What is Data Profiling?

Profiling is a crucial part of data preparation programs. It's the process of examining and analyzing data sets to gain more insights into their quality. Having mountains of data is the norm in modern business. But accuracy can make or break what you do with it.

Profiling helps you learn more about how accurate and accessible your data is while giving your teams more knowledge about its structure, content, interrelationships and more. This process can also unveil potential data projects, highlighting ways you can use your data assets to boost the bottom line.

Types of Data Profiling

There are three primary forms of profiling.

The first is structure discovery. This process is about formatting data to ensure everything is uniform and consistent. Statistical analysis can give you more insight into your data's validity.

The second type of profiling is content discovery. With the content discovery, the goal is to determine the quality of the data. It helps identify anything incomplete, ambiguous or otherwise null.

Finally, we have relationship discovery. As the name implies, it's about determining how data sources connect. The process highlights similarities, differences and associations.

Why Profiling is Necessary

There are many benefits to profiling data. Ultimately, the biggest reason to include it in your data preparation program is to ensure you work with credible, high-quality data. Errors and inconsistencies will only set your organization back. They can misguide your strategy and force you to make decisions that don't provide the desired results.

Another benefit is that it helps with predictive analysis and core decision-making. When you profile data, you're learning more about the assets you hold. You can use this process to make predictions about sales, revenue, etc. That information can guide you in the right direction, making critical decisions that help generate growth and success.

Organizations also use profiling to spot potential issues within their data stream. For example, the content discovery phase highlights errors and inconsistencies. Chronic problems may point to a glaring issue within your system, helping you spot quality data issues at their source.

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What is a Data Onboarding Process?

Gathering data about your customers is a critical part of the marketing puzzle. You need to understand who they are to perfect the customer experience while obtaining knowledge about their unique needs. If you operate entirely online, gathering that data is relatively easy. But what if you have to transfer offline data to an online environment?

For example, you may interact with prospects at events or during an in-store sales process. The information you gather in offline environments stays separate from what you collect online, creating potential headaches in managing your customer relationship management strategies.

Data onboarding is about connecting offline records with online users, painting a complete picture of a customer's journey with your company.

Why Data Onboarding Processes are Important

The goal of data onboarding is to consolidate data about your customers regardless of where they came from or what stage in the buyer's journey they enter your online environment. For example, say that a prospect learns about your company in person. They might buy a product, creating the first customer records.

In the future, they might want to continue supporting your business through online purchases. Onboarding ensures that your marketing and sales teams are up-to-date on that customer's history. It allows your teams to provide the most impactful marketing materials that cater to every customer's needs.

How Data Onboarding Software Improves Efficiency

Traditionally, reconciling offline and online data is an arduous process. Basic offline data includes customer names, email addresses, contact information, etc. Meanwhile, online data refers to digital IDs, transaction data, device information, etc.

The onboarding process requires you to match that information to create a complete profile for every customer. Usually, that would involve manual data inputs and reconciliation. Not only is that resource intensive, but it also leads to quality and governance issues.

Data onboarding software improves efficiency across the board. Teams can quickly create, edit and manage data from a single platform. The software becomes a single source of truth for all teams accessing the data while improving accessibility to all stakeholders. Features like automation can make quick work of matching and anonymization, optimizing how your organization uses customer data.

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