Data normalization, wow it gets me damn excited! - said no marketer ever. But if lead generation, reporting, & calculating ROI is important to you like any other marketer, then data normalization will matter too.

The ways of business can drive normal mortals crazy. Marketing has evolved greatly from sending postal mails to social media marketing & email campaigns in the advanced times of today. In earlier days as well as current, being in possession of accurate client data is indispensable for successful campaigns. But in today's world of cut-throat competition, it has become damn important to have client data which is relevant & exact. Majority B2B marketing campaigns fail to generate the needed ROI as the customer data is stale & irrelevant.

Enough backstory, let's get into what data normalization actually is. The data that's available to a corporate contained in databases shall have similar attributes that are attached to it. For example, data such as job title, industry, state, country, or platforms/technologies. These crucial details can impact lead scoring & lead nurturing messages, so their accuracy & consistency are vital. This is where data normalization comes in.

The original idea can be traced back to the IBM researcher E.F. Codd, who in 1970 published a paper describing the relational database model. Codd described normal forms of database relations as an essential element of the relational technique. Since then data normalization found favor among a ready audience as highly efficient means for data storage was very necessary.

In simple terms, data normalization is a technique that enables to create relativity & context within marketing database by grouping similar values into one common value. It can be the marketer's trump card when it comes to organizing campaigns & keeping track of marketing qualified prospects. Main goals of performing data normalization are:

Eliminating redundant data. Enforcing data consistency. Ensuring data dependencies.

 Isolating data. How does data normalization works?

There are three key steps to data normalization from project process standpoint:

Interpretation of data & recognizing which data fields are predominant.

This is the hardest part. Understanding the possible permutations of dirty data & knowing how normalized data should look like can become tough for the inexperienced ones. Marketers catch data values that are most relevant to your business in unique ways. In normal business order, it is important to familiarize with the business situation in order to recommend starting points & then assess the database that is set up for each field to work through a list of data values relevant to your business.

If a marketer catches some data from a form, he'll want to know what data is being collected as its important for segmentation & qualification. Yea, it can be asked as an open text field on the message but understanding where & how this data is collected can aid a lot in determining whether normalization is actually needed.

Interpreting the lookup matrix of viable variations of dirty data & the finite.

This is the final process. Here the establishment of the matrix that maps dirty data to new standard data values. Once it's done, the data normalization program will be needed in the marketing automation system to make it easier to compare the entry data with the final result.

Common normalization problems that can be cured by data normalization

As many things in marketing, normalization gets trickier once you think about how the information is collected. There are three common means of data collection in marketing that cause issues which can be cured by data normalization.

Web forms that have open text fields are used by many organizations to prospect & collect customer data. This tends to create confusion among prospects who have similar job titles. For example, two prospects who both have a job title of "Director of Operations" could fill out the form "Dir of ops" & "DOP" respectively. Without Data normalization process in place, the data won't reflect this commonality.

Trade shows are commonplace for marketers to collect business cards. There are plenty of data extraction technologies that can pull information from hard sources & convert information into digital sources. But still, normalization issues can happen after inputting these data sets into a larger database.

Sales reps do their own marketing during outbound prospecting activities. When they connect with qualified leads, they'll need to manually enter the prospect data. Even though this method is necessary, it's inefficient as it leaves plenty of chance for human errors in a number of critical fields.

Where data normalization should fall?

You, marketers, know that a lot of activities go into the lead management system which include collecting data through lists & forms, scoring individual leads, adding contacts to nurture campaigns & pushing leads to sales for follow up. So there's no doubt that bad data can affect numerous systems & processes.

So, marketers should ensure that data normalization starts at the very beginning of lead management workflow & should continue to run in the background. In this way, it can be ensured that every action taken against data is worthwhile so it remains clean, complete, and meaningful to your business processes.