There is a lot of talk about broad aspects of data science and data engineering, but few mention the importance of quality data and the processes behind it. Data observability is a term that handles data behind the scenes. From infrastructure to data movement, it’s integral to provide an unobstructed flow of information.
Data observability helps understand and manage data health, movement, and quality. It uses a wide spread of technologies and activities that allow you to identify data issues and bottlenecks in real time and prevent data downtime. When we look at complex infrastructures and data architecture, we can easily see why there needs to be a discipline in managing data across all data management tools, technologies, and organizations.
Why data observability?
You might think that if you have quality infrastructure and data collection tools in place, you won’t need to monitor data as often. That might be true in some cases, but with the speed and volume of data these days, issues might arise sooner than you think.
Data observability allows teams to continuously monitor their systems and analyze how data interacts with all aspects of the IT infrastructure. This helps them to identify errors and issues they weren’t aware of and apply improvements to stimulate more effective data traffic. By monitoring data, teams across the whole organization can optimize processes and have set in procedures that simplify future data science and data engineering projects. Data observability leads to faster mean time to detection (MTTD) and mean time to resolution (MTTR) when issues occur.
But, let’s face it, most organizations fail to turn their focus to data and how it moves. Going with predefined notions and thinking that data needs only monitoring is a recipe for failure in the long run. If it works now just means that it might not work in the future. With the way technology changes and how IT infrastructures change shape, most forget to, let’s say, update their data structure and pipelines.
The pillars and basics of data observability
For data observability to work, there are 5 pillars that contribute to its process, as defined by Moses:
This applies to the freshness of data and how up-to-date it is. It prevents data from becoming stale, which could lead to wrong metrics, insights, and conclusions. Basing your decisions on old data leads to mistakes and loss of time and money.
It’s no secret that what you do with data and what you get from it, depends on the quality. All the infrastructure might be in working order, but that doesn’t guarantee that the data itself is good. The quality pillar makes sure that your data can be trusted and therefore utilized properly.
Data tends to go from 0 to 100 quickly, meaning that the volume and speed of data can change instantaneously. Data observability gives you those indicators that the volume has changed and that once low data quantity has become bigger.
Changes in data schema and how it is organized can often result in broken data and cause data downtime. It’s important to be aware of these changes in time, so companies can prevent data obstructions and issues.
In case of data breakage, you want to know where it happens. Data lineage answers that and tells you which upstream sources or downstream ingestors were impacted and where the data came from, i.e who controls it. The data lineage process pinpoints exactly where the problem is when data breaks. It also collects information about said data or metadata which helps with governance.
Benefits of data observability
When we talk about data observability, some benefits can be summed up in these:
Discovery of data errors and issues before they occur
Data observability is here to ensure that errors and issues in data and its infrastructure can be detected before time. Being one step ahead allows for data to move more swiftly and smoothly. It also makes sure that bad data is detected on time before it even has the chance to end up in your data warehouse, for example.
Minimized data downtime and a more stable data environment
With data observability, one can minimize the time spent detecting and fixing errors. With the usage of machine learning models, environments can be learned and made more stable. This means that, in the future, responses to changes can be quicker and more effective.
Timely delivery of high-quality data to all users across different organizational levels
This means that data is continuously observed so the quality of it is much higher and more easily distributed across different organizational levels. With a more stable data infrastructure, it’s far easier to give access to data to more users. This is where data democratization comes into play. With a higher data quality, insights and decisions made are therefore more quality and accurate.
Troubleshooting and resolving issues faster and on time
Observability allows for quicker eros and issues detection. If there is a breakage in the data flow, it’s will be faster to identify where it happened and how so recuperating processes can start on time. Having a complete overview of data flow and architecture means a much better response time in any troubleshooting efforts.
Greater operational efficiency and more efficient monitoring and alerting
Data observability provides a way for data monitoring and alerting when changes occur. If there are infrastructures and monitoring tools set in place, operations can consequently be smoother and more efficient. If everything works and issues can be detected on time, all other operations will have less chance to encounter errors.
Increased collaboration between different departments and roles
We already established that data democratization and data observability go hand in hand. If data, and the right one, has an interrupted flow among departments, teams and people can collaborate more efficiently and exchange findings that matter. They can trust data that serves as a base for their daily operations. Data transparency is important when different teams have to make joint decisions.
Enhanced trust in data for more confident decision-making
rust and confidence in data and information are extremely important. If someone bases decisions on bad data, it can inflict far greater negative consequences than expected. With data observability, users get a certain level of confirmation that data is correct, actual, and good for further usage. Users can form their actions and strategies on a more confident level because they know that the insights drawn from data are accurate.
Not many consider data observability as something integral to be implemented in their company or institution. Most of the time, they trust their infrastructure and technology to just do their job. What they lack is an understanding that time, technology, and data can change instantaneously. Data observability will take an important place in business strategies as something that needs to be a part of business processes. Companies, that are already encountering huge amounts of data, are facing difficulties in handling it. Observability and the health of data, infrastructure, and technology are vital to creating and carrying out high-quality and stable data strategies.