Data visibility is a relatively new concern among IT business leaders and engineers. This concern stems from the widespread adoption of multiple data-producing applications to improve CI/CD workflows. This brings the problem of having to monitor the data then produced just to maintain peak performance. You know you have this problem if you have to log into more than one application when you get to work.

This article will guide you through what data visibility means for the different levels produced in a modern IT outfit. You’ll also learn the latest reasons why you ought to do more than just searching for the definition of “data visibility.” It only makes sense to seal the reading experience with suggestions for how you can master visibility across complex IT infrastructure. We intend to equip you with tools to analyze the sheer volume of logs when troubleshooting performance issues.

Blurry and clear binary signifying data visibility

What Is Data Visibility?

Before we proceed with issues around data visibility, let’s start with addressing what it is. Data visibility is the degree of ease through which an enterprise can monitor, display, and analyze data from disparate sources. Attaining ample data visibility within your organization brings about many benefits. The most important benefit is that you get more data from which to make informed business decisions. On the other hand, not having enough access to your data, or ignoring data visibility totally, has adverse effects on your business. Let’s explore this assertion as we look into why data visibility is important.

Why Data Visibility Is Important

A recent (2019) study by Keysight revealed what over 300 IT professionals think about the effectiveness of visibility in prevailing infrastructures. Through inference, a handful of reasons why you should have a visibility strategy in play come to mind.

  • Methodically searching through data is the best way to troubleshoot network performance problems.
  • Application downtime is less recurrent when logs (data) are clearly visible around any such occurrence.
  • A central (comprehensive) data visualizer reduces how much time and learning effort is required to maintain a robust (even complex) infrastructure.
  • Complex workflows have complex data access hierarchies only manageable when you know who can see which level of your data.
  • Lack of data visibility is the main reason why network and application outages even happen.

From these reasons why you certainly need to consider data visibility, it’s easy to partition data into distinct parts. For one, we have data that is emerging from and moving between enterprise applications. Such applications include, if any, your in-house developed product(s) and the external ecosystem that extends beyond your organization. Then there’s data about the network through which all the information passes through and the infrastructure. Let’s look at these two types of data closely before explaining what to do with either of them.

Data Visibility Across Enterprise Applications

Take inventory of the applications currently producing data across your business floor. Once you consider how much of the total pool of data is available to draw insights from, data availability becomes the first concern. In order to achieve a perfect visibility situation, you must grade the available data and see what’s worth including in your data visibility plans.

Much of the data generated across intertwined applications will not be classified. Often this is due to there being too much of it to analyze. “Dark data” is a term referring to raw information wrongfully classified as useless. A mistake like this can happen either by omission or by not having enough experience (or time) to deal with that type of data. Where users have to sign in to different SaaS and IaaS platforms in order to perform regular business tasks, it’s almost always the case that several instances of dark data amass. Data that would otherwise have made their business tasks easier to carry out were they discovered and analyzed. 

The first place to look in order to reduce the amount of dark data flowing past your teams is to have audits. Such audits would also help discover data you’re focusing on but would rather not. You can say dark data is the opposite of a case where, even when logged, some data holds low importance each time it’s displayed.

Another scenario brought to the surface by careful audits is the existence of duplicate data analysis or storage. The deliverable from suggested audits is a discovery report. This way you know all sources of data and the exact type of data available to work with.

Network and Infrastructure Data Availability

Several types of networks and infrastructure should be in place to ensure applications are accessible to your teams and customers. We’re not going to look at the various data visibility challenges for the various models. But a common theme is that performance data should be logged and analyzed. Logs are priceless when troubleshooting performance issues. Most downtimes that go for unreasonably long do so when the logs are vague to the teams who are supposed to bring things back online. Ambiguous logs can simply be a case of there not being enough data to reach conclusions and remedy problematic situations. Holes in the logs are suggestive of the idea that you may be tracking wrong sets of data. Introspective looks at your network and the components involved would be the first step in resolving this.

Most Common Data Visibility Problems

Scenarios discussed in the above sections bring a plethora of problems when they materialize. Let’s consider just two such problems along with how to resolve them. Where relevant, tool pairs that maximize data availability will be suggested in order to give you a head start when making your own visibility plans.

1. A Disconnect in Data Versions Across the Enterprise

Where data is neither available nor visible, it’s common for employees to hold versions of documents unique to their workstations. At any given time, you’ll have storage space wastage due to there being redundant data on the network. To resolve this problem, the use of version control applications is best practice. Knowing the deliverable from such applications is the next stage. This makes it easier to aggregate reports for the involvement of every member of a pipeline.

A perfect example of such a solution applied where developers, project managers, and customer interfacing teams converge would be Jira + GitHub. Data from the target users’ tickets would trickle into Jira from feedback software and then to developers as issues and patch requests in GitHub. There’s a seamless data flow and visibility stemming from the complete availability of data. In turn, this often always results in faster versioning and great user experience overall.

2. Difficulty in Monitoring Data Across Your Infrastructure

You shouldn’t have to guess how your Kubernetes clusters are performing. Or even how the network is responding to your scaling plans. Performance metrics are often the first indicator of impending doom. However, the task of watching multiple screens just to keep an eye on all logs shouldn’t be forced on anyone either. A centralized log management tool that makes data available from all servers and networks would be great at such a point. This is where a product like Scaylr comes into play.

Scalyr, coupled with whichever application server infrastructure of your choice, lets you stay ahead of performance dips and outages. This happens through event notifications generated from your logs and assigned to relevant actors. The fast ingestion and search capability of Scalyr ensures data visibility the very instant that you decide and plug in the data sources you wish to monitor. Further analysis of said data through many of its features makes it perfect when considering data visibility across your enterprise.

Attaining Perfect Data Visibility for Your Organization

Owing to the fact that companies are using different software applications to essentially run their businesses, a lot of data is produced every day. Much of this data is lost as dark data and will never be used to gain a competitive advantage on the market. All the same, a lot of data that shouldn’t have much weight is wrongly focused on. If you wish to attain a euphoric state of data visibility, consider all the data around your applications and infrastructure. Analyze and optimize for data availability before you go at data visibility. Only then can you effectively try out various data management and visualization tools.

This post was written by Taurai Mutimutema. Taurai is a systems analyst with a knack for writing, which was probably sparked by the need to document technical processes during code and implementation sessions. He enjoys learning new technology and talks about tech even more than he writes.

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