Forty-five percent of executives see revenue leakage as a systemic problem for their companies. And at the core of those systemic issues are bad data processes.
But for many companies these issues often go unchecked due to a restricted view of their data operations. Errors flow through data pipelines, affecting insights and reducing the ability to make well-informed decisions. Which begs the question: If you don’t know something’s wrong, how can you fix it?
Without effective processes in place to monitor your data, you’ll lack end-to-end insights and put your data quality at risk. This is where data observability can help.
What is data observability?
Data observability refers to how well you understand the health of the data in your systems. It’s not simply a technology or single practice, it’s a collection of activities and technologies that drive clean, optimal data operations. There are five pillars of data observability:
- Freshness - Freshness checks how up to date your data is, and the frequency that you update it.
- Distribution - Distribution looks at whether your data matches the expected values. If it doesn’t, there could be a reliability issue.
- Volume - Volume explores the completeness of your datasets and gives some insight into the health of your data sources.
- Schema - Schema looks at how you’ve organized your data to find any breaks in the tables or data. The larger your datasets, the harder it can be to pin down where breaks are.
- Lineage - Lineage helps you map out your data’s entire journey through the pipeline. It looks at the sources, where the data’s heading, its storage, transformations and the users involved with it.
These pillars help you gather valuable insights into the state of your data, and understand how reliable it is for reporting and analytics. Data observability is important because it:
- Improves your data quality. By monitoring the data pipeline at every stage, data observability helps detect errors that may lead to poor data quality. This ensures your data is accurate and reliable, leading to better decision-making.
- Allows you to resolve problems faster. Data observability helps you identify issues in real-time and respond to them quickly. This can help reduce the risk of inaccurate reports, data loss, and increase the overall efficiency of your data operations.
- Helps you spot opportunities and maximize your profits. With a wider view of your data, you’ll have a better understanding of how well individual business processes are performing. This helps you capitalize on positive trends.
- Makes your teams more productive. Solving issues in your datasets reduces frustration and minimizes downtime. This helps your teams stay focused on tasks that are beneficial to business growth.
- Boosts collaboration between teams. Data observability provides a consolidated view of data across your teams and departments. This helps break down silos and improves data sharing, enabling you to make data-driven decisions based on complete datasets.
Ultimately, all of these benefits help you generate more revenue and aid your business growth. But how can it help you spot areas where your revenue might be slipping away?
Identifying your revenue leaks with data observability
Ever heard the phrase “data rich, insight poor?”
This is often the case for companies who have to deal with large volumes of data that would be impossible to monitor manually. As a result, huge numbers of orders and customer queries can lead to operational issues.
This was the case for a global logistics company. They were dealing with thousands of shipments every day and losing vast amounts of money through accounting inefficiencies. Their goal was to collate information from dispersed systems and find out whether they were applying appropriate charges on their air parcels.
With help from the CloverDX data platform and a new data warehouse, they had a full, consolidated view of their data from across the business. This helped them isolate problem shipments, identify patterns of incorrect bills, and eliminate invoice delays.
Rather than the teams having to generate manual Excel reports, they received hourly updates to see time-sensitive data, such as surges in shipments. This helped them improve their resource management and better prepare for spikes in activity.
The data warehouse handled their billing data from multiple systems. With the ability to find and fix incorrectly billed items, they were able to eliminate major sources of revenue loss. And, with increased observability across their shipments, they were able to view and forecast on key metrics they weren't able to see before.
A unified view of your data
Knowing something is wrong is better than not knowing at all. Automated monitoring improves your data observability and helps you maintain perfect data health.
But to achieve high levels of data observability, you’ll need a powerful data platform with the right automation capabilities. With CloverDX, you can take control of the entire data process in one place and remove tedious manual steps. You’ll have full observability over your data lifecycle and have the right tools to identify and fix revenue leaks. Book a demo today.