Why Network Data Might Derail Your AI Advances

Your network data may need to be improved before you can begin applying AI and ML technology.

More companies are pursuing network solutions that allow Artificial Intelligence (AI) and Machine Learning (ML) advances to improve network performance and efficiency, resulting in benefits like cost savings and lightening the load for IT teams. But if network data is lacking in quality or frequency, it may be impossible to realize the benefits of AI and ML.

Enterprise Management Associates recently conducted a survey of 250 IT professionals, asking them to describe their experiences with AI and ML and how it relates to networking advances. While network complexity was considered the biggest challenge, the second was network data.

According to the findings, 90% of respondents had experienced at least one significant challenge when working with network data and their AI/ML solutions. Here are some of the reasons network data can be a difficult obstacle to navigate:

Security: For some industries, such as healthcare or financial services, sharing network data with AI or ML tools in the cloud can be a security risk. They prefer to keep solutions in-house where they can control who has access to it. While some vendors work with anonymized data to identify trends and gather insights, there are companies that still aren’t comfortable with this type of data collection.

Data Quality: Networking insights are only ever going to be as good as the quality of data. If there are systemic errors, formatting problems or consistency issues across siloed systems, the data quality won’t be sufficient for AI to make sense of it.

Bandwidth Costs: For some companies, the cost of sending network data to and from the cloud creates a difficulty with bandwidth costs. In some cases, this can be managed by processing and analyzing data at the network edge, but if bandwidth costs are a concern, it’s important to ask the vendor how they plan to address it.

Granularity: Some networks simply don’t offer data frequently enough for it to provide useful insights in a AI/ML tool. For instance, an SD-WAN networking solution limits the collection of data for telemetry because telemetry traffic can negatively affect the network’s performance.

Begin With an Assessment

Even if you aren’t currently pursuing an AI approach to networking, you will benefit from determining the quality and accessibility of your network data. See if any area of the network is prone to errors that would make AI data collection unusable. And consider data collection intervals. Even a five- or ten-minute window allows a lot to happen in a network.

If you’re ready to see if your network can handle AI and ML technologies, ITBroker.com can help. We can assess your current infrastructure and match you up with the best supplier based on your business and needs. Contact us today to get started.