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Max Lukichev

Whether you onboard 3rd party data or curate data for your customers, internal or external, Data Quality is one of the most important considerations. Selling data of poor quality will inevitably result in reputational losses, negatively impacting your business. Whereas onboarding bad data could ruin your other data initiatives, corrupt downstream systems and lead to inaccurate analytics, which in turn leads to business losses.

Ensuring good quality of data requires implementing robust practices in the following areas:

Understand your data, specifically the unknowns or “blind spots”
Monitor your data - proactively alert when data deviates from norm
Build your data platform to leverage data confidence score so the bad data and good data are treated differently

We’ve spoken about the importance of data monitoring extensively, let’s now take a look at the first step - understanding the data.

Understanding data

For years, one of the most handy tools for data experts was profiling. There are a number of free, open source and commercial products for data profiling available in the market.

Data Profiling is a process of analyzing data and summarizing this information in the hope of assessing quality of the data. For example, finding what are the attributes in the dataset, what is the distribution of values, top or least frequent values, percentage of populated and unique values etc.

The consumption of profiling reports is manual, time consuming and quite honestly, boring. It becomes even more challenging when reports provide too much detail so it quickly becomes overwhelming. On the other hand, if it’s very high level, it would be hardly useful as it will uncover only a few of the most visible problems (Figure 1).

Figure 1: Out of the box Spark Dataframe profiling

Now, if you multiply it by drastically increased volume and velocity of data in recent years, it becomes clear that profiling in its classic interpretation is no longer up to the task. So are we doomed? Fortunately not.

Introducing the Telmai Profiler++

It’s clear that just throwing more information at people in the hope that it will solve data quality concerns is not going to work. Fully relying on ML to detect and act on data issues also doesn’t seem very feasible as there is a tremendous amount of knowledge and context about data in the heads of data experts accumulated for years working in specific data domains.

So we’ve combined ML and the intuitive and seamless experience of data experts together to bring to you Profiler++. ML does what it does best - crunching through tons of statistical data and bringing up the most valuable information. Whereas the experts can shine at what they do best - applying all their knowledge in making decisions based on that information.

Telmai’s engine processes data, collects tons of statistical information, significantly more information than a typical profiling tool would, which is then fed to the ML engine, where all of this information is analyzed and then brought to the user via fast, interactive and engaging user experience. So instead of reading a thick folder of statistical reports, it’s like watching a movie which tells you a story about your data.

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Data profiling helps organizations understand their data, identify issues and discrepancies, and improve data quality. It is an essential part of any data-related project and without it data quality could impact critical business decisions, customer trust, sales and financial opportunities.

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To get started, there are four main steps in building a complete and ongoing data profiling process:

Data Collection
Discovery & Analysis
Documenting the Findings
Data Quality Monitoring

We'll explore each of these steps in detail and discuss how they contribute to the overall goal of ensuring accurate and reliable data. Before we get started, let's remind ourself of what is data profiling.

What are the different kinds of data profiling?

Data profiling falls into three major categories: structure discovery, content discovery, and relationship discovery. While they all help in gaining more understanding of the data, the type of insights they provide are different:

Structure discovery analyzes that data is consistent, formatted correctly, and well structured. For example, if you have a ‘Date’ field, structure discovery helps you see the various patterns of dates (e.g., YYYY-MM-DD or YYYY/DD/MM) so you can standardize your data into one format.

Structure discovery also examines simple and basic statistics in the data, for example, minimum and maximum values, means, medians, and standard deviations.

Content discovery looks more closely into the individual attributes and data values to check for data quality issues. This can help you find null values, empty fields, duplicates, incomplete values, outliers, and anomalies.

For example, if you are profiling address information, content discovery helps you see whether your ‘State’ field contains the two-letter abbreviation or the fully spelled out city names, both, or potentially some typos.

Content discovery can also be a way to validate databases with predefined rules. This process helps find ways to improve data quality by identifying instances where the data does not conform to predefined rules. For example, a transaction amount should never be less than $0.

Relationship discovery discovers how different datasets are related to each other. For example, key relationships between database tables, or lookup cells in a spreadsheet. Understanding relationships is most critical in designing a new database schema, a data warehouse, or an ETL flow that requires joining tables and data sets based on those key relationships.

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1. Data Collection

Start with data collection. Gather data from various sources and extract it into a single location for analysis. If you have multiple sources, choose a centralized data profiling tool (see our recommendation in the conclusion) that can easily connect and analyze all your data without having you do any prep work.

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2. Discovery & Analysis

Now that you have collected your data for analysis, it's time to investigate it. Depending on your use case, you may need structure discovery, content discovery, relationship discovery, or all three. If data content or structure discovery is important for your use case, make sure that you collect and profile your data in its entirety and do not use samples as it will skew your results.

Use visualizations to make your discovery and analysis more understandable. It is much easier to see outliers and anomalies in your data using graphs than in a table format.

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3. Documenting the Findings

Create a report or documentation outlining the results of the data profiling process, including any issues or discrepancies found.

Use this step to establish data quality rules that you may not have been aware of. For example, a United States ZIP code of 94061 could have accidentally been typed in as 94 061 with a space in the middle. Documenting this issue could help you establish new rules for the next time you profile the data.

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4. Data Quality Monitoring

Now that you know what you have, the next step is to make sure you correct these issues. This may be something that you can correct or something that you need to flag for upstream data owners to fix.

After your data profiling is done and the system goes live, your data quality assurance work is not done – in fact, it's just getting started.

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Data constantly changes. If unchecked, data quality defects will continue to occur, both as a result of system and user behavior changes.

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Build a platform that can measure and monitor data quality on an ongoing basis.

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Take Advantage of Data Observability Tools

Automated tools can help you save time and resources and ensure accuracy in the process.

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Unfortunately, traditional data profiling tools offered by legacy ETL and database vendors are complex and require data engineering and technical skills. They also only handle data that is structured and ready for analysis. Semi-structured data sets, nested data formats, blob storage types, or streaming data do not have a place in those solutions.

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Today organizations that deal with complex data types or large amounts of data are looking for a newer, more scalable solution.

That’s where a data observability tool like Telmai comes in. Telmai is built to handle the complexity that data profiling projects are faced with today. Some advantages include centralized profiling for all data types, a low-code no-code interface, ML insights, easy integration, and scale and performance.