Driving Business Decisions with Data Observability

Trustworthy data is the key to business success. Without trust, your data is useless and can even lead to disaster. Download our white paper to learn how Nitrate™, GSPANN's role-based AI-driven data observability offering, can jumpstart your business and put you on top.

Data analytics offers significant business benefits by transforming raw data into actionable insights. It enables organizations to make informed decisions, optimize operations, and gain a competitive edge. By analyzing large volumes of data from various sources, businesses can identify trends, patterns, and correlations that drive strategic planning, improve customer experiences, and enhance product development.

A reliable data set can drive business success through personalized marketing strategies, accurate demand forecasting, and efficient resource allocation. This can lead to increased revenue, improved customer retention, and higher overall business performance. However, the same data that can drive business success can also prove to be its downfall.

Data Explosion Muddies the Waters

The explosive growth in data volume has led to significant challenges in maintaining data quality and trustworthiness. As organizations grapple with massive amounts of information, their ability to effectively manage, process, and secure this data has not kept pace.

According to recent studies, data volumes are expected to double every four years, with global data predicted to surpass 175 zettabytes this year. This rapid expansion has outstripped many organizations' capabilities to implement robust data governance, quality control, and security measures. Consequently, the sheer volume of data makes it increasingly difficult to ensure accuracy, completeness, and consistency across all datasets.

The mismatch between data growth and management capabilities has created a perfect storm for untrustworthy data. Rubrik's "The State of Data Security" study reported that two-thirds of IT leaders express concern that data growth is outpacing their security capabilities. This gap leaves organizations vulnerable to data breaches, inconsistencies, and errors. The pressure to quickly process and utilize vast amounts of data often leads to shortcuts in data validation and quality assurance processes.

The result is a higher likelihood of inaccurate, outdated, or corrupted data infiltrating decision-making processes, potentially leading to flawed insights and misguided business strategies. As organizations struggle to keep up with this data deluge, the risk of relying on untrustworthy data for critical business decisions escalates.

Untrustworthy Data Leads to Disaster

Here are three well-documented cases where untrustworthy data produced a business disaster:

JP Morgan Chase's "London Whale" Incident (2012)

JP Morgan Chase suffered approximately $6 billion in losses due to the failure of their Synthetic Credit Portfolio Value at Risk (VaR) Model. The model, designed to manage risk associated with trading strategies, was compromised by several factors:

Inadequate model development and testing
Insufficient oversight and quality control
Manual data uploads without proper checks
Frequent formula and code changes in spreadsheets without controls. This incident led to intense regulatory scrutiny, damaged the bank's reputation, and resulted in $1.020 billion in penalties.

Unity Technologies' Ad Targeting Error (2022)

Unity's Audience Pinpoint tool, used for targeted player acquisition and advertising, ingested bad data from a large customer. This caused significant inaccuracies in the training sets for its predictive ML algorithms, leading to:

A loss of approximately $110 million
A 37% drop in Unity's share price
Delayed launches of new revenue-driving features
Damaged investor confidence in the company's strategy

Equifax's Inaccurate Credit Score Fiasco (2022)

Equifax, one of the three major credit reporting agencies in the U.S., experienced a coding error that resulted in inaccurate credit scores for millions of consumers. The consequences included:

Potential denial of credit or loans for some consumers
Higher interest rates for others
A class-action lawsuit against the company
Damage to Equifax's reputation and trustworthiness in handling sensitive financial data

How to Produce Trustworthy Data: Find a Reliable Partner

To attain real-time data observability and implement a system that produces trustworthy data, business leaders should follow these steps:

Assess current data infrastructure
Define clear objectives
Implement a comprehensive data observability framework
Leverage advanced technologies
Establish data governance and security measures
Develop incident response and recovery procedures
Continuously monitor and optimize

Recognizing the critical role of engaging with a competent software systems integration firm is of critical importance. These partnerships are essential for seamlessly connecting disparate software applications, databases, and platforms within an organization.

A proficient systems integration firm brings expertise in aligning various technological components, ensuring they work harmoniously to support business processes and objectives. This integration is crucial for eliminating data silos, improving operational efficiency, and enabling real-time data flow across the enterprise.

By leveraging the skills of integration specialists, your business can avoid the pitfalls of poorly implemented systems, reduce operational costs, and gain a competitive edge through enhanced data utilization and decision-making capabilities.

What You’ll Learn in This White Paper

The white paper discusses the importance of data observability in today's digital economy, where data drives innovation and strategic decision-making. Traditional observability tools focus on system health but often fail to address the reliability and relevance of data within pipelines, transformations, and business-critical metrics. GSPANN's Nitrate™ is a solution that bridges this gap by providing comprehensive data observability, ensuring data quality, integrity, and trustworthiness throughout its lifecycle. This enables organizations to make informed decisions based on accurate and actionable insights.

Nitrate™ offers a role-based, AI-driven approach to data observability, featuring a single pane for monitoring data health across all personas, real-time pipeline observability, automated data reconciliation, and anomaly detection with self-subscription alerts. The platform supports seamless integration with various cloud environments (e.g., Azure, Google Cloud Platform, Amazon Web Services) and data platforms (e.g., Databricks, Snowflake) while providing customizable dashboards and alerts tailored to different user roles. These features ensure continuous monitoring of data ecosystems, fostering trust among business and technical teams and improving operational efficiency.

Data observability is not just a technical necessity but a critical enabler of business success. It empowers organizations to navigate complex data ecosystems confidently and drive superior outcomes. Download this white paper to find out how Nitrate™ can drive business success.