Artificial Intelligence is essential to help companies stay competitive in the fast-paced digital landscape. To achieve AI ambitions, organizations need data and a cyber-resilient data platform to support them, and this will mean a growing need for data observability. As organizations become increasingly data-driven toward achieving AI ambitions, they recognize the need to ensure data accuracy, reliability, and quality. Data observability solutions empower organizations to gain clear visibility into their data pipelines, rising to meet the growing requirement that data is correct, timely, and dependable. Data observability is similar to observability in software engineering, which focuses on scanning and understanding the state of systems and applications through logs, metrics, and traces. Data observability includes several concepts, and here are some examples:
Data Freshness: How up-to-date is the data?
Data Volume: Monitoring changes in the amount of data, which can indicate issues or unexpected behavior in data pipelines.
Schema Changes: Tracking data structure or schema changes can affect downstream processes and analytics.
Data Quality: Identifying data accuracy, consistency, and completeness issues.
Data Lineage: Understanding the source and flow of data through various systems and processes, which is crucial for troubleshooting, impact analysis, and compliance.
In this blog post, we will explore the key trends that will shape the landscape of data observability in 2024. Unfortunately, some concepts, such as data governance, are unwelcome to some people! There is often a tendency to use technology to solve a ‘people’ problem. For organizations who want to ‘move fast and break things,’ there must be a balance between ‘breaking things’ to make progress and generating vast amounts of technical debt and data debt.
Soup to Nuts: End-to-End Data Observability
In 2024 and beyond, data observability will move beyond focusing on individual units of data pipelines to include end-to-end observability throughout the data estate. Meeting AI aspirations means looking after data, so organizations will aim to monitor and understand data flow across the entire pipeline from soup to nuts, from data ingestion to data delivery. Organizations can proactively identify and resolve issues quickly by adopting a broader approach. In the long term, businesses will benefit from improved data quality and reliability, which means they can trust their data more. Therefore, the AI ambition has the intangible benefit of improving the quality of previously unloved data.
Increased Adoption of AI/ML in Data Observability
As the volume and complexity of data continue to grow, businesses may need more than traditional methods of data observability. To achieve AI ambitions, companies may need to integrate artificial intelligence (AI) and machine learning (ML) techniques in data observability processes as a first step into their AI journey. In 2024 and beyond, I expect to see increased adoption of AI/ML-driven solutions that automatically detect anomalies and predict data quality issues. Since businesses are fluid and dynamic, these can take the form of suggested and actionable insights for data teams, so there is a human in the loop.
Data Observability and the need to meet Governance and Compliance requirements
Data governance and compliance will play a crucial role in data observability trends in the era of stricter data regulations, such as the General Data Protection Regulation (GDPR), the Digital Operational Resilience Act (DORA), and the California Consumer Privacy Act (CCPA). Organizations must have robust data governance frameworks to maintain privacy, security, and ethical use. In 2024 and beyond, data observability will go hand in hand with data governance to ensure compliance with evolving regulatory requirements. If you think it is expensive to implement it, consider the cost of not implementing it!
Data Observability means Real-time Monitoring and Alerting
Organizations will require real-time monitoring and alerting capabilities in their data observability solutions as data pipelines become more intricate and real-time data processing gains importance. In 2024 and beyond, tools that can provide instant warnings for anomalies, latency spikes, or data inconsistencies will become increasingly prevalent in data-savvy businesses. Real-time monitoring will allow data teams to take rapid action to prevent issues and preserve data reliability.
Collaboration between Data Observability and Data Engineering Teams
Data observability requires cross-functional cooperation and collaboration between business teams and data engineering teams if the business is serious about implementing it correctly. Without cross-functional collaboration, Companies will not achieve the dream of being data-driven with robust data observability practices. Artificial intelligence ambitions will help to drive data excellence within organizations because people want AI on their resumes! Collaboration is a smart career move; power is not about keeping power and influence for yourself; the more you enable and empower other people, the more your influence will grow. Servant leadership works well in environments where people have difficulty collaborating.
Data Observability supports the need for data to be 'good enough' for AI.
As organizations strive to leverage the power of data for decision-making and Artificial Intelligence, data observability will play a critical role in providing the basics by ensuring the data is ‘good enough’ in terms of accuracy and reliability of data.
To summarise, as companies start to understand that you can’t have AI without data, we expect to see data observability become more prominent than it currently is. Implementing the ‘people’ aspect will take time as organizations move towards end-to-end observability, increased adoption of AI/ML-driven solutions, a focus on data governance and compliance, real-time monitoring and alerting, and improved collaboration between data observability and data engineering teams. Embracing these trends will enable organizations to unlock the full potential of their data to make their AI work in the business. It also has general benefits for the company. By leveraging data observability, organizations can reduce downtime, enhance operational efficiency, enrich data quality, and build confidence in their data assets. Altogether, the goal of being insights-inspired and data-driven ultimately leads to better business outcomes.
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As I wrap up this brief exploration of this topic, it’s clear that there’s a vast ocean of nuances and insights that we’ve only begun to skim the surface of. Whether you’re a seasoned expert in the field or just starting to dip your toes into these waters, the journey toward understanding is helpful to making your AI work.
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