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Data Observability and Metadata Observability - Same Problem, Different Solutions

Farnaz Erfan

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What do you think the two shapes above have in common?

Would you have guessed that they have the same perimeter? Well - they do!

The perimeter of these shapes are the same, but they are not the same shape, they don't have the same dimensions, their angles are not the same size, and colors are different. In fact, one of them has a very small dot embedded inside it, not even noticeable at first glance.

This is exactly what Data Observability and Metadata Observability have in common and also not so in common.

Data observability is the degree of visibility you have into the data at any given point. Data Observability knows exactly what goes on inside the data, its state, its shape, form, value, uniqueness, and changes it has seen through time.

This full picture of data is not and will not come from its metadata.

Metadata - being data about data - doesn’t exactly know what is inside. It only infers information about the data, given limited facts about it. Any SQL database readily provides this information about its tables and federated views. For example, the numbers rows in a table, the last it was updated, the range or min/max values in its various columns, its primary key, and whether the table saw some schema change, such as columns that were dropped or added.

Metadata Observability in the analogy above only gives us the perimeter of the data.

Let’s look at an example. Would a table that was updated in the last hour indicate that its data is fresh and reliable? What is the barometer to determine freshness? Is it only a timestamp? Maybe, maybe not. What if I tell you that just a few rows were updated and not the whole table? What if the table was updated but it collected some garbage? Is the data in the table fresh, and is it reliable?

While Metadata Observability looks at data about the data, Data Observability on the other hand looks at the actual data itself, and its values. It is able to validate the accuracy of the data. It can identify that the data has drifted during an update and that the number anomalies have increased, or decreased.

Additionally, metadata can not be the observability gauge for complex datasets such as semi-structured sources, streaming data, data directly coming from an application, or data retrieved by APIs. These data sources do not conform to a data model and often lack proper metadata.

While both types of observability platforms have their own use cases, a clear understanding of the differences between Data Observability and Metadata Observability helps in choosing the right tool for the right use case and setting the right expectations. And some platforms like Telmai actually offer both.

Data Observability	Metadata Observability
When you need to monitor all your data across every stage of the pipeline.	When you only want to monitor your data warehouse and other structured databases.
When data accuracy and validity is critical to your business.	When a peripheral view of data recency and volume levels gives you a piece of mind.
When you need to monitor the actual data values to detect anomalies and drifts in your data.	When you can rely on only metadata updates to only see schema changes in your operational data stores.

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Curious to see what Data Observability can do for your data? Try Telmai for free.

Connect your data and monitor it fully in just a few clicks.

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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.