How Data Observability Will Be Key for Enhancing Data Quality in 2024

ByPaul Christiano Last Update on

In the world of big data, organizations are overwhelmed by exponentially growing data volumes. As per Statista, the amount of data created globally is projected to grow from 64 zettabytes in 2020 to a staggering 181 zettabytes by 2025!

However, research shows less than 50% of companies manage data as an asset, and only 28% have a data-focused culture. In this landscape, maintaining high quality data is crucial but challenging.

This is where data observability comes in – providing comprehensive visibility into data systems to help improve quality. Observability will be a key trend in 2024 that organizations must embrace to drive informed decision making.

This article examines how data observability enables enhanced data quality. It covers:

  • The top data quality challenges organizations face
  • Ways observability helps tackle these challenges
  • Real-world applications showing the benefits of observability for improving quality
  • Predictions for the data observability market‘s growth in 2024

Let‘s get started.

Why Data Quality Matters

First, what is data quality? It refers to how accurate, complete, consistent, and timely data is.

With data now a crucial business asset, poor data quality can damage organizations in multiple ways:

  • Faulty analyses and insights: Bad data leads to misleading analytics and inaccurate insights about the true state of business operations and markets.

For example, a financial services company had to withdraw a market analysis report after discovering major data quality issues that skewed their models and predictions.

  • Incorrect decisions: Low quality data causes errors in assumptions and conclusions, resulting in detrimental business decisions that can negatively impact performance, growth, reputation and more.

A retailer opened multiple new stores after incorrectly analyzing demographics and demand due to dirty customer data. This led to huge losses.

According to analysts like Gartner, poor data quality costs organizations an average of $12.9 million per year! So it‘s clear that maintaining quality is critical.

How Data Observability Boosts Quality

This is where data observability comes in. Data observability involves monitoring data systems in real-time to manage health and performance.

Key capabilities include:

  • Data discovery and cataloging
  • Metrics and anomaly monitoring
  • Alerting and root cause analysis
  • Workflow and lineage tracking

Research shows the global data observability market size is predicted to grow from $3.7 billion in 2022 to $16.7 billion by 2027, at a CAGR of 34.2%. Major forces driving adoption are digital transformation and the move to cloud.

Top vendors include Datadog, Monte Carlo, Anomalo, Soda, and Hecate.io.

Data observability enhances data quality and reliability by:

Detecting Issues Proactively

Intelligent algorithms analyze trends to identify anomalies, inconsistencies, and errors early. This enables rapid remediation before problems propagate across data pipelines.

Monitoring Health in Real-Time

Customizable dashboards track key metrics like accuracy, freshness, and duplications. This provides a real-time pulse of overall data health.

Improving Team Collaboration

Shared visibility helps data producers and consumers collaborate better. Everyone gains the same understanding of quality issues.

Increasing Efficiency

Optimized pipelines reduce wasted engineer time and computing costs caused by faulty data flows.

Driving Strategic Decisions

Data health scorecards give executives visibility into quality status. This enables data-driven priority setting and goal tracking.

Ensuring Compliance

Consistent, auditable data helps demonstrate regulatory compliance and avoid huge penalties.

Real-World Applications of Data Observability

Here are some key domains where data observability will be crucial for maintaining quality in 2024:

Banking

Banks must ensure accurate customer data, transactions, risk models, and portfolio valuations to avoid disastrous decisions. Observability platforms like Monte Carlo help monitor data flows from source systems into analytics, risk, and regulatory reports.

Healthcare

Patient diagnosis, treatment planning, monitoring, and personalized medicine rely on high-quality data. Observability helpsHospital improve electronic health record (EHR) data and drive quality care.

Automotive

Self-driving vehicles generate huge volumes of real-time sensor and telemetry data that must be accurate. Observability helps maintain integrity across data pipelines.

Retail

Customer shopping behavior data, inventory management, and product recommendations depend on quality data. Observability platforms give retailers like Kroger visibility into analytics and AI/ML models.

Oil and Gas

Exploration and drilling require analyzing massive geospatial datasets. Observability helps verify sensor data accuracy and prevent catastrophic downstream decisions.

Key Predictions for 2024

As per leading analysts, here are some data observability trends to expect in 2024:

  • Increased adoption across mid-size enterprises. Observability was initially limited to tech giants.
  • New solutions tailored for specific industries like automotive, healthcare, and manufacturing.
  • More ML-driven capabilities like predictive analytics, intelligent alerting, anomaly detection and auto-remediation.
  • Tighter integration with data catalogs, data quality, and master data management solutions.
  • Stronger partnerships between observability vendors and cloud providers like Snowflake, Databricks, and AWS.

Final Thoughts

In summary, organizations must focus on data observability in 2024 to improve data quality. They can no longer risk impacting key initiatives and decisions due to poor data. Observability provides the comprehensive visibility needed to continuously monitor quality and address issues proactively.

Companies struggling with dirty data should evaluate leading observability platforms like Datadog and Monte Carlo. The ability to track data systems in real-time and get actionable insights will be a key competitive advantage. Organizations that embrace observability will drive innovation and growth through trustworthy analytics.

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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