OQLIS: Leaders in IoT data analytics and prediction
Author: Johan Steyn
OQLIS has grown to become a provider of IoT data analytics with a proven track record to interpret and predict data, empowering business leaders to make better decisions and anticipate demand.
When it comes to Artificial Intelligence (AI) platforms and solutions, many South African business executives aren’t aware of the local enterprises that provide them. I am delighted to be affiliated with OQLIS in my roles as an author, AI business consultant, and conference speaker who works with AI thought leaders from around the world.
I recently had the privilege of interviewing the company’s co-founders, Shawn Winterburn and Andrew Bosma. We talked about how their solutions are based on helping executives see and make sense of massive amounts of data in real-time.
I was interested to learn more about how they see the Internet of Things (IoT) and about how this technology has evolved in light of the advent of AI.
Smart sensors on the Edge
Sensor technology that collects data sent to dashboards for analysis is not new. Infrastructure management, security, agriculture and energy management are some of the areas that have benefited from devices that collect data.
These “dumb devices” are unable to interpret the data they collect or communicate with one another. A team of human operators are always needed to manually interpret the data for decision-making.
In a world where the distribution of large data sets at high speed became possible through enhanced computational technology like the Cloud and 5G networks, it has become difficult for people to interpret the data effectively and fast enough. The challenges these days are not only to see trends from the data but also to predict what the data is showing and make effective business decisions immediately.
The advent of “Edge computing” introduced a new level of complexity to the scenario. The rapid development of the processing power of individual sensors has led to the proliferation of networks and devices positioned at or near the device location.
Edge computing refers to the technique of processing data at a location that is geographically closer to the origin of the data. Increasing the processing power placed closer to the point of use improves the management and utilisation of the firm’s physical assets. This enables the processing of data at faster speeds and in greater quantities, which eventually leads to enhanced real-time action-driven outcomes.
The challenge of data in Edge computing
Shawn told me that this is a very dynamic and fast-evolving field. In the past, it was a complicated process to make sense of business data, but they have seen how this technology offers corporate leaders new opportunities to make smarter, better and more informed decisions.
Andrew was quick to add that utilising the benefits of sensor data is not easy. Deploying Edge sensors introduces the challenge of analysing volumes of data at velocity. The ability to harvest data from all their sensors is only the start for organisations. The real challenge is around aggregating and exploring that information. “You need sophisticated technology that allows you to ingest that data and store it and then subsequently analyze it to make those strategic decisions,” he added.
Before beginning to reap the true benefits of Edge computing, organisations must overcome several difficulties. The protection of corporate and individual personal information and privacy is a challenging obstacle. Malicious actors are able to insert unauthorized code or even replicate entire nodes, allowing them to steal and change data without being discovered. They are also capable of tampering with data as it traverses the network by attacking the routing information of the network, which can affect throughput, latency, and data paths by deleting and substituting data.
Controlling who has data access is the next challenge. Due to the physical isolation of edge devices in a distributed computing system, many devices within the system are responsible for handling identical data. This raises the likelihood of security breaches and makes it more difficult to monitor, authenticate, and allow access to data.
Scalability is the most challenging barrier to overcome. At each remote Edge location, many monitors are required to determine the overall health of each IT component, including the physical access, power, and cooling, as well as the servers and network devices. As a result, it is challenging to perceive and comprehend the status of the entire Edge ecosystem as well as the influence that each Edge component has on the other components.
The OQLIS solution
What is needed is intelligent software that holistically manages widely dispersed and diverse sensor inputs. This is accomplished by gathering, integrating, and evaluating information from numerous sources. This management abstraction layer is responsible for facilitating monitoring and administration with a “low touch” approach, hence eliminating the need for human involvement.
As original equipment manufacturers (OEMs) continue to add sensors to a vast array of products, such as pipelines, weather stations, smart meters, vehicles, data centres, facilities, retail, and healthcare-specific equipment, the potential applications are expanding exponentially.
OQLIS enables enterprises that have invested in IoT systems to manage and communicate key performance indicators by actively viewing and analyzing data to track, trace, and measure key metrics across operations in real-time.
The OQLIS platform can analyze huge volumes of data from a variety of sources and provide the results clearly and concisely for users who wish to increase productivity, reduce expenses, or get a deeper understanding of their environment.
Andrew told me that one example of how their platform helps clients is around smart buildings. “How do you achieve a correlation between the number of people in the building and the amount of oxygen that those people are breathing in order to maximize productivity? You need environmental sensors that are tracking the CO2 levels in a building. Using AI to interpret and action the data you might want to automatically open windows at certain stages or predict where the trend of CO2 might be going based on the volume of people coming into the building.”
Shawn added that the OQLIS platform provides their clients with the ability to measure what could never be measured before. “This includes seeing the correlation between data and improvements in operations in real-time, managing operational and ecosystem constraints and providing ongoing feedback between sales, marketing and production.”
He added that their platform enables business operations to effectively manage their insights, without the need for data scientists. “It is all about saving costs and improving efficiencies.”
Intro the future
I was keen to learn from Shawn and Andrew about how they see the future of Edge computing technologies. “I think there’s a huge opportunity for businesses to leverage these technologies. Obviously, as time goes by these sensors are getting cheaper, and the technology is getting increasingly better,” Shawn mentioned.
He added that, in time, it will benefit their customers in a lot of different ways in terms of being able to utilise computing technologies “on the edge” so that they don’t need to have very expensive backends. “I think there’s going to be really some exciting stuff that’s going to come through in sensors going forward.”
I was impressed. It is clear to me that OQLIS has grown to become a provider of IoT data analytics with a proven track record to interpret and predicting data, empowering business leaders to make better decisions and anticipate demand.
You need a technology partner who is truly interested in your success and can take you by the hand on your journey from the data desert to the AI lake. Have a look at their website and make contact with them today: www.oqlis.com
Rated as one of the top 50 global voices on AI by Swiss Cognitive, Prof. Johan Steyn is on the faculty of Woxsen University, a research fellow with Stellenbosch University and the founder of AIforBusiness.net.