marketing campaigns. Your sales teams are affected too.

Low-quality and missing data means that they lack the critical context that they need to speak directly to the biggest concerns of your prospects and customers. This directly leads to lower sales and poor quality analysis. Further, low-quality data negatively impacts lead scoring , which hinders their ability to effectively segment and categorize prospects so that they can engage with them in a way that will resonate with them.

Here are 5 of the top reasons why all companies should normalize their customer data in some form. With normalized data, it makes it a whole lot easier to find and merge or delete duplicate customer records.

With duplicate records, companies can never be sure that they are working with full and complete information when referencing a single record. When it comes to marketing, duplicate records may result in your prospects receiving the same marketing materials more than once. In sales, splitting a single customer's data between two records means that your sales reps may engage with prospects while lacking the appropriate data and insights. Duplicate HubSpot data also increases your storage costs.

Imagine that you are a B2B company. You want to segment your prospects based on their job titles. Sometimes, datasets will have information that conflicts with each other, so data normalization is meant to address this conflicting issue and solve 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. Consider of the state of big data today and how much of it consists of unstructured data. Organizing it and turning it into a structured form is needed now more than ever, and data normalization helps with that effort.

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.

It is usually through data normalization that 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 and not benefiting the organization in any meaningful way.

And 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. 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 reasons to perform this process, all of them highly beneficial. One of the most notable is the fact that data normalization means databases take up less space. A primary concern of collecting and using big data is the massive amount of memory needed to store it.

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.