What Is Data Monitoring and How To Go Beyond



When it comes to data, cleanliness rules. The squeakier your information, the easier it will be for you to use it effectively. Properly scrubbed, accurate data can unveil tons of stories that you can use for everything from budgeting to marketing to sales.

Here’s the problem, though: Data can get “dirty” at any point in the collection process. That’s why it’s critical to understand the state of your data. After all, if you accept damaged, flawed, or otherwise unclean data into your system, you’re flirting with problems down the road. Instead, it’s smart to set up your team for success by implementing data monitoring tools and a data observability solution to productively utilize your data.

Understanding the ins and outs of data monitoring

How does data monitoring work? Data monitoring software evaluates incoming information based on preset rules. Any information that doesn’t adhere to a rule is flagged so it can be corrected or eliminated. Thanks to advanced technology, data monitoring happens in the background. Your employees don’t have to do anything special unless they receive notice that potentially corrupt or incomplete data requires attention.

It’s hard to overstate the importance or benefits of data monitoring, particularly if you’re serious about collecting and using data. For instance, the process allows you to gain a high degree of control over data quality from the moment it arrives. Data monitoring acts as a digital gatekeeper, denying entry to data that could disrupt your system. Best of all, it’s automated so you don’t have to look over every piece of data manually.

Introducing data observability

If you’re looking for a way to receive real-time monitoring across your infrastructure, data, and application layers, data observability platforms not only monitor, but can predict and prevent issues before they cost you money. Data observability is a natural next-step move that goes beyond implementing monitoring measures.

Consider a form-fill that you use to gather customer information. Perhaps you ask for phone numbers. Do you allow hyphens? Parentheses? If you’re permitting customers to input phone data any way they want, the data may not come to you in a useful state. While data monitoring will alert you when there is an issue based on a pre-defined problem, data observability allows active learning, giving teams the ability to review data assets, assess schema changes, and identify root causes to unknown problems.

Data observability systems give you a granular view of your data no matter where it is. Remember that data can become dirty or flawed long after it first enters your business. Data observability provides you with the ability to continue shepherding information as it feeds and fuels your corporate needs.

Ways to improve your data workflows

Do you like the idea of incorporating data monitoring and, on a wider scale, data observability, into your company? You’ll want to follow a few best practices.

1. Investigate the right software for your company’s needs.

Unless you plan to create a data observability system in-house from the ground up, you’ll be purchasing pre-developed software. Take time to look through database management software options carefully. Ideally, you’ll want to find a system or partner that works with companies of your size.

Be sure to read reviews of any system that you’re considering purchasing. And sift over which ones offer the features your business needs. Don’t forget that you want your software to grow with your company. So if you plan on scaling soon, aim for a system that can handle rapid expansion.

2.  Stay up to date with your data pipelines.

Every time you add another source of data into your company, you change your data pipeline. Let’s say you upgrade to a better customer relationship management (CRM) system. While your upgrade may be wonderful from a holistic standpoint, it could create wrinkles in your data flow.

The way to iron out those wrinkles before they become major issues is by analyzing how your new system affects incoming and existing data. What new rules will you need to put into place to keep the data squeaky clean? Where are possible stumbling blocks? Stay ahead of the curve by identifying and eliminating bottlenecks to ensure pipeline performance.

3. Survey your data for freshness.

Modern businesses win when data is new. Though you may assume that the data you’re getting is timely, it may be older than you think. If your company depends on real-time information, invest in a data observability tool to identify the why behind the error.

The world runs on data. But if data isn’t optimized and usable, it can’t be an asset. Make sure your company’s information comes in spotless and retains its cleanliness through widespread data monitoring and observability.

Angela is a senior editor at Dreniq News. She has written for many famous news agencies.