Looking to the year ahead, demand is a key focus: Demand for versatile data management tools, demand for data observability, and customer demand on...
Let's Talk Data Observability
Optimizing data observability enables organizations to evaluate and understand their business data continuously. Here's what to know about observability.
Every year, new buzzwords seem to permeate discussions among visionary business leaders and forward-thinking decision-makers striving to get the upper hand on their competition. Last year, one phrase that was on everyone's mind was "data observability."
The concept of data observability is everywhere right now - and not just in our 2023 predictions piece.
Data observability has been praised for improving data health and making data engineers' lives easier. Over the next few years, the data observability market is projected to experience a staggering compound annual growth rate (CAGR) of 60%. At this rate, it will reach a value of $2 billion by 2026.
With all the hype around data observability, it can be difficult to discern what this concept means.
Fortunately, Syniti industry experts Rex Ahlstrom and Matt Wagnon recently tackled this intriguing topic, covering everything from the basic definition to how a data observability platform can optimize the health of your data. Here are some of the insights the duo provided.
What Is Data Observability?
Gartner describes data observability as:
"an adaptation of the observability concept from the DevOps world, but in the context of data, data pipelines and data platforms. It considers data issues an engineering problem and strives to empower data engineers to provide accurate and reliable data to consumers and applications within expected time frames."
Data observability is the process of "being able to observe things automatically and allow your solution to be up and running," according to Matt Wagnon. It involves "data monitoring, anomaly detection. It requires automation and Artificial Intelligence (AI)."
He explains that a modern data stack that facilitates observability will "proactively alert you if something goes wrong," promoting true end-to-end reliability.
Bad data can impede business growth and threaten continuity. Data observability aims to strengthen your data and analytics processes by enabling you to evaluate it on an ongoing basis. You can leverage these insights to improve data quality, reliability, and distribution, all of which are essential to the success of your business.
You can create a detailed data lineage by implementing observability solutions and processes. A lineage outlines where data originates and what pathway it follows as it's distributed throughout your organization.
Creating a data lineage allows your organization to trace errors back to their source, improving the efficacy of your analytics processes.
Why Does Data Observability Matter
Data observability is important to everyone, including vendors, customers, stakeholders, and the business itself.
From a vendor perspective, a lack of data observability can lead to disruptions that impact both upstream and downstream members of the supply chain; from the customer's viewpoint, a lack of data observability can lead to downtimes, outages, or diminished support.
A business that suffers from poor data observability will be prone to more frequent outages and struggle to meet the needs of its customers, especially during times of growth.
Although data observability profoundly impacts business health, many organizations still suffer from subpar observability and most organizations haven't achieved data observable across their entire tech stack.
How Does Data Observability Work?
At the most basic level, data observability is intended to detect errors in data before they skew forecasts or analytics reports. For instance, data observability is poor if an organization relies on siloed solutions like disparate machine learning software and metadata scanners. This lack of observability can lead to data quality errors, negatively influencing decision-making processes.
Data observability unifies disparate resources and tools to facilitate anomaly detection. You can better understand how data is being used, who's using it, and whether incongruencies exist in your data pools.
Perhaps more importantly, data observability enables you to monitor day-to-day data intake and management processes. Say that one day a system that normally has thousands of records suddenly has zero. Multiple systems depend on these records to provide performance reports and guide decision-making. With data observability, you can proactively recognize this discrepancy before it adversely impacts your organization.
Let's use this example. In the analytics world, you've got to set up a warehouse or a data monitor that is purpose-built to offer insights into your data. It can give you charts and dashboards to send to your end stakeholders.
Currently, your data quality solution may need to be revised to keep up with your data. So, from a processing and relevancy perspective, you can create these data marks that are purpose-built for data quality. You can have data replicating from there or virtualize into there and keep these monitors constantly running. They can run every night, every hour, or be in real-time. Depending on your team, you may make these decisions based on policy and then forget about them, expecting them to run.
It's rarely the case that they will run smoothly, but unfortunately, there's no monitoring of the problems either way.
Data observability will allow you to monitor everything.
Say, for example, that one table usually has around 10 million to 12 million records throughout this period. Imagine one day it has zero records, and you have all these dependencies on that data. If that is the case, all your charts, dashboards, and data quality reports are now shot. You have to wait until the business recognizes it for them to open a ticket before you know something's wrong.
Data observability will automatically alert the support, monitoring, and DevOps teams when such events happen, creating a ticket and solving the issue quickly.
Components of Data Observability
The concept of data observability includes five core components:1. Automation
Data observability solutions move away from manual ticket generation processes in favor of automated anomaly detection. Wagnon likens it to a smoke detector — it alerts you to a potential data issue before you have a five-alarm fire on your hands.
2. The Data Itself
Data observability is all about making your data more visible and easier to evaluate. When your data is observable, you can closely monitor its quality and accuracy, in turn optimizing usability.
3. Data Pipelines
Data observability allows you to create detailed data lineages and trace information from intake to analysis. Developing transparent data pipelines will support your compliance, governance, and analytics initiatives, all of which play a critical role in business continuity.
4. Data Infrastructure
Data observability provides actionable insights into your data infrastructure. You can trace the flow of data as it passes through various systems, tools, and software solutions across your entire network.
5. Data Users
While data observability does offer distinct benefits to your business, the concept is designed with the end user in mind, whether it's a customer or your in-house analytics professionals. Data users will benefit from improved observability, accuracy, and accessibility.
Where Can Customers Begin to Think About Data Observability?
According to Matt and Rex, your organization should be mindful of data observability if you have any business intelligence (BI) analytics solutions in place.
Data observability solutions will improve the health of data, simplify information management, and support the migration of your data sets to a centralized platform so you can more effectively leverage them.
Leaders should be thinking about data observability before they encounter a major problem. This level of consciousness also promotes better trust and buy-in among your staff.
Think about it — every time one of your team members launches your analytics platform and wonders whether it will load the desired report, they'll probably be at least a little frustrated.
Conversely, if they know it will populate detailed, accurate data, they'll lean into the resources you've provided them, maximizing your ROI and driving value for the business.
Improving Observability and Health of Data with Syniti
If your organization wants to embrace data observability, it must first tear down data silos and rein in unnecessary information sprawl. To do that, it needs a robust solution like Syniti Knowledge Platform (SKP). Our unified, cloud-based solution simplifies data migration, management, governance, and analytics.
To learn more, schedule your demo and discover the power of Syniti.