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Turning big data into deep data
The coming of big data has also heralded the biggest conundrum of recent times for business. While businesses now have access to terabytes and more of data, they lack the ability to optimally utilize the data. There is lack of experts who can cull the relevant data, analyze it and turn it into usable insights. Thus, despite sitting on a goldmine, businesses can’t find the gold.
There are certain industries, however, which seem to have cracked the code. For example, in the transportation industry of the U.S., connected sensors provide telematics data which is used to enhance fleet operations. This is being successfully implemented by United Parcel Service of America Inc. to convert their trucks into what they call “rolling laboratories” which provide in-depth data into the entire functioning of the trucks. Thus, they have data with regard to fuel consumption, mileage, wear and tear of tyres and other parts, engine hours etc. Such a deep wealth of data enables them to be fully in-charge of their fleet and its maintenance schedules.
Drivers and fleet managers can be warned before time by using DTCs or diagnostic trouble codes that are generated from the electromagnetic devices onboard the vehicles. Important components such as engines and refrigerators as well as other components are connected via sensors to telematics data. The information thus collected can be put to action if the need so be or it can be aggregated, analyzed and prioritized as per need to help the drivers and fleet managers. This is one of the biggest examples of big data being put to its optimal use.
Making the data actionable, however, can be a burdensome task as it involves providing access to everyone in the service supply chain so that the problem is resolved in time. Under normal scenario, this would mean several phone calls, emails, search for information and excessive paperwork leading to loss of time. If the service delivery process has to be made efficient and effective the data access must be provided to all people in the service supply chain such as the driver, mechanic, fleet manager, vehicle and equipment manufacturer, service providers and others.
A more effective approach is to integrate the telematics data with the service-relationship-management-solution in a closed loop. With the use of SRM raw data can be quickly converted into intelligent information that is actionable. This information can be immediately shared with all the members of the closed loop. Also, since SRM deploys cloud to store data, the data is always available to anyone in the closed loop via any device or at any location. Thus, the extra time needed in the usual process of sending emails, making phone calls, searching for information and doing excessive paperwork is avoided. The time to diagnose the problem and find a solution is reduced, thereby, reducing the actual time taken to conduct repair work. This in turn ensures that the vehicle recovers from the damage more quickly and is ready to provide service.
While the above example explains how telematics is used in the transportation industry to make optimal utilization of data for service delivery, the same process of using sensor data and combining it with SRM software can yield similar results for other industries, manufacturing units or commercial activities. Other industries can take the example of the transportation industry and turn their databases into deep knowledge banks.
While the above is one solution, the era of artificial intelligence and machine learning is upon us. An IDC report notes that by 2025 about 20% of the data produced globally will be extremely important to our daily lives. Of this 10% data will be hypercritical to our living. Even though this sounds ominous, it implies that businesses must pay special care in the collection and analysis of data as well as the infrastructure they deploy for the process so that the reliability of the network, its bandwidth and availability are of a very high standard. In turn, this must lead to extremely secure environments including new business practices and legal policies to protect such hypercritical data and prevent potential liabilities.
In the coming years, there will be greater use of AI and machine learning in analysis of big data. However, as per the same IDC report, the data that will be tagged for AI and machine learning analysis by 2025 would only be 15% of the global data. The data size will further shrink as only 20% of the tagged data will be analyzed by the cognitive systems. Thus, this renders the collection stage of data important and its methods to be sanitized so that more data can be tagged for analysis, thereby resulting in a deep wealth of insights.
The field of data analytics and big data lacks experts and corporations are looking for them. This is the best time for young aspirants and professionals to seize the opportunity by attending data analytics online courses which will prepare them for jobs in the industry.
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