What is Big Data Profiling and How Does It Ease the Challenges of Digital Transformation?
Many organizations are adopting digital transformation initiatives to better understand their customers. With these initiatives also comes the gathering of data, including demographics, buyer behavior, and buyer preferences.
However, data gathering only ends up impacting the bottom line when it leads to auspicious business results. Thus, many organizations are looking to big data profiling, a visual assessment that uses a toolbox of business rules and analytical algorithms to discover, understand, and expose inconsistencies within data sets. The knowledge used from the data within these sets is subsequently used to improve data quality while continuously monitoring and maintaining the integrity of such data.
A market poised to explode
The big data profiling market is poised to explode with the amount of data being created on a moment to moment basis. From the sharing of personal data between patients and doctors in the medical sector to the gauging of customer sentiment in retail, a multitude of devices and applications participate in the data creation process. Furthermore, just having data is not enough, since, with millions of records, quality matters too.
If you’re looking to make sense of your organization’s customer interactions, adopting a big data profiling solution may be of interest. In this blog post, we explore data profiling in depth, the opportunity it presents for organizations, and how professionals can quickly get started.
What is big data profiling?
Big data profiling is a visual assessment using a toolbox of business tools and algorithms to discover, understand, and expose inconsistencies within data sets. Within these tools, end users are also subject to industry best practices around how they handle their data, matching data handling with existing business rules that might exist within business process management (BPM) solutions. Data profiling is also capable of matching the high-level description of data with key metadata, revealing relationships that incumbent methodologies might miss.
Big data profiling exists in several different categories. These include:
- Structure Discovery – For data to be practical, there needs to be validation of its consistency and format. Structure Discovery helps achieve this validation through pattern matching, which helps read the type of data within a specific field. Structure Discovery also examines basic statistics within data, including minimum and maximum values, averages, standard deviations, and more.
- Content Discovery – Many data management activities begin by examining inconsistencies and ambiguous entries within a data set. This ensures the avoiding of data errors, which may prevent an organization from being able to reach customers due to bad addresses. Known as Content Discovery, this involves checking detailed data and making the necessary fixes.
- Relationship Discovery – In some cases, there is data already in use. Under these circumstances, big data profiling helps to understand the relationships between data and narrows down between specific fields, especially where data overlaps.
With an idea of what kinds of data profiling exist, there are also various techniques used in the industry. These include:
- Column profiling, which scans through an existing table and counts how many times a data value shows up within a specific column.
- Cross-column profiling, which uses key analysis and dependency analysis. Key analysis looks for a common attribute value through a primary key while dependency analysis determines the existence of a relationship or structure between data sets. Both dependency analyses look at data within the same table.
- Cross table profiling, which uses foreign key analysis to cut down on data redundancy and identify data value sets that can be mapped together. Analyzed data often may not map back to a primary key, with meaning and structure to be determined.
- Data rule validation, which proactively uses big data profiling to verify that data instances and sets are following the pre-defined rules. This process is primarily used to evaluate data quality and identify areas of improvement.
Learn more about Accelerite
We’re at the intersection of data and understand the organizational desire to improve business processes, customer interaction, and business decision making. With customers across a variety of industries, we understand how big data profiling and analytics is helpful for gaining control of data quality in the era of digital transformation.
ShareInsights on Hadoop and AWS makes it easy for anyone to explore, transform, and visualize big complex data lakes in minutes. It’s the most comprehensive platform for accelerating time-to-insight and will help you create a truly digitally connected organization. Learn more about future proofing your analytics with ShareInsights here.