Data ‘Poka-Yoking’ With Data Observability for the Modern Data Stack
Loretta Jones is VP of Growth at Acceldata.io with extensive experience in marketing to SMBs, mid-market companies, and enterprise organizations. She is a self-proclaimed "startup junkie” who credits her degree in psychology from Brown University for helping her navigate a career in Silicon Valley and be successful in marketing.
While in the past, businesses used data to gain an edge over their rivals, in today’s competitive environment, data is imperative to stay in business. Modern businesses rely increasingly on data to manage all aspects of their operations, from everyday workflows to impacts on business strategy and customer interactions. As a result, data stacks have become extremely complex.
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As such, it’s more important than ever that businesses have the ability to observe their data in real-time to ensure accuracy and reliability. Data observability for the modern data stack offers end-to-end visibility into the full data pipeline, enabling businesses to identify and resolve issues quickly. This can be especially helpful in large organizations where there are diverse sources of data and multiple stakeholders who rely on it for their success.
With the emergence of new data environments, old data management methods cannot effectively serve businesses, and the solution lies in data observability, which is the key to making modern data stacks run smoothly.
The “What” and “Why” of Data Observability
Optimizing data can be challenging for data science teams in many ways, such as managing raw data and ensuring data reliability while keeping costs down. A key strategy to face those challenges is to maintain a comprehensive view of all data activity.
Here’s where data observability can help through enhanced visibility into your data and systems.
Data observability refers to using technology and tools to understand, diagnose, and manage data health throughout its lifecycle. It discovers, triages, and solves data issues in real-time.
With data observability in place, companies can gain insights into the performance and behavior of their data systems, which can inform decision-making and help optimize processes. For example, data observability can help companies identify bottlenecks or inefficiencies in their data pipelines or discover opportunities for improving the accuracy or reliability of their data.
Data observability thus helps companies maintain the integrity and value of their data assets while ensuring that their data systems are performing to their full potential.
Know the Difference: Data Observability vs. Data Monitoring
Even among data experts, the concept of “data observability” can cause consternation: How is it different from data monitoring?
Well, both techniques observe and track data movement. Most monitoring solutions provide insights into how the technology stack performs—but they don’t provide information about data quality.
Monitoring data does not provide insight into how the organization’s data, processes, and pipelines are working. Data observability goes beyond data monitoring by tracking data across servers, applications, and tools. It provides the visibility required to streamline data tracking effectively.
To monitor and track data, you need to know what you’re looking for. Observability reveals insights on all data interaction with disparate tools, detecting problem areas and allowing for a faster time to detection and resolution of issues.
The Need for a New Approach to Data Management
Why is data observability such a priority now?
While companies have been transforming digitally for several years, the rush to cloud adoption resulting from the pandemic era and increased data monitoring and protection challenges have compelled companies to adopt data observability.
Data observability also helps organizations comply with regulations and industry standards, such as those related to data privacy and security. By monitoring and understanding how data is being used, organizations can ensure that they comply with these regulations and standards, which can help protect their reputation and avoid costly fines and penalties.
With the ever-increasing amount of data and complexity, traditional data management systems are failing to keep up. Understanding and managing information have become increasingly difficult as more challenges have cropped up, like:
The Surfeit of Distributed Data Sources
Until recently, most data infrastructure was designed to handle relatively small amounts of data. The sources were mostly internal and on-premise.
Nowadays, data sources can be both internal and external, and the volume has increased exponentially. The sheer volume combined with distributed data sources is a recipe for delays and mishaps. Managing modern data with legacy systems can be a nightmare scenario with inaccurate or unidentified data.
Digital Transformations Have Become More Complex
The rapid rise in the use of cloud computing through hybrid architectures and multi-cloud operations has complicated many organizations’ cloud ecosystems in recent years.
As more companies transform digitally, they are ingesting data from diverse, numerous, distributed data sources with different data models. Therefore, organizations need to adapt, collect, and aggregate the data, then transform it into a standard format to make it all usable.
As an added challenge, abrupt changes can result in a wave of failures downstream, and it is difficult to identify the source of the problem in the midst of all these complications. Consequently, the modern data stack requires a proactive approach to data management through data observability, which allows users to pinpoint the source of problems and resolve them as quickly as possible.
Trying to Use a One-Size-Fits-All Data Pipeline for Complex Pipelines
The result of implementing ingestion pipelines for current data volumes using legacy tools and processes has increased complexity and headaches. Recently, some tools have automated data ingestion. Those solutions are part of the modern data stack, and their goal is to reduce how long it takes for those tools to make data usable for end users.
Because everybody needs compliant access to current, high-quality data, there is a need for data observability tools to bring visibility, tracking, and control of what is happening to the ingested data and how it is being used in the tech stack.
The Benefits of Employing Multidimensional Data Observability at Scale
Despite multidimensional data observability for the modern data stack being a relatively new discipline, there are already many observability practitioners and tools available in the market. As more organizations implement the strategy, the benefits of data observability have become more evident. Here are some highlights from the state of observability 2021 report from Splunk.
- Observability-leading companies are 2.1 times as likely to say that they can detect problems in internally developed applications in minutes.
- With strong data observability, leaders report a 69% better mean time to resolution for unplanned downtimes.
- The average annual cost of downtime associated with business-critical, internally developed applications decreases to $2.5 million, versus $23.8 million for beginners.
- Measurable improvements in visibility, software delivery, and innovation improve the digital experience.
These benefits are substantial, especially considering these are still the early days for the technology. Organizations adopting data observability for the modern data stack will enjoy benefits in performance, security, streamlined operations, and cost savings over time.
The Bottom Line
Leveraging a data observability solution helps organizations solve the challenges of complex data pipelines. It protects data quality and reliability, provides insights into how the data is used, reduces complexity and costs, and increases agility and visibility.
All in all, data observability for the modern data stack is becoming a must-have technology for any organization looking to make the most of its data. However, an effective data observability solution must encourage proactive methods to prevent, identify, and resolve data management issues.
By adopting a modern data stack that includes a sophisticated data observability tool, organizations can unlock the power of their data and maximize its potential. Data observability is no longer a nice-to-have; it’s essential to stay competitive in an increasingly challenging business landscape.