The world is becoming an increasingly data-centric place, especially in commerce, government, and industry. Analysis permeates nearly every field, and that means data quality matters as much as collecting and using data. Continue reading to see why data quality monitoring software is important in many types of operations.

GIGO 

"Garbage in, garbage out" is one of the oldest maxims in the analytics business. If you feed garbage into a system, it doesn't matter how much computing power or how many nifty algorithms you throw at the problem. Folks who want good results understand that data monitoring software is the first line of defense against the GIGO monster. If you can automatically and consistently check the inputs for bugginess, your odds of producing quality analysis and reports will go up significantly.

Stability

Data quality monitoring is also a stability issue. Bad inputs can trip up database packages and code. This can lead your analytics systems to become unstable. In the worst scenarios, you might end up with crashes or even security compromises. The classic disaster scenario is the injection attack, where a malicious party packs code into your data to trigger a crash or even gain administrative access through something a buffer overrun. If you want to have a stable and secure system, the smart move is to put data monitoring at the front of your process.

Scaling

An organization may also want to scale its analysis efforts. If your system is already struggling with junky data, scaling up will only make every problem worse. To achieve relatively effortless scalability, you have to have dependable data quality monitoring software in place. Once you know the system can handle everything you throw at it, you can begin to scale up. As the data monitoring setup continues to prove to be reliable, you can continue to scale both in terms of size and complexity.

Trust

Ultimately, most organizations producing analysis, datasets, and reports want someone to trust them. These could be internal or external parties. In some cases, you may need to be able to trust autonomous systems using the data to run independently and reliably.

Data quality monitoring ends up being about trust. If you send a report to a decision-maker, can they trust that the recommendations are grounded in the best available data? Results matter and that means the proof of your data quality monitoring efforts will end up being in how much trust people can put in the final product.

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