Time for a data check-up: how data profiling leads to high-quality data

You probably know about going to the doctor every given time to have a check-up of your overall health in order to identify potential or ongoing health risks for prescribing treatment if needed. If this has to be done regularly due to constant changes in our organism, why can’t this apply to business data as well?

Companies are ever-changing entities that develop at a fast pace. Practices that were essential to process data at the beginning may not be of any use at any given moment, even more so with the constant growth of volume: it becomes almost impossible to track the state of millions of data only with the human hand.

Fortunately, just as doctors have special tools to help them diagnose what needs to be done, there is a special practice for identifying issues in the information your business produces: it is known as data profiling, and we will get to the bottom of how it helps to achieve high-quality data.

What is data profiling and what does it entail?

Data profiling is an analysis technique that allows diving into the state of data through examining, cleansing, and monitoring its structure and relationships with other areas and datasets, as well as identifying inconsistencies, accuracy and overall errors to be corrected and further prevented: it is mostly used to know the condition of your data and what needs to be done to achieve high-quality data.

Benefits of data profiling

When adopting this method, you will find yourself enjoying some benefits of data profiling, which are the following:

High-quality data every day

Are you having trouble identifying data quality issues? Data profiling can help you accurately pinpoint missing fields, outliers, duplicates, and inconsistencies in your information to facilitate your data cleansing strategy. You will end up saving time and effort thanks to its speed and accuracy, regardless of the data volume.

A better comprehension of your data

Get to know your data better: a profiling process aids in understanding the distribution of values, the relationships between fields, and the occurrence of patterns or trends. By identifying and addressing issues early on, data profiling can help optimize your reporting and analysis according to their purpose, among other processes.

Easier searching and queries

Finding the best place to keep your data will save you an unfathomable amount of time since searching will take place in seconds when stored in the most suitable system: data profiling, while it helps reach high-quality data, it also makes it easier to locate information within a larger string.

Discovering the best data storage system

You have probably wondered more than once: where can you store your data, which is the best option? Data profiling can help you choose between different systems such as data lakes, warehouses, among others depending on the current state of your data and the future uses it will have.

Better and faster cleansing processes

If you didn’t know data cleansing could get easier, data profiling makes it possible: it identifies data quality issues that need to be addressed, such as missing values or duplicates, and can assist in the development of a data cleansing strategy to make it faster, better, and more efficient.

Enhanced decision-making

Having all your data with the highest quality, understanding its purpose, and storing it accordingly will bring you great relief regarding your decision-making process, since data will be available, clean, and accurate thanks to this method. This will also improve your governance strategy since it is ensured that data is of the highest quality possible and used within the rules and regulations of your business.

In case you were left wondering after the benefits mentioned before: data profiling vs. data quality, what do these have in common?

Thanks to profiling, reaching high quality doesn’t seem to be as out-of-reach as before, since it brings accurate insights about the structure of your data, its relationships with other data assets, the uses it has been given, and what kinds of transformations or cleansing methods might be necessary to ensure that it is always accurate, consistent, and fit for purpose. In other words, it provides all the necessary information to take action into improving your data.

Once the role of data profiling in data quality has been set, let’s take a look at how it can be related to other types of processes regarding data and its management.

Which are some data profiling use cases?

There are at least four other instances in which data profiling can bring great benefits to your data. Check them out:

Now that you know which areas benefit from it, it is time to see how data profiling works and its different types to see which would fit your needs better. Take a look!

First steps to start profiling data

As any new process to be undertaken, you must consider some essential data profiling steps before going into detail about a complete implementation.

1. Gather all your data

The first step to follow is gathering all your information from one or multiple data sources into one single repository, followed by the associated metadata for the next step.

2. Perform profiling techniques

According to Chandra, there are at least 3 profiling techniques you can use depending on your main goals:

• Structural analysis. If you wish to know if your data is consistent and formatted correctly, a structured discovery (or analysis) will suit you best, since it helps determine the validity and consistency of data by delivering simple statistics about the condition of your information.

• Content analysis. This one focuses on data quality: it discovers specific errors within individual data records and pinpoints inconsistencies when formatting and standardization are hindered by incomplete information.

• Relationship analysis. In order to discover the relationship between any given dataset, a relationship analysis helps to understand similarities within data workflows and the fields they are reliant on, as well as preserving relationships when moving or migrating data.

3. Validate your data

Once your data undergoes a profiling method, you need to ensure that it meets your requirements according to your rules and regulations, such as being in the needed format, being within a certain range, being consistent, and so on.

4. Monitor your data quality

All of the above needs to be sustainable in some way. Since it is not recommended to be a one-time process, it needs to be monitored at all time for achieving the best of qualities. You need a tool that connects to your sources of information, performs the profiling technique of your choice, and corrects and validates all of your data while ensuring safety and quickness throughout the process.

Is data profiling time-consuming?

Just like any process within data management, profiling has its challenges and downsides if it is not appropriately approached. Nevertheless, the most recurring issue regarding profiling is time: it can take a great amount of time when performed deficiently, manually, or without the proper tools to carry this method out.

In this sense, these are the following challenges regarding time and bad profiling:

• Large volumes of data. It may be possible for a person to keep track of 200 registers per day, but when this number grows exponentially up to thousands or millions, it becomes practically impossible for the human hand to cope with such volume. Hence, data profiling becomes hindered by delays, human errors, and other recurring issues regarding volume.

• Ensuring data quality. If done manually, profiling can take a significant amount of time to clean and prepare data for analysis, exploration, and reporting to acquire high-quality insights. Things like correcting errors, filling in missing values, or reformatting the data as needed become extremely time-consuming, which results in overall delays inside the organization.

• Keeping track of your data’s development. Documenting the findings during a profiling process can also be time-consuming, as it may require creating reports or visualizations to track the root cause for preventing further issues, which may become an issue itself.

Good news is that, even though profiling can seem like a significant time-consuming process, it can be quickly fixed using the right tools and approach, depending on your specific needs. In this sense, the following story is a real use-case on how profiling can result in complete success without much trouble.

Data profiling use case: choosing the best storage for your data

If you are still not quite sure you need a data profiling process for your business, this case will help you sort it out:

We have talked about how data profiling helps in deciding whether to use a data lake or a data warehouse for a particular project. Sometimes it is not too obvious or clear which platform will be the best option to use, since they are commonly mistaken to be the same.

Which is the best storage system for you? It depends on the state your data is in as of now: if it is mostly standardized with a structured format, your best option would be a data warehouse, since it stores ready-to-be-used information in its best shape. On the other hand, if you have issues with your data due to multiple errors, duplicates, it is spread among different sources, and it is not standardized, your best option would be a data lake: this storing method allows you to have your data as it is in one single source for mining, transforming, extracting, and cleaning it later on. In this sense, data profiling helps you know exactly what you need according to the state of your data.

By performing a profiling technique, you can get a better understanding of the characteristics of your data, such as its structure, volume, and complexity, to finally determine which type of system is best suited to handle your information and support the specific cases and projects you have in mind.

This was the case of a company that had to choose between a storage system when spreadsheets were not able to process large amounts of information: the quantity of information grew at such a fast pace that they found it difficult to determine how to deal with the magnitude of datasets and where to place them safely.

The solution they found was conducting data profiling. It helped them to explore their datasets, to leverage the challenge to be tackled and make the decision of changing the way they manage their workflows through the use of a specialized tool.

Which data profiling tools do you need? This is what you can use

There are several ways for you to improve your data quality, and profiling it is one of the best, most complete solutions out there, and it can only be achieved through the use of a platform to do so.

Let our robust infrastructure and user-friendly platform help you easen your approach to a data profiling system. You can do the following:

• Get complete profiling of your datasets by connecting all your different sources to our platform.
• Save time identifying each data type and its particular formats for each field in a dataset.
• Detect missing or null values, regardless of the volume of data you are managing.
• Identify patterns and trends in your data to prevent and avoid further issues and errors.
• Obtain a complete overview of the state of your data by gaining basic statistics, such as min, max, mean, and standard deviation.
• Find errors or inconsistencies in your data based on your rules and regulations.

Your company can integrate and manage all the information it produces to reach high-quality data with the help of our platform, all in one place and at your pace. 

Let’s get to know your needs better: get in touch with us to listen to your particular requirements to help you begin a process of data profiling for the benefit of your data quality.