Data Quality

What is Data Profiling?

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Poor data quality is a recipe for disaster for companies across any and all industries. That’s why data profiling is indispensable to a good quality data strategy.

Harvard Business Review once reported that only 3% of data meet quality standards — and that extremely low number continues to hold true to this day. So, how can companies collect, access and properly analyze relevant data? The answer is simple: data profiling.

Data profiling takes raw data and transforms it into actionable insights for your business. Data profiling sorts through your data and examines, analyzes and summarizes it into a high-level overview. Through this process, your company can identify any potential data errors, such as missing values, duplicated records, unusual outliers, unnecessary values, and so on.

Besides sifting through potential bad data, data profiling also uses analytical algorithms to recognize data set characteristics and create sharable reports that are relevant to your business.

Read on to discover more about data profiling.

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What are the different types of data profiling?

Not all data profiling is the same. In fact, there are three main types of data profiling that you should familiarize yourself with:

  1. Structure profiling
    This type of profiling checks your data for consistency and formatting within the structure of the dataset. It also helps you understand the type of data in a field, such as numeric, text, range or picklist.For example, it can help to identify phone numbers without the correct number of digits or those that may have an “x” or “ext” to denote an extension number. Structure profiling will show you the current state of your data structure and help define the structure moving forward. It can answer important questions, including do we keep the extension in the phone field or place it in its own field?
  2. Content profiling
    This type of profiling prioritizes the data quality. It identifies which data isn’t standardized to fit with the existing data and whether it needs to be fixed. This can include identifying phone numbers without an area code or email addresses missing an “@” character. This type of profiling is looking at the accuracy of the values in the fields that hold each data point to identify any systemic data issues.
  3. Relationship profiling
    This type of profiling analyzes the connections (relationships) between data. This is helpful in understanding data workflows and the fields they are reliant on as well as preserving relationships when moving or migrating data.

Each type of profiling works together to help your organization sort and improve the quality of your data — making it accessible and understandable for business intelligence.

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What are CRM data profiling techniques?

There are many ways to profile your data. The following four steps will help you understand your data and make it fit for business use:
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1. Discovery

Structure discovery, content discovery and relationship discovery help you get the lay of the land of your data landscape. You will uncover patterns and inconsistencies in your data formatting, see what data is linked together, identify missing or inaccurate data, and where the same data may be captured multiple times using different fields. It’s essential to know what your current data situation looks like before starting any data cleansing or data migration projects. Any sort of data quality work or business process implementation is better poised to be successful having gone through the profiling process first.

Profile your Salesforce or Dynamics 365 data with a free DemandTools data quality assessment.

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2. Documentation

The documentation phase may be an arduous task — especially if you’ve been putting off documenting your processes, reasons for collecting data points and where they’re used. But documentation of what you discover is absolutely required for any profiling exercise to bring value.

It will also drive the point home to document as you go, including any time a business process changes or new data points are added to your CRM. Think of the documentation step as creating a database that explains all of your other databases. It’s where you can note which data is used the most and show how systems work together using specific data points or functions.

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3. Standardization

The next step is to make sure your data follows a defined format, often referred to as standardization. For example, a United States postal code of 33914 could appear as 33914-1234 or 339 14 with a space in the middle. This not only makes it difficult to query and report on but it throws off every other process that relies on that data. Fixing those errors and keeping formats consistent across all data and systems makes human or computer analysis much more feasible.
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4. Cleansing

Data cleansing is the act of fixing any formatting errors to meet your new standardization rules. It also involves removing any outdated, corrupt, duplicated, or useless data. Some consider this a last step in the profiling process when in fact, this step never ends. It is the cyclical, ongoing management of your data quality.
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What are the benefits of data profiling?

Data profiling is the key step between collecting and using your data. It’s a process that enables your business to discover, understand and organize the data that you’ve acquired.

It helps with:

  • Better data quality
  • Predictive data decisions
  • Proactive crisis management
  • Organized sorting
  • Business process optimization

Each year, data profiling becomes more and more important as it ensures high-quality data is being collected and used efficiently. It not only helps to find important information that may be hidden within your data, but it also helps your organization follow data regulations and industry standards.

Similarly, business systems are expected to house increasingly diverse and large amounts of data that need to be report-ready and usable. The quantity of the data — no matter how expansive — will not benefit your organization if it can’t be correctly diagnosed, integrated and understood for business analysis and strategy.

Important note: In June 2021, Apple announced its plans to introduce Mail Privacy Protection (MPP) for its native Mail client users. This feature prevents email senders from accessing tracking data to more accurately engage with customers. Data profiling can be an effective way to meet the challenge head-on as it prioritizes customer-submitted data that won’t be blocked for certain users in the future. Learn more about the mail privacy protection update here.

53%

of organizations say that missing or incomplete data seriously impairs their ability to fully leverage their CRM system

*(source: Validity Research: State of CRM Data Health 2022)

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Most common data profiling challenges

The most common difficulty that faces data profiling is the large amounts of data that companies often need to process. For many, their data systems contain information from years before, which may not have been standardized or formatted — resulting in thousands of errors.

More challenges include:

  • Incomplete data: Older data may suffer from poor quality as there may be missing information in important fields.
  • Inadequate profiling tools: If your data profiling tools aren’t expansive enough, it may become too difficult to analyze the full data source completely or thoroughly.
  • Manual data profiling: May be incomplete and resource-intensive, yielding low returns for high efforts.

Many of these data profiling challenges can be met head-on with how you choose to conduct your business’s profiling.

DemandTools Assess helps businesses profile their CRM data.

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How is data profiling conducted?

Data profiling utilizes a wide variety of techniques to sort through a company’s data. These techniques include consistent duplicate scanning, field usage tracking and data quality exception reporting.

By identifying different types of data and recognizing patterns, data profiling can then be used to highlight any potential problems that are harming your organization’s data quality. These errors can vary from misspellings to incomplete information to duplicate data.

Profiling should also include the phase for documenting workflows, the intended use for each field, and data access thresholds.

Due to the intricate nature and large volume of data that profiling needs to sort through, profiling solutions are often needed to ensure good quality of data.

55%

of businesses use manual processes to identify and correct data quality issues

*(source: Validity Research: State of CRM Data Health 2022)

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Data Profiling Solutions

Data is your most valuable asset, which is why most companies choose to implement purpose-built data management software that can handle data sets of varying sizes and complexity. It’s more time and cost-effective than manual data profiling, which can overwhelm department resources and has the added potential of human error. If you’re interested in reading more about the connection between data quality and CRM adoption, read our post here.

To help companies evolve with the future of data, Validity’s DemandTools is a versatile and secure platform that cleans and maintains CRM data efficiently and — most importantly — accurately. To learn more and discover how you can access report-ready data for improved ROI, check out DemandTools here.

Try DemandTools for free today!

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