Over the last few years, big data has changed from a competitive differentiator into a critical tool for growth and development. When used to its full potential, data can increase profits, reduce overall spending, and uncover opportunities. This is the reason why more than 97 percent of organizations are now investing in big data and artificial intelligence initiatives.
In its raw form, however, big data is largely unusable. It needs to be prepared, processed, and analyzed for both quality and content. It also needs to be summarized before it can help a company improve its operations.
As such, businesses perform data profiling to better understand the condition and the value of their data, making it discoverable and actionable along the way.
Very simply, data profiling is the process of analyzing data to understand how it’s structured, what it contains, the relationships between data sets, and how it could potentially be used most effectively.
Organizations are increasingly using data profiling because it can improve many processes across the enterprise by delivering a number of benefits, which we’ll explore next.
Prior to 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.”
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 is capable of delivering on its intended business outcome. At the same time, they can determine whether more data is needed before getting started.
In the age of the agile organization, employees need to be able to locate specific types of data quickly and easily during projects. When data is unsearchable, it can be very difficult 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 prior to launching any big data project.
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.
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.
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.
Relationship discovery is used to analyze data that’s 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.
The processes 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 failure to have the right people in place and failure to use modern data tools. With that in mind, here are some of the challenges that businesses typically face when profiling data:
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.
At the same time, without the right tools in place, profiling can require the use of trained experts to analyze the results and make decisions based on the findings. 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.
In order 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, though, 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 Neebo can help businesses accelerate their data profiling initiatives. With Neebo, all data is consolidated into one virtual centralized hub, making it easier to process and manage. To learn more about how Neebo helps teams discover information, share and collaborate on insights, and publish reports, check this out.