Collecting data on your business’ practices can being about valuable insights and help inform your company’s leaders as they make critical decisions that could affect the organization for years to come.
Data like productivity, meeting room utilization, IT infrastructure performance, ticket response time and security analytics can help set the organization up for future success, but it takes a lot of careful and particular planning – as well as technology – to gather that data in the first place.
Why should we collect data?
Understanding what your organization needs, the ability to be agile and respond to changing dynamics is now more important than ever, says Patrick Hubbard, head geek at IT software company SolarWinds.
“For example, hybrid operations are often the result of organizations adopting cloud, SaaS, HCI, and more to solve specific business challenges, not infrastructure technology limitations,” Hubbard says.
“To provide leadership with analytics they expect, IT teams are increasingly collecting and analyzing non-infrastructure data in combination with traditional performance metrics.”
In online retailing, data like shopping cart abandon rate, total checkout ASP and last-minute upsell CTA performance would be valuable metrics and help measure responsiveness, resulting in faster browsing results, reducing errors and keeping customers shopping with you.
In online retailing, data like shopping cart abandon rate, total checkout ASP and last-minute upsell CTA performance would be valuable metrics and help measure responsiveness, resulting in faster browsing results, reducing errors and keeping customers shopping with you.
“It’s in effect transforming customers into performance monitoring agents to ensure the delivery of high-visibility services and quickly spot issues with their underlying resources,” Hubbard says.
In another industry like healthcare, comparative analytics of mobile hardware AP connection density wit patient occupancy data would be valuable.
Along with assuring that wireless equipment is functioning as intended, teams can use the data to optimize their APs and distribution networks.
“And in most cases, IT pros are finding business operations data is easier to integrate in the past,” Hubbard says. “Though this shouldn’t be a surprise—IT pros are great at finding ways to get the data they need.”
What kind of data should we be collecting?
Collecting data from every corner of each of the organization’s IT systems may seem like the right approach since in theory you should be able to learn more with that amount of information, but that can be more trouble than it’s worth, Hubbard says.
“But in reality, too much data can be worse than too little data,” Hubbard says. “If you need a data lake for operations metrics, it can distract the team from what really matters—managing and transforming the critical systems of the business.”
A better approach, Hubbard says, is to use your monitoring and management tools to measure, then assign value to operations data.
“Observability and visibility are really about zooming back your monitoring perspective to consider new or more helpful instrumentation,” Hubbard says.
“With that in hand, select the most valuable subset of all available data that best measures IT and infrastructure operations relative to the needs of the business.”
That’s a different approach from simply connected to monitoring interfaces and collecting data from every application, server and network report.
“It’s an ongoing re-evaluation of what to monitor, what data to keep, where blind spots are, and what detail the team needs for reconfiguration, optimization, and troubleshooting,” Hubbard says.
Data collections systems: three primary options
According to Hubbard, there are three main options for data collection solutions: single vendor tools associated with products, free or in-house developed technology and third-party commercial solutions.
Although there are three options, most IT pros end up adopting third-party tools because they tend to aggregate most data in one system.
“This in turn allows multi-source data analysis in an increasingly decentralized environment,” Hubbard says. “It also reduces time spent building and managing data collection platforms themselves.”
There are a variety of options available, ranging from on-premise to hosted solutions; agent or agentless based collection; network, app, or cloud focus; and self or vendor-managed installations.
“As is the software industries habit, solutions are offered to fill just about every nook, cranny, and team requirements’ profile,” Hubbard says. “It’s a golden age for data collection, and IT pros have more and better options than ever before.”
Deploying data gathering infrastructure
IT pros can deploy data collection gathering infrastructure in two plans, each with different options, according to Hubbard.
First is the storage and analysis plane that acts as a central collector, data analyzer, dashboard and report server. Some can be self-hosted, typically running alongside other business applications on virtual servers.
Others are software-as-a-service based, that shift the platform management and hosting outside.
“IT pros generally select the back-end option based on licensing and appetite for making data collection WAN dependent,” Hubbard says.
There’s also the polling or agent plane that connects to infrastructure, apps, APIs and other custom data sources. Components include network monitors, remote collectors, cloud-native app integration agents, log aggregators, traffic analyzers, SIEM and policy analysis, backup monitoring, multipath network analysis and more.
“This plane is also rapidly expanding to include services like Salesforce, Office 365 and Google Workspace, marketing and production applications, and more,” Hubbard says.
Typically as an add-on service from the cloud, organizations can also opt to augment their data practice with artificial intelligence and machine learning.
“These systems make the value of all the collected data more easily accessible to less-technical business experts and are focused more about discovery and insight than specific data point collection or reporting,” Hubbard says, citing Microsoft Power BI as an example.
Data collection and a digital transformation is not as technical as you might think
Effective digital transformation is actually less about budget and technology and more about process and habits, says Hubbard.
“With digital transformation, it can be difficult to measure the actual ROI of modernization, migration, or new development despite dozens of online vendor post-transformation calculators,” Hubbard says. “The only thing that matters in transformation is your business, your data, and your processes.”
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Existing monitoring systems can observe newly transformed operations and compare them with performance, costs and user satisfaction before, during and after that digital transformation takes place.
“Reassuring the C-suite that you’ll maintain visibility even during big-budget, high-visibility transformation projects can go a long way to reducing risk anxiety and solidifying resolve to make complex changes,” Hubbard says.
“Better, expanded performance collection can also prove transformed applications are achieving the business goals driving the technology change in the first place.”
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