According to Statista, the data analytics global market has been forecast to reach $70 billion and more by the year 2025. It is shown in another report that the data volume consumed, copied, captured, and generated all over the world is expected to be 182 zettabytes in the same year. When you consider the rise in data volume, it’s imperative for businesses to ensure data quality and leverage that data to drive their decision-making processes.
A poll by Statista found that 45% of organizations surveyed in Europe and the United States have listed that their employees don’t have analytical skills, which is proving to be the main challenge in using data for driving business value in 2022. To stay competitive in the marketplace and achieve greater confidence for their data sets, organizations are turning towards data profiling as the core ingredient for their overall data management strategy.
What Is Data Profiling?
The assessment of data is known as data profiling, using a combination of business rules, tools, and algorithms to create high-level reports of the condition of the data. The main goal of data profiling is to uncover missing data, inconsistencies, and inaccuracies so that data engineers can do their investigation and correct the sources.
The reports of data profiling are generally graphs and visualizations with tables that display the relevant metrics, like the duplication degree in the set of data. Furthermore, data profiling isn’t only meant to be used to troubleshoot risks to data integrity and data quality. It can also become a core discovery process used by analysts for uncovering the relationships, structure, and content between different sources of data.
To sum it up, data profiling helps create a profile of the quality and state of the data. The collected information for building this profile involves metadata like data length and type, generated statistics related to the data set, and dependencies between the tables. Data profiling also involves tagging the set of data like assigning categories and keywords for making the data searchable and speeding up future analysis.
Now that you know what data profiling is, let’s share a couple of examples to define data profiling in a more expansive manner.
Examples of Data Profiling
There are many use cases of data profiling within organizations that seek to better maintain and understand their data. Here are some examples to better paint the picture:
When a data warehouse is created by a business, its goal is to collect data from different sources and store it in standardized formats, where it can be easily accessed to be analyzed. However, if the data quality is poor, then gathering all of it in one location doesn’t solve the bigger issues, as you get bad decision-making from bad data.
When you incorporate data profiling in the workflow of the data warehouse, it offers a check against data of low quality. As the collection of information is done via ETL processes, you can use data profiling to validate the integrity of the data and comply with data rules during or before the intake processes. Now your business has centralized data, which you are confident can be reliable for making informed decisions.
Mergers and Acquisitions
Let’s assume that your business is merging with your competitor. You’ll then have access to a wealth of data that is entirely new for discovering new customers and finding new insights. However, you’ll first have to integrate all that data with the data that already exists. Data profiling offers a higher-level overview of the newer data assets made available, along with their dependencies.
Here, data profiling can be used to identify data that has been duplicated between the two systems and can show where the format of the newer data is different so that your data teams can work on standardizing it. Now your data is ready to be merged and cleansed into one place.
Data Profiling Benefits
Apart from offering an improvement in data visibility and quality, data profiling provides concrete benefits to businesses, which include the following:
Improved Data Confidence
Data profiling can be helpful for data analysts and engineers in identifying and correcting issues. That process ensures greater confidence in the conclusions that are drawn from the sets of data. Also, data profiling ensures teams can identify the major causes of problems, so they can be corrected during the process of data collection.
Engineers tend to improve the searchability of their sets of data when they tag them with categories, keywords, and descriptions to make them easier to discover. That streamlines future analysis where this data is incorporated and also offers non-technical users access so that they can use search terms to query data sets.
Decision Making that is Predictive
The advanced use cases of data like machine learning and artificial intelligence tend to rely on properly formatted and standardized data for powering their algorithms. Engineers can use data profiling to enforce these standards better, while the data sets are also validated for accuracy, ensuring that erroneous conclusions aren’t being drawn by these technologies.
Types of Data Profiling
There are three main types of data profiling, which are: relationship discovery, structure discovery, and content discovery. We shall be reviewing all of these in detail below:
The data values scope is broadened by relationship discovery for cataloguing the links between the tables and records. That includes references in a table, like the value of a cell populated by calculating the other values of the cell or references between data sets and tables, like keys that are primary and foreign.
Such connections are vital for tracking and cataloguing so that you maintain data integrity if you duplicate or import the data set to another database. On the other hand, if the data has been sampled, the calculated values can persist if their argument isn’t a part of the cross sections.
The process of validating whether data is properly formatted and consistent is structure discovery and is also known as structure analysis. Pattern matching is the standard technique used for structure discovery, where data engineers will test records against patterns that are known for types of data. For instance, pattern matching can be used for scanning columns of email addresses to confirm if all of them have “@” and end in domain suffixes.
You can also calculate basic statistics with structure discovery around numerical data like standard deviation, and mean, median, and mode. These statistics aren’t only useful metadata for analysis in the future but also indicate data validity. For instance, you can spot outliers for further investigation or an average that is unusually high.
In content discovery, the engineers will place all their attention on the data values to identify whether there are any errors in the records. Content discovery will look for issues that are obvious, like missing values and problems that are nuanced, like data that is ambiguous or incorrect. For instance, there may not be a street address in some records.
The street address may be given in others, but the numeric values are spelled out (Eastern Street), while there may be some with numerals (Eastern Street). That may appear innocuous, but if you’re shipping to customers and then find out that the carrier accepts only numeric formats, then your business will be impeded by improper data.
Data profiling can improve data-driven decisions. IT Chronicles have revealed that less than 30% of organizations believe that they have managed to achieve a culture driven by data. Most companies face the challenge of analyzing their data effectively and ensuring that they have confidence in the end results. These issues are addressed by data profiling as they check the data for accuracy and consistency before analyzing them.
The information is helpful for engineers in solving issues proactively with the transformation and intake of data and allows confidence of a higher degree in the conclusions as there is validity of data. Data profiling holds the key strategy for building businesses that are data-driven.
Marc-Roger Gagné MAPP