Today’s enterprises operate at a global scale, managing multiple facilities and working with partners across several regions. For you, efficient operations is a necessity to remain competitive.
The challenge, naturally, comes from understanding operations across these numerous locations. Why can’t traditional data collection and analytics methods handle these challenges? How can you effectively collect data and gain insights?
Distributed analytics is the answer, made possible with the help of cloud computing. In this post, we’ll examine distributed analytics and the advantages it offers over traditional data collection methods.
Traditional methods of capturing and centralizing data delay insights
Among the common push backs for implementing new analytics capabilities is the argument “we already have tools in place.” While this may be true, traditional methods of capturing and centralizing insights can have significant drawbacks.
According to Thomas W. Dinsmore, Senior Director at DataRobot, these challenges include:
- Memory usage: The amount of data enterprises handle today is too large to be handled by individual machines.
- Processing: The handling of big data also means that single threaded processing — or executing one command at a time — can no longer produce results at the rates we need.
By sticking to traditional data methods, enterprises will have to wait longer for data analyses. Unfortunately, this isn’t acceptable in today’s marketplace. Manufacturers that don’t understand shifts in demand, for example, may be stuck producing goods that won’t be selling as well as expected. The same goes for consumer-facing businesses like grocers and retailers.
Data efficiency leads to accelerated insights and business velocity
It should be no surprise that organizations that are able to achieve data efficiency can benefit from increased insights, improved business velocity and reduced costs. Enter distributed analytics, defined as multiple machines analyzing separate bits of data to answer problems. Cloud computing makes this possible by enabling the analyzing of data simultaneously across several locations.
We’re already seeing distributed analytics in practice within industries like healthcare. Siemens Healthineers Digital Ecosystem, for example, allows hospitals to compare their medical device usage against other connected hospitals around the world.
This works due to the following process:
- The ecosystem is powered through Dell EMC World Wide Herd (WWH), which creates a single computing cluster across all hospitals.
- When an analytics request is made, WWH disperses the computational requirements across computing nodes, which store data from local hospitals.
- The requests are completed in the nodes where the needed data resides.
- The results are then sent back to the requesting user.
Through this process, someone in a North American hospital could find out - in real-time - how their MRI machine usage compares to all others connected to this ecosystem in the world.
Distributed analytics does have its nuances
Despite the potential of distributed analytics, there are several nuances that you should understand. Among the most important is that not all tasks can be distributed the same way.
Dinsmore broke tasks into the following three types:
- Embarrassingly parallel: Tasks that can be performed independently of each other. For example, two workers should perform SQL queries at the same time.
- Linear parallel: Tasks that can be performed independently of each other, though the desired result relies on a higher-level calculation of all tasks. Examples include weighted means and benchmarks.
- Data parallel: Tasks that can be performed independently of each other as long as each performer has a meaningful piece of the data.
Creating data parallel tasks oftentimes requires a reorganization of data across workers, which adds time to problem solving (i.e., latency). That’s not all—some tasks can’t be made parallel at all. In these cases, the employees executing the tasks must communicate with each other to ensure proper data collection and analysis.
In other words, distributed analytics needs to be approached with the user in mind.
You need the right strategy to implement distributed analytics
Cloud computing allows for enterprises to leverage distributed analytics to better understand their operations and processes across all locations. Yet implementing this requires expertise outside of traditional analytics. New data solutions must be considered.
We at Antuit are those experts, specializing in developing customized data solutions to address our clients’ specific needs. We develop and execute a strategy to integrate your existing data technology with big data platforms, IoT systems and new applications to deliver on-demand insights for your enterprise.
Cloud computing and distributed analytics are new and powerful concepts that every organization should consider. We’re here to show you how.