How to get the most out of network performance-management tools

With most enterprises deploying multiple network performance-management tools, it’s challenging to creatae a unified picture of what they know.

(Editor’s note: Enterprise Management Associates took a look at how individual organizations use multiple network performance management (NPM) tools and how they try to integrate them to improve efficiency. In this article, EMA’s research director for network management Shamus McGillicuddy presents findings from “Network Performance Management for Today’s Digital Enterprise,” a recent survey of 250 network managers that suggests best practices for dealing with this issue.)

The typical IT organization has three to six network performance management (NPM) tools installed today, and if they remain siloed, network operations will be fragmented and inefficient – a persistent challenge for network managers for many years.

EMA asked 250 network managers to identify their preferred procurement strategy for NPM tools and found that enterprises have a strong preference for a fully integrated, multi-function platform. However, they rarely achieve this state. For example, EMA found that enterprises that currently have 11 or more NPM tools are the most likely to state a preference for this fully-integrated strategy. Thus, while they profess a desire to consolidate tools, they are not succeeding.

Why so many NPM tools?

Part of the problem is that enterprises collect and analyze so many different types of data with NPM tools. Infrastructure metrics collected via SNMP MIBs and traps are a foundational data source for NPM, but they don’t contain all the answers a network manager needs from NPM tools.

Most enterprises also collect traffic data, including network flows, packets, or both. EMA research also observed strong interest in synthetic traffic generated by active monitoring tools. The most popular source of data for NPM analysis is management-system APIs. In other words, network managers have strong interest in pulling data from other IT management systems into NPM tools for contextual analysis.

Given this data diversity, tool fragmentation is inevitable. After all, no vendor excels at collecting and analyzing every class of data mentioned above. They usually excel at one or two classes of data types, meaning that enterprises inevitably acquire additional NPM tools to cover gaps in visibility.

Correlation across NPM tools

EMA asked survey participants to reveal how they correlate insights across multiple NPM tools. The most popular approach (25% of respondents) was the use of a network operations management platform or manager of managers that pulls insights from multiple NPM tools. These platforms are typically good at event management and alarm correlation across multiple NPM sources.

Next, 19% cited direct integration between point tools so that one tool can correlate insights pulled from another. This approach can get complicated if an enterprise is using more than two tools.

Another 19 percent integrate their NPM tools with an artificial intelligence for IT operations (AIOps) advanced IT analytics platform, 15% integrate NPM tools with a service management platform, and 14% stream NPM data to a data lake for correlative analysis. Only 7% claimed to perform these correlations manually, which is good because it’s an inefficient and error-prone technique. A handful claimed that they perform no correlation across tools.

EMA also asked enterprises how successful they were within this cross-tool correlation. Twenty-seven percent said they were very successful, and 49% were successful. The rest were somewhat successful, somewhat unsuccessful, or uncertain. EMA classified this last 24% as “less successful.” This question about success allowed EMA to search for potential best practices.

Enterprises that manually correlated insights across tools tended to fall within the “less successful” cohort. The most popular approaches to correlation – direct integration between tools and integration with a manager of managers – had no statistically significant associations with success.

Best practices

However, three less popular approaches to cross-tool correlation were preferred by successful organizations.

Successful enterprises preferred Integration with a service management platform or correlation via streaming of NPM data into a data lake for analysis. Very successful enterprises integrated their NPM tools with an AIOps platform.

EMA believes that these three latter approaches are potential best practices for addressing the problem of NPM tool sprawl. AIOps tools appear to be the best option. There are many standalone AIOps platforms that can fulfill this need, such a Moogsoft and Splunk. Furthermore, some NPM vendors, such as Broadcom (formerly CA) are developing their own AIOps platforms that can correlate insights across their suites of NPM and IT operations management tools.

EMA recommends that enterprises investigate AIOps platforms if they are struggling with network management tool sprawl. However, integration with service management platforms or the use of a data lake with a data analytics stack may also prove helpful.

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