Data Profiling: What It Is & Its Importance To Your Business
As your business continues to expand, your data will continue to play a vital role in helping you make sound business decisions. Your data will inform everything from sales and marketing to customer service, so performing data profiling will become more and more significant– especially as your data sets grow bigger and bigger everyday.
What Is Data Profiling?
Data profiling is a process in which your data is carefully examined and analyzed for inconsistencies. The process involves determining how correct your data is, how credible your data is, what are the sources and calculation methods, verifying the security of your data, and ensuring that your data set is in compliance with all rules and regulations concerning privacy laws. This is all done through the use of analytical algorithms that detect the characteristics of your data set, such as the mean, minimum, maximum, frequency, and percentile, in order to evaluate the quality of your data.
Why Is It Important?
Managing all of the data that your organization continues to collect will only become more challenging the bigger your company grows. Data profiling is important because it helps to track the quality and consistency of your data; for example, by checking whether the information in your tables actually matches the corresponding descriptions.
Data profiling will help you make sure that your data is properly formatted, standardized, and integrated. If it’s not, it can result in missed sales opportunities. For example, if your data isn’t properly standardized, you may have customer addresses that spell out the name of the state where they are living as well as addresses in which the name of the state is identified by a state abbreviation. When attempting to contact customers in a certain state, you could miss a significant number of them when performing a query as a result of a lack of standardization. Missing or inaccurate information can result in missed sales opportunities as well.
Benefits For Your Business
The following is a quick breakdown of the benefits of data profiling for your business:
Better Quality And Credibility
The quality of your data basically refers to whether it’s correct or not. Data profiling can determine the quality of your data by comparing two different sources for the same data or by using a set of business rules to validate the data. Business rules are a series of conditional statements that represent the processes, policies, and procedures of a business in the form of text or a decision tree. As for the credibility of the data, this doesn’t refer to the accuracy of the data itself but to the source of the data. For example, you can identify whether social media engagement over a certain period of time has a direct correlation on product sales.
Predictive Decision Making
By verifying that your data is accurate and is coming from a credible source, it can influence your decision making process in a more positive manner. You’ll be able to use your data more accurately to reveal possible outcomes for new scenarios, which requires accurate data based on current and historical facts to perform.
Quickly Identify Problems
The use of data profiling can help you flag data that’s inconsistent or inaccurate before it’s leveraged to make important business decisions. By identifying problems in your data set, you reduce the risk of making poor decisions based on inaccurate data.
Your data set or data warehouse collects data from numerous sources. These sources can include your website, various social media sites, blogs, and other big data markets. Data profiling will allow you to organize the data you have access to in several ways. For example, you’ll be able to trace the data to its original source and to make sure that it’s been properly encrypted. You can also use data profiling to analyze the different databases, tables, and source applications to make sure that the data meets your specific business rules and standard statistical measures.
All of this will help to keep your data organized and provide you with a better understanding of the relationship between your available data, required data, and missing data. This knowledge will make it a lot easier to implement future strategies and to identify long-term goals.
Areas Where Data Profiling Plays A Crucial Role
Data profiling is especially important in these critical areas:
- Data Warehouse And Business Intelligence Projects
Your data warehouse stores a significant amount of data collected from multiple sources that is then used by your BI (business intelligence) tools to transform into actionable insight. Running data profiling on your data warehouse for potential data quality issues can help you pinpoint what you will need to correct using ETL (extract, transform, load).
- Data Conversion and Migration Projects
With data profiling, you’ll be able to pinpoint issues with data quality. You can address these issues using scripts and data integration tools as you’re copying data from the source to the target. You’ll also be able to use data profiling to identify new requirements for the target system.
- Source System And Data Quality Projects
In addition to identifying data with quality issues, you’ll be able to use data profiling to identify the source of those issues; for example, interface errors, corrupt data, or user inputs.
The following are the major functions of data profiling:
- The ability to perform a thorough evaluation of the quality of your data.
- The ability to collect different types of data, data length, and recurring patterns in your data.
- The ability to collect descriptive statistics, including min, max, sum, and count.
- The ability to accurately tag your data with the proper categories, descriptions, and keywords, thereby improving your ability to search through data.
- The ability to discover metadata and to identify where metadata is missing. This includes key candidates, functional dependencies, foreign-key candidates, and value patterns and distributions.
- The ability to evaluate how accurately your metadata describes the values in the source database.
- The ability to perform an inter-table analysis.
- The ability to determine whether your existing data can be used for other purposes.
- The ability to assess how much risk will be involved in integrating your data in new applications.
There are three main types of data profiling techniques that you can implement. These include structure discovery, content discovery, and relationship discovery.
1. Structure Discovery
Structure discovery analyzes your data set to make sure that the data is consistent and correctly formatted. Structure discovery can be done several ways. One of the more common methods of structure discovery is through pattern matching, which allows you to identify a wide range of format-specific information, such as whether a field is text or number-based. Additionally, you can use structure discovery to gain insight into how valid your data is by using statistics such as the minimum and maximum values, the medians, the modes, the means, and standard deviations.
2. Content Discovery
Content discovery analyzes the quality of your data by identifying incorrect or ambiguous values or null values. Fixing such problems depends greatly on the standardization process. For instance, the process may include finding and correcting data to fit email addresses into the correct format. If these email addresses weren’t properly formatted, it could cause a lot of issues, such as the inability to contact customers by email. As you can imagine, this can be quite costly, which is why it’s important that you use content discovery to fix non-standard data.
3. Relationship Discovery
The last data profiling technique used is relationship discovery. Relationship discovery is a process that uses metadata analysis to identify important relationships between your data. Running relationship discovery will help narrow down the connections between specific fields as well, especially where your data overlaps. By running relationship discovery, you can reduce some of the challenges that often occur when your data isn’t aligned in your data warehouse.
Easy 4 Step Process
Ralph Kimball, an expert on BI and one of the original architects of data warehouses, recommends implementing a four-step process when it comes to data profiling:
- Determine whether a project is worth pursuing by using data profiling at the very start. Data profiling will help you to determine whether the data you have is suitable for analysis.
- Before moving your data into the target database, identify data quality issues in your source data and make sure to correct it.
- Determine what data quality issues can be corrected by ETL as your data is moving from the data source to the target database. The use of data profiling in this instance can help you determine whether additional manual processing will be required.
- Fine-tune your business rules as well as your ETL process by using data profiling to identify all hierarchical structures, including foreign or private key relationships.
You can choose between two types of data profiling techniques: basic data profiling techniques, involving column-level analysis, or more advanced techniques.
Basic Column-Level Analysis
Basic column-level analysis can be performed by using one of these techniques:
- Distinct Count and Percent
This technique allows you to identify unique keys within the source data. Being able to identify these keys is essential for processing inserts and updates. Although this information can be easily found by the source column name or even through discussion with source data owners, this may not always be possible, in which case you’ll want to perform a distinct count and percent analysis.
- Zero, Blank, And Null Values
This technique is extremely important as it can help you identify data that is unknown or missing. This information can help your ETL architects to set up the appropriate default values.
- Minimum, Maximum, And Average String Length
This technique lets you choose the right data types and sizes in the target database. It can help improve performance as well since you’ll be able to reduce the width of your columns so that they are just big enough to meet current and future requirements. This way you’ll minimize the table scan time and improve your query performance.
- Numerical And Date Range Analysis
This technique, which collects information on minimum and maximum numerical and date values, will help you balance your data storage and performance requirements by identifying the proper data types.
The following are a few more advanced data profiling techniques:
- Key Integrity
Using zero/blank/null analysis, key integrity will help to ensure that keys are present in your data. Key integrity can also prevent issues with ETL as well as future analysis by helping to spot any orphan keys.
Cardinality is a technique used to inspect the relationships between related data sets, such as one-to-one and many-to-many. This helps improve the ability of your BI tools to properly perform inner or outer joins.
- Pattern, Frequency Distributions, And Domain Analysis
This technique will allow you to make sure your data fields are properly formatted. Correct formatting is very important when it comes to outbound communication.
Data Profiling With The Cloud
The number of available data sources has been increasing significantly over the past few years. For example, the Internet of Things (IoT) has resulted in everyday devices being capable of collecting potentially valuable data. It’s why many businesses are turning to the cloud to increase their data storage capacity. As more and more companies move to the cloud, data profiling has become even more important. Data profiling is a must if you’re planning to harness the data being collected through the cloud. Not only will you need to rely on data profiling to ensure that your data is accurate, properly formatted, and coming from credible sources, but you’ll need to use it to maintain compliance standards as well.
Managing The Profiling Process
Considering the amount of data that you’ve already collected and will continue to collect, data profiling is incredibly time-consuming and labor intensive if done manually. Fortunately, the data profiling process can be automated through the use of any number of tools. Some of the data profiling tools to consider adding to your data analytics stack include commercial tools such as Informatica, SAS DataFlux, and Oracle Enterprise Data Quality. There are also several reliable open-source data profiling tools available as well, including Talend Open Studio, Quadient DataCleaner, and Aggregate Profiler.
The data you have access to is one of your company’s biggest assets. That data allows you to make more informed business decisions and to develop more successful marketing and sales strategies throughout the future. However, all of this depends on the quality of your data, which is why data profiling is absolutely essential.
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