Data Quality & Observability:
Ensure value and transparency

Modern stacks are complicated.While amassing vast amounts of data is an achievement, it doesn’t automatically translate to value. And data without quality is like a recipe without any good ingredients — there’s potentially a meal at the end of the process, but if it’s not going to taste good, the recipe isn’t very useful.

Data quality refers to the ability of data to fit its intended purpose, ensuring it is accurate, complete and reliable for decision-making. Observability in the context of data is the ability to fully understand the health, status, and performance of data systems and pipelines through monitoring, logging and tracing. Together, data quality and observability empower organizations to trust their data-driven decisions by providing transparency and insights into your data operations.

$15M

The average annual financial repercussions of poor data quality.

Source: Gartner Data Quality Market Survey

Key features to look for

Data profiling: Understand your data’s structure, content and quality. This is the preliminary step in identifying areas that need attention
Data cleansing: Clean or remove corrupt, inaccurate or erroneous bits of data to maintain its integrity and reliability
Data enrichment: Enhance the data’s value by appending related information from external sources. This not only adds depth but also context
Data monitoring: Keep an eagle eye on data streams to detect anomalies, inconsistencies or any deviation from the defined quality standards
Data pushdown: Sick of being blindsided with egress fees? Leveraging tools that address data quality at the source can amplify security, hasten processing and maximize ROI

Use cases by use case:

E-commerce: Ensure personalized recommendations that resonate with users, leading to better conversion rates and a better user experience
Risk management: Identify potential risks and take preemptive mitigation measures
Operational efficiency: Drive accuracy of operational metrics, improved process efficiencies and reduced downtime

Use case by industry

Healthcare: Providing correct diagnoses, treatment plans and care outcomes. Ensuring data quality can literally be a matter of life and death in this sector
Energy: Managing resources, forecasting demand and managing the grid to ensure reliability and efficiency
Retail: Driving more effective inventory planning and more personalized shopping experiences