Bad data can be seen as kryptonite to a company’s bottom line. Like a super spreader, it sneaks in, replicates, and corrodes your informational warehouse like waves on clay. And when that happens, trust is compromised, which can lead to additional risks and possible mishaps. After all, a company’s reputation and insight accuracy deeply impact its bottom line.
What is a Data Warehouse?
Data warehousing technology allows businesses to aggregate data and store loads of information about sales, customers, and internal operations. Typically, data warehouses are significantly larger than databases, hold historical data, and cull information from multiple sources.
If you’re interested in learning more about data warehouses, try reading: Why We Build Data Warehouses
Why is Data Warehousing Important to Your Bottom Line?
In today’s highly personalized digital marketing environment, data warehousing is a priority for many corporations and organizations. Although data warehouses don’t produce direct profits, the information and insights they facilitate act as beacons for corporate and industry trajectories. For some businesses, informational warehouses provide the data fuel needed to populate their apps and customer management systems.
What is Good Data?
A data warehouse is only as good as the information in it, which raises the question: what constitutes good data?
Informational integrity is tied to seven key pillars:
- Fitness: Is the data moving through the pipeline in a way that makes it accessible for its intended use?
- Lineage: From where is the info coming, and is it arriving at the proper locations?
- Governance: Who has access to the data throughout the pipeline? Who controls it?
- Stability: Is the data accurate?
- Freshness: Did it arrive on time?
- Completeness: Did everything that was supposed to arrive land?
- Accuracy: Is the information accurate?
Early Detection Saves Time and Money
The longer it takes to find a data pipeline issue, the more problems it creates — and the more it costs to fix. That’s why early detection is vital.
Data errors are like burrowing viruses. They sneak in and keep a low profile while multiplying and festering. Then one day, seemingly out of the blue, the error rears its ugly head and causes chaos. If you’re lucky, the problems stay internal. If you’re unlucky, the error has a catastrophic downstream effect that can erode confidence in your product or service.
Examples: The Costly Compound Effect of Data Warehouse Errors
We’ve established that data warehouse errors are no-good, horrible, costly catastrophes. But why?
Upstream Data Provider Nightmare
Imagine if other companies rely on your data to fuel their apps, marketing campaigns, or logistics networks. A mistake that manifests from your camp could have a disastrous domino effect that leads to a client-shedding reputation crisis.
Late-Arriving Data
Late-arriving data is another nightmare if other companies rely on your data. Think of it as a flight schedule. If one plane arrives late, it backs up every other flight that day and may force cancellations to get the system back on track.
Understanding Leading Indicators of Data Warehousing Issues
Leading indicators signal that bad data has weaseled its way into a data pipeline. However, built-in status alerts may not always work. For example, it’s possible to receive a 200 success response from an API built on the HTTPS protocol since the check only applies to the connection, not the data transfer. Intrinsically, it’s essential to understand the leading error indicators.
Catch data pipeline leading error indicators by:
- Setting up baselines
- Establishing data checkpoints
- Tracking data lineage
- Taking metric measurements
Maintaining a healthy data warehouse is of vital importance, especially if other businesses rely on your services. Working with data warehousing solutions is often the best option in terms of cost optimization, speed, and overall performance. They have the skills, tools, and institutional knowledge to ensure everything runs smoothly.
