Big data has become the talk of town, for it not only saves costs and improves business agility, it also gives the power to foresee threats
Internet of things (IoT) or machine-to-machine communications has revealed whole new universe of information/data for both enterprises and consumers. Through big data, both business entities and consumers can become more cognizant. Through the collection and analysis of data, trends and configurations surface, IoT provides a host of relevant information.
Everybody in the industry is talking about how big data can streamline businesses, make it more effective and agile and improve decision making. Big data is not just about the amount of information that needs to be managed. The difference between big data and traditional business intelligence (BI) is the utter scale and complexity of the data as well as the speed of using the data for making key decisions.
Simply put, it is a collection of data so huge that traditional data processing applications or on-hand management tools cannot decipher or manage. The data is just too fast or too big to gain any valuable information from. Examples of such unstructured data range from social media posts, twitter feeds, and metadata to web server logs, satellite imagery, traffic flow sensors, and telemetry from automobiles, GPS trails… the list goes on. The key to liberation of valuable assets from this huge gamut of data lies in connecting these data sets, understanding and analysing them to provide meaningful results.
Big data analytics and connected enterprise will be the two most important M2M trends in the years to come. A recent study by Berg Insights says the number of wireless connections will reach 489.2 million in 2018, fuelled by developments in the areas of big data analytics and connected enterprises. M2M and data analytics are attempting to shift from descriptive and diagnostic analytics to predictive analytics. This is a paradigm shift that can bring in an evolutionary change in data interpretation.
How can enterprises benefit?
Big data and M2M will play an integral part of enterprises. The ability to transform raw data from devices and machines to decision-making processes allow operational and financial trends to be pinpointed more easily in real time. M2M with its proven ability to save time, cut costs, enhance customer service and improve operational efficiency will play a crucial role in big data analytics of enterprises. This makes sense for enterprises to derive additional value by transferring data sets into mainstream business applications and enterprise services.
Why will it be a disruptive trend?
Processing entire data sets in high-speed memory opens the way for more predictive analysis, data mining, what-if analysis and results to be visualised, which is the easiest way to remember information. Fast easy-to-run analytics enables users to pose questions they wouldn’t have even thought of asking before.
How can it optimise businesses?
M2M data in manufacturing: Let’s take the example of the manufacturing industry. What traditionally happens when a machine or system breaks down is that an alarm is raised and the issue is looked into by engineers. What M2M enabled with big data can do is intimate us of this impending breakdown even before the incident occurs.
Sensors can be placed on the manufacturing floor and these sensors can transmit data to a central unit that can constantly monitor, analyse and raise alarm in the event of a pending breakdown. This is a much more cost-effective approach than incurring a breakdown and having to halt production until a replacement part can be ordered and installed.
M2M data in healthcare: The potential of big data analytics and M2M has not ceased to attract the healthcare sector either. An increasing number of healthcare practitioners and hospitals have started using M2M data and analytics to predict patients’ reactions to various drugs and its dosages.
According to IDC, following are the capabilities for which healthcare organisations intend to use analytics:
l The ability to identify patients/members in need of care management (cited by 66 per cent of respondents)
l Clinical outcomes (identified by 64 per cent)
l Performance measurement and management (identified by 64 per cent)
l Clinical decision-making at the point of care was (identified by 57 per cent)
Data sources from social media, unstructured clinical data and mobile devices are being used to support accountable care.
In conclusion, without data management there is no value in M2M applications. Big data proves the perception that information is power. By using the nadirs of knowledge assimilated through big data analytics, businesses can power themselves with an improved understanding of its resources. The significance of M2M lies primarily in the analysis of data and companies with an aim to generate serious revenue from M2M will unquestionably need a big data strategy. zz