Outages. Downtime. Bottlenecks and degrading performance. Usage and capacity bills increasing every month. Users who can't get the data they need when they need it. Not to mention the stress and expense of cloud migration initiatives.

As organizations rely more on sophisticated applications for ingesting, streaming, and analytics, system and data pipeline vulnerabilities can increase. Data operations has become so complex that it's difficult to see problems and points of failure before they happen. 

Without data observability tools like Acceldata, organizations are missing opportunities to be proactive and become 'data first' companies. Data observability is a new category of applications that go beyond just monitoring applications and reduce the complexity of modern day data operations. Preventing service problems, getting a clearer picture of system performance, and democratizing the real time data internal users need are possible with observability tools.

However, a Dynatrace survey of organizations found the average large company has observability for only 11% of its applications and infrastructure. And just 13% are using observability to monitor the experience with apps and websites from end to end. 

Integrating observability into your company's tech arsenal can help you deliver a better experience. It can also give you a leg up on the competition in improving service for external and internal customers.

What Does Data Observability Mean?

As a basic, data observability means maintaining a constant pulse of the health of your data at the infrastructure layer, the data layer, and the data pipeline layer. 

From a data performance perspective, there are three traditional sources of support: logs, traces, and metrics. While logs provide a record of what happened and when, traces give insights into application performance. Metrics give you feedback in the form of data according to predetermined key performance indicators (KPIs).

The technology expands upon these sources of data performance support to include additional points of understanding. Freshness, distribution, volume, schema, and lineage are known as the five pillars of data observability.

Data Volume, Flow, and Quality

Freshness refers to the age of the information contained in your databases and data stores. The frequency of updates, how much data is coming in and from where, and overlapping data can impact freshness. During audits, you may discover you have duplicate information from multiple sources. However, one application collected the data a year ago, while the other one gathered it five years earlier. Chances are good that the newer information is more accurate.

Distribution is about whether your data meets the parameters you want it to. Is the information in the fields uniform? Are there formatting mistakes and discrepancies that could cause an application to misinterpret them - or throw them out altogether, producing errors?

Distribution problems can sometimes be traced to human error and subjectivity. Other times, they can be due to the complexities involved as data moves between multiple applications and technologies.

Alongside distribution is the concept of quality. This refers to how complete your information is. Missing values and incomplete fields can present problems. So can data sets that contain dissimilar or disjointed pieces of data. 

Schema drift happens when employees or automated processes make changes to the structure of the information. This can impact analytics applications downstream.

Lineage shows how data is coming in, where it's going, and who's using it as it flows through various processes. This information can reveal points of stress or vulnerabilities, as well as whether some data is underused. In some cases, there may be data that's not being tapped at all. Lineage can identify opportunities to relieve bottlenecks waiting to happen or better ways to establish workflows.

Why Is Data Observability Necessary?

Cloud-based services and environments are dynamic, with constant changes and updates in real time. Adding to this complexity is the fact that many organizations have a hybrid cloud computing environment. Multiple players are involved in delivering services and analytics so users can quickly consume accurate data.

Imagine your IT team is busy troubleshooting a problem with one app, while there's another one about to crash. These apps are hosted by different vendors in the cloud, and monitoring both is difficult to impossible at times. Your IT team doesn't know the second one's about to crash because the point of failure isn't obvious.

You likely have performance monitoring tools that can alert your team that the system is down, but that'd be too late. The app ends up crashing, and there isn't any way to reroute things because the backup system also went offline. Your IT team still needs to resolve the issue with the first app before it can tackle the outage. Although this is a "worst-case" scenario, it can easily happen when human resources are overtaxed.     

Multiply this scenario by the number of apps, services, vendors, and employees involved in your company's operations. Without technology or intelligence to monitor the big picture in real time, things can get messy. For internal staff who rely on cloud-based services and apps, irritation could lower job satisfaction and lead to turnover.  For customers that rely on your systems to make real time decisions, it's more than a problem. It could be a significant loss of money or a life threatening situation.

Data observability is about revealing and seeing the connections between all the moving pieces. It shows how a problem or hiccup in one area can influence performance in another. The technology answers the question of why interconnected systems may be having difficulties. 

Data observability offers insight into potential issues in an automated way that is more efficient and scalable than manual processes. 

Among them, your company can become less reactive in its approaches to problem-solving. Teams can plan out real solutions rather than rushing to resolve emergencies. The quality of your data will improve and become more reliable. Analytics can tap sources closer to what's happening at the moment instead of months or years ago. And the decisions you implement will be grounded more in reality than speculation.