As I have written extensively before, the primary purpose of any data you collect or manage is to derive actionable insights from that data using various types of data analytics. A casual browsing on data analytics will tell you that there are 4 types of data analytics and they are: Descriptive analytics, Diagnostic analytics, Predictive analytics, and Prescriptive analytics. Descriptive analytics focuses on what happened, diagnostic analytics relays why it happened, predictive analytics previews what is likely to happen and prescriptive analytics conveys options on what you should do about it. But you’ll be missing out on an exciting area called Edge Analytics if you relied solely on this type of classification.
Let’s look at the scenario of an offshore oil rig which has hundreds of sensors collecting data but miles away from any decent data center to process and analyze this data. What if the sensors had access to decentralized process systems that could perform data analytics and possibly shut off a faulty valve right then and there based on the diagnosis and prediction? Wouldn’t that be more efficient than sending all that sensor data back to central data centers miles away and relaying back the same information much later? Yes, that’s where edge analytics comes in.
WHAT is Edge Analytics?
Simply put, Edge analytics is the collection, processing, and analysis of data at the edge of a network either at or close to a sensor, a network switch or some other connected device. With the growing popularity of connected devices with the evolution of Internet Of things (IOT), many industries such as retail, manufacturing, transportation, and energy are generating vast amounts of data at the edge of the network. Edge analytics is data analytics in real-time and in-situ or on site where data collection is happening. Edge analytics could be descriptive or diagnostic or predictive analytics.
WHY Edge Analytics?
Is edge analytics another gimmicky term invented just to make our lives complicated? Not really. Organizations are deploying millions of sensors or other smart connected devices at the edge of their networks at a rapid pace and the operational data that they collect on this massive scale could present a huge problem to manage. Edge analytics offers few key benefits:
- First benefit is to reduce latency of data analytics. In many environments such as oil rigs, aircraft, CCTV cameras. remote manufacturing environments, there may not be sufficient time to send data to central data analytics environment and wait for the results to meaningfully impact decisions to be taken on site in a timely manner. As mentioned in the oil rig example in the introduction, it may be more efficient to analyse data on the faulty equipment right there and shut off the valve immediately if needed.
- Second benefit is scalability of analytics. As the number of sensors and network devices grow, the amount of data that they collect also grows exponentially and it increases the strain on the central data analytics resources to process these huge amounts of data. Edge analytics enables organizations to scale their processing and analytics capabilities by decentralizing to the sites where the data is actually collected.
- Third benefit is that edge analytics helps get around the problem of low bandwidth environments.The amount of bandwidth needed to transmit all the data collected by thousands of these edge devices will also grow exponentially with the increasing number of these devices. And many of these remote sites may not even have the bandwidth to transmit the data and analysis back and forth. Edge analytics alleviates this problem by delivering analytics capabilities in these remote locations.
- Lastly, edge analytics will probably reduce overall expenses by minimizing bandwidth, scaling of the operations and reducing the latency of critical decisions.
WHEN should edge analytics be considered?
Even though edge analytics is an exciting area, it should not be viewed as a potential replacement for central data analytics. Both can and will supplement each other in delivering data insights and both models have their place in organizations. One compromise of edge analytics is that only a subset of data can be processed and analyzed at the edge and only the results may be transmitted over the network back to central offices. This will result in ‘loss’ of raw data that might never be stored or processed. So edge analytics is OK if this ‘data loss’ is acceptable. On the other hand, if the latency of decisions (& analytics) is not acceptable as in flight operations or critical remote manufacturing/energy, edge analytics should be preferred.
WHO are the players in edge analytics?
Apart from the smart sensors and connected devices to collect data, edge analytics requires hardware and software platforms for storing data, preparing the data, training the algorithms and processing of the algorithms. Most of these capabilities are increasingly being delivered on general purpose server / client and software platforms. Intel, Cisco, IBM, HP, and Dell are some of the leading companies driving edge analytics.
WHERE is edge analytics deployed the most?
Given that edge analytics benefits organizations where data insights are needed at the edge, Retail, Manufacturing, Energy, Smart cities, Transportation and logistics vertical segments are leading the way in deploying edge analytics. Some use cases are: retail customer behavior analysis, remote monitoring and maintenance for energy operations, fraud detection at financial locations (ATMs etc.), and monitoring of manufacturing & logistics equipment.
HOW to deliver edge analytics?
Getting to edge analytics is not an overnight task and it typically involves creating the analytics model, deploying the model and executing the model at the edge. There are decisions that need to be made in each of these areas with respect to collecting data, preparing data, selecting the algorithms, training the algorithms on a continuous basis, deploying/redeploying the models etc. The processing/storage capacity at the edge also plays a key role. Some of the merging deployment models include decentralized and peer-to-peer deployment models with pros and cons for each.
As far as I am concerned, edge analytics is an exciting area with organizations in Industrial Internet Of Things (IIOT) area increasing their investments year over year. Leading vendor companies are aggressively investing into this fast growing area In specific segments such as retail, manufacturing, energy, and logistics, edge analytics delivers quantifiable business benefits by reducing latency of decisions, scaling out analytics resources, solving bandwidth problem and potentially reducing expenses.
About the author:
Ramesh Dontha is Managing Partner and Editor-In-Chief at Digital Transformation Pro, a management consulting and training organization focusing on Big Data, Data Strategy, Data Analytics, Data Governance/Quality and related Data management practices. For more than 15 years, Ramesh has put together successful strategies and implementation plans to meet/exceed business objectives and deliver business value. His personal passion is to demystify the intricacies of data related technologies and latest technology trends and make them applicable to business strategies and objectives. Ramesh can either be reached on LinkedIn or Twitter (@rkdontha1) or via email: rkdontha AT DigitalTransformationPro.com