Data, as often and as generously this word/concept has been used in the public domain, we’d believe that by now its demystified. However, that’s not entirely true. Most industries and professionals have a limited understanding of data and the importance of harnessing the power of data. Some industries and its professionals realize the true potential that data mining and closely-connected technologies like IoT, machine learning, AI offer. Even fewer industries have been the flag bearers who have been early adopters and implementors and have taken technological risks that have been game-changing in their future growth. These companies are witnessing quantitative and qualitative improvements that have a substantial impact on their bottom-line and sustainable growth.
Industries in the Middle East, in general, have been slow movers in the acceptance of data mining and IoT. It is not that the region lacks investment or enthusiasm towards the potential these technologies offer. On the contrary, its lack of awareness (hence, a certain level of hesitation) in the middle and lower management in core industries. In addition, there is a dearth of good solution providers that themselves understand how data is studied and findings are applied back into the business to optimize business processes. Majority of solution providers are offering basic industry solutions that are purely built for the beginner stage. That’s it. The absence of long-term scope, a digitization strategy is the biggest drawback.
Asset reliant industries like Transport & Logistics, Building & Construction, Oil & Gas, and more, can draw great business insight through implementing the right IoT solution that allows them to extract in-depth asset data that can support predictive, preemptive decision-making. Typically, any business goes through three phases of data analysis no matter if a business has or doesn’t have any historical data. To develop a better understanding, these phases are explained below:
INCEPTIVE – Initial or Visibility Stage
Most businesses find themselves start almost at the same level. Some do have limited historical data, however, it is neither critical nor plays a significant role. The primary role is played by the IoT solution provider to lay a strong foundation where initial data can be extracted through active assets on the field to help understand basic usage statistics. The next level of expertise comes from in-house management. It is imperative to understand how asset-related decisions were taken so far and what were the results.
To explain further, a transport and logistics company gathers feedback from drivers and studies their route patterns to decide on which day, what time on certain route delivery can be done in the most efficient manner. When this business know-how is combined with the initial data on the vehicle – location, time, speed, etc. Quick decisions (and trails) can be carried out to verify the findings. This stage is the biggest learning curve for the business, but it also offers plenty of quick rewards and improvements. Basically, in this stage human resource involvement is high. This changes as the business progresses through various stages.
PROGRESSIVE – Analytics & Automation Stage
Businesses enter this stage after months of continuous monitoring and tracking of a major portion or the entire of its asset portfolio. During this time period, the business collects a large amount of valuable data that is studied to infer protocols, rules, alerts, notifications, and actions that can be taken by the application. Based on the learnings extracted from the machines, the application can improve its accuracy and outcome.
Human involvement begins to reduce in this stage and most of the decisions are taken remotely. Taking the above example further, routes can be optimized on the go, drivers can be notified, alerts can be generated on time, fuel, driving behavior, and more. Furthermore, improver and unauthorized use of vehicles can be mitigated/eliminated. As the volume of data increases and analysis is carried out, the application and the business processes become smarter and more efficient.
ADVANCED – Predictive & Autonomous Stage
In this stage human involvement is minimum. By now the business has collected years worth of in-depth data that has been studied and analyzed and applied to streamline processes. The application has evolved through various stages of learning and reached a point where it is autonomous enough to take predictive measures to consistently optimize operations. Going back to our original example, in this stage the application will smart enough to predict which vehicles need maintenance, refueling, overhauls, planning the quickest route on-the-go based on area of delivery, assigning drivers to areas that they best know/mostly work in, providing feedback to drivers to improve their driving behavior, informing the warehouse ETA of the vehicle to keep the next load of deliveries ready, and much more.
This is the stage when end-to-end optimization takes places. Where all points of the supply chain/operations are connected to keep talking to each other and planning and improvising as they go. Human resources are only required to oversee these processes and make a few core decisions. Everything else is controlled by the application and the machines/assets. This can be further enhanced by adding layers of Artificial Intelligence and other technologies to make a business more flexible, preemptive, and sustainable.
Businesses need to understand that complete optimization and efficiency can no longer be achieved without studying data. The power of data will only continue to grow in the coming years. For businesses to reduce cost and improve quality, they need to invest resources in data mining, IoT, and machine learning. Only businesses that adopt and evolve will succeed in the current economic ecosystem.
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