What is Data Profiling?

Data profiling is analyzing and exploring data to understand how it’s structured, what it contains, the relationships between data sets, and how it could be used most effectively.  As such, data and analytics teams will perform data profiling better to understand the condition and the value of their data to determine best how to transform the data into an analytics-ready form.

DataOps Process: How it helps

Data Profiling Basics

Organizations are increasingly using data profiling because it can improve many processes across the enterprise by delivering many benefits, which we’ll explore next.

  • 1 Aiding project management

    Before beginning a project, a manager might use data profiling to determine whether there is enough insight to move forward. In turn, this reduces time and money waste while shortening the overall project lifecycle and improving the odds of success.

    “Data profiling may reveal that the data on which the project depends simply does not contain the “Data profiling may reveal that the data on which the project depends simply does not contain the information required to make the hoped-for decisions,” explain Ralph Kimball and Margy Ross in their book, Relentlessly Practical Tools for Data Warehousing and Business Intelligence. “Although this is disappointing, it is an enormously valuable outcome.”

  • 2 Improving data quality

    Profiling can help companies ensure their data is clean, accurate, and ready for distribution across the enterprise. This is especially important when extracting data from paper and spreadsheet systems and databases where information was entered manually.

    By assessing data quality, project managers can determine whether the information can deliver its intended business outcome. At the same time, they can determine whether more data is needed before getting started.

  • 3 Enabling searchability

    In the agile organization’s age, employees need to locate specific types of data quickly and easily during projects. When data is unsearchable, it can be challenging to locate within a larger string.

    To improve discoverability, businesses tag and categorize their data so that users can locate individual items and sets within databases using specific keywords.

    It’s also necessary to discover and assess all metadata from within the source database. As such, to ensure accuracy and optimal discoverability, metadata should be thoroughly vetted and updated early on before launching any big data project.


DataOps Process: Drivers and Objectives of DataOps

Types of Data Profiling

There are many different ways a team of analysts can approach data profiling. For example, data can be profiled based on its overall quality, cybersecurity, credibility, lineage, and so on. But ultimately, data profiling can be broken down into three separate categories.

  • 1 Content discovery

    Content discovery involves analyzing data rows for errors and systemic issues. For example, this may involve reviewing a list of customers who don’t have valid email addresses.

  • 2 Structure discovery

    Structure discovery is necessary for making sure that data is formatted correctly and is consistent throughout a database. Structure discovery might entail checking a list of addresses for town names or zip codes, for example.

  • 3 Relationship discovery

    Relationship discovery is used to analyze data in use and identify relationships across spreadsheets or database tables. To illustrate, customer and order data is typically not stored in the same table in a database. Following a transaction, these two relationships would need to be discovered and linked to have any value.


Challenges of Data Profiling

The process of profiling data isn’t all that difficult. It’s something that a professional with intermediate data management knowledge should be able to accomplish—particularly when they have the right tools.

Issues related to data profiling are typically more systemic in nature. In many cases, they stem from a failure to have the right people and failure to use modern data tools. With that in mind, here are some of the challenges that businesses typically face when profiling data:

  • 1 Data volume

    Data profiling often requires working with massive datasets. When doing profiling tasks by hand, it can be tremendously time – and labor-intensive. For this reason, most businesses now leverage SaaS-based tools to automate certain elements of profiling.

  • 2 Resource allocation

    Simultaneously, profiling can require trained experts to analyze the results and make decisions based on the findings without the right tools in place. Data scientists and analytics professionals can be very expensive, as the average data scientist salary is now about $120,000 per year on average. This is why more and more organizations are turning to advanced data visualization and preparation tools.

  • 3 Data access

    To start the data profiling process, it’s necessary to have all of your data in a single location. Data is often difficult to locate in an enterprise setting because it tends to live across disparate departments and applications. Data silos—which affect the majority of businesses—can make data profiling very difficult.

    The good news is that a modern platform like Datameer can help businesses accelerate their data profiling initiatives. With Datameer, all data is consolidated into one virtual centralized hub, making it easier to process and manage. Learn more about how Datameer helps teams discover information, share and collaborate on insights, and publish reports.

Datameer SaaS Data Transformation

Datameer is a powerful SaaS data transformation platform that runs in Snowflake – your modern, scalable cloud data warehouse – that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics.  With Datameer, you can:

  • Allow your non-technical analytics team members to work with your complex data without the need to write code using Datameer’s no-code and low-code data transformation interfaces,
  • Collaborate amongst technical and non-technical team members to build data models and the data transformation flows to fulfill these models, each using their skills and knowledge
  • Fully enrich analytics datasets to add even more flavor to your analysis using the diverse array of graphical formulas and functions,
  • Generate rich documentation and add user-supplied attributes, comments, tags, and more to share searchable knowledge about your data across the entire analytics community,
  • Use the catalog-like documentation features to crowd-source your data governance processes for greater data democratization and data literacy,
  • Maintain full audit trails of how data is transformed and used by the community to further enable your governance and compliance processes,
  • Deploy and execute data transformation models directly in Snowflake to gain the scalability your need over your large volumes of data while keeping compute and storage costs low.
Data modeling low code icon

Data Profiling in Datameer

Datameer provides a rich array of data profiling features to give your users a comprehensive view on their data, including:

  • Automated visual data profiling, which provides data distribution information for each field to understand what the data looks like and identify data preparation needs such as cleansing and eliminating outliers,
  • System-generated recommendations, which use machine learning to examine the data and provide recommended actions on the data, such as how to perform joins and blends,
  • System- and user-generated data profile information, which includes documentation, properties, comments, tags, and more to provide further context and profile information on the data.

The system- and user-generated profile data also facilitates data discovery via Google-like faceted search.  This allows users to search and explore datasets and data models that meet certain profiles.

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