Data analytics in the era of industry 4.0
A. Gusnanto
University of Leeds, Leeds, UK
ABSTRACT: In the era of globalization, the presence and information and communication technolgy is inevitable. Its rapid growth demands industrial aspects to employ effective data analytics.
This paper highlights the importance, impacts, and challenges of data analytics in the fourth industrial era. In this paper, the journey and estabslishtment of industrial revolution has been discussed in the Introduction part preceding the discussion of the importance of data analytics, particularly in the fourth industrial revolution. The focus of data analytics is this paper is big data which will benefit both business and academic sectors. It has been proven that big industries impacted by big data are mostly related to telecommunication and manufacturing. However, there are also challenges of data analytics. Some of the challenges include weak ability to identify problems, absoption of genetal statistical principles, information technology infrastructure, and data governance and legal aspects of data.1 INTRODUCTION
The Government of Germany has adopted the Industrie 4.0 initiative (Bundesministerium fur Bildung und Forschung 2019) that elaborates the goverment’s strategy in dealing with the fourth industrial revolution. Similar initiatives are also adopted by major industrial and developing economies, such as Industrial Internet (of Things) (Jeschke et al. 2017) and Internet+ (Keane 2016). The fourth industrial revolution is considered a continuation of the first to the third industrial revolutions. The first one started around the 18th-19th century, which was characterised by energy revolution where the manufacturing processes were transformed with the advent of steam engines. This was followed by the second industrial revolution around the turn of the 19th to 20th century that was indicated by more efficient manufacturing process, and the use of electricity and internal combustion engines.
The third industrial revolution was characterised by digital revolution, the invention of personal computer and automation process around 1970-1980s. The fourth revolution is indicated by cyber physical system (Lee et al. 2015), where automation process is characterised by data exchanges in the internet of things (Zhong et al. 2017).In this fourth revolution, data is at the heart of everything. For example, the concept of ‘smart manufacturing’ is a system in which data exchanges are pivotal in the communication between sensors (as data generation tools) and the controllers, so that they can give feedback to the system to adjust the manufacturing process to be optimal (Davis et al. 2012). The feedback itself is interpreted as data by the control system. In a general context, Industry 4.0 has to embrace the arrival of big data (Kusiak 2017), which is characterised by 4 V’s: Volume, Velocity, Variety, and Veracity (Hashem et al. 2015, Yin & Kaynak
2015). The volume of data that are generated everyday grow exponentially and it is no longer uncommon to find businesses that generate gigabytes of data everyday (Lee et al. 2014). The term velocity refers to the type of data that are considered as streaming data. A famous example would be data that are generated by hundreds of millions of users in social media. The data are generated continuously and massively, across a wide network (Wollschlaeger et al. 2017). However, it is also possible to have this type of data in general when, for example, business operations involved are also ‘continuous’ such as airline travel, traffic or logistic network, broadcasting, hotels or services, and other online internet networks (Wang & Wang 2016). In terms of variety, the types of data that are generated in Industry 4.0 vary quite considerably. Some of them are in text format, while some others are, for example, in picture, voice and video format (Hashem et al. 2015). Each of these types of data needs analytical tools that are specific for them.
Lastly, the term veracity refers to the situation where either, first, the information in the data are ‘thinly’ spread in the whole data and they are only meaningful when we combine that information or, secondly, the relevant information are located in a small unknown part of the whole data (Gandomi & Haider 2015). The latter means that other parts of data may be relevant for our context, but they are not necessarily relevant for the problem at hand.2 IMPORTANCE OF DATA ANALYTICS
With such characteristics, data analytics becomes crucial in the era of Industry 4.0. In this paper, we shall highlight some of the benefit of data analysis, particularly with big data. The first and foremost benefit is accurate prediction (Lee et al. 2014). In the current business climate and other factors that often change, many businesses and organisations depend on accurate prediction to adapt with current changes. Long time ago, these changes happened within years or months. Accurate prediction with data analytics was not urgent because all parties have time to digest what was going on and had enough time to adapt. However, at the moment, the changes can happen in hours and minutes. Data analytics becomes crucial to create prediction because with many information in the data, our mind may not be fast enough to focus on the most relevant bits of the information. Even if we can focus on them, it still takes time to digest the bits of information before any action can be considered (Vercellis 2011).
Accurate prediction can advantage businesses in many different ways. The main one is to increase and improve business operation, especially in managing resources and inventories, and to increase business efficiency (Gregori et al. 2001, Wen et al. 2003). A famous example of this is the pricing method for airline tickets that have to take into accounts many factors to make accurate prediction on how much ticket should be sold to maximise profit and occupancy, in real time. Another advantage of accurate prediction is to optimise marketing campaign by tailoring the campaign to potentially suit individual customers, mainly in the context of online transaction (Chen et al.
2012). The prediction is done based on the customersaAZ historical data, to identify what the customers need. Businesses then can tailor promotions or discounts on products or services that they need (Linoff & Berry 2011). On the other side of this prediction, the same principle can also be used to detect fraud and abuse of service. This is because, based on past historical data, businesses can predict “reasonable” behaviour of their customers and will flag a behaviour that out of the ordinary, probably in quite a big way (Chen et al.2012). Banks and card service providers have implemented this principle in their system that would detect account fraud, for example (Cardenas et al.
2013).
Another benefit of data analytics is that, using statistical principles, it can turn data into knowledge and knowledge into insight (LaValle et al. 2011). Why is this important? Because decision maker can then turn the insight into action. Data analytics is critical to understand the underlying, possibly unobserved, patterns of the data. To get into insight, statistical principles need to be considered even from the start when any idea was still conceived. An example of this is utilisation of customer survey by businesses. Many believe that survey is a method to get customer feedback. However, if statistical principles are adhered, survey is not only a reflection of how a business gives service to its customer, but also how they operate.
As an implication of the above, data analytics enables decision making to be taken quickly where relevant (Provost & Fawcett 2013). With fierce competition and so much data around us, it is no longer adequate to rely on intuition only when making business decision. Data analytics is able to give guidance to make decision quickly, which is a major advantage. Business competitiveness is about staying ahead in the competition, and this business trait will give that advantage to stay ahead.
3 INDUSTRY IMPACTED BY DATA ANALYTICS
Which industry will be impacted by (big) data analytics? Virtually all industries will be impacted by data analytics, especially those industries that involve big data such as telecommunication and manufacturing (Gandomi & Haider 2015, Sagiroglu & Sinanc
2013).
Optimising service quality, development of new products, efficiency, and waste reduction are some examples of “target” of data analytics that will give great impact to industries. Retail industry is an example where data analytics gives a major impact, where online transaction, customer segmentation, marketing, pricing, and optimisation of logistics and distributions have benefited from data analytics (Linoff & Berry 2011). Other industries, from finance to transportation, from healthcare to agribusiness are expected to get much greater impact in the future (Chawla & Davis 2013, Groves et al. 2013, Lv 2015, Waller & Fawcett 2013, Wolfert et al. 2013).Big industries are not only the ones that will be impacted by data analytics. Small and medium enterprises have begun to benefit from data analytics. A small ice cream shop in Leeds (UK) managed to understand their ice cream sales (Brennan & Mark 1994). They recorded their sales in detail including the types of ice cream they sell, and they also record the weather and their posts in social media. The data was then analysed, and the results indicate that the increase in sales happened when the shop posted pictures of ice cream in social media. This is just a simple example where data analytics can benefit small businesses. Other examples exist in the context of restaurants, suppliers to big supermarket chain, and other services Bi 2014, Coleman 2016, Watson
2014).
4 CHALLENGES AHEAD
Looking forward, data analytics in the era Industry 4.0 face four main challenges, especially in the developing countries. The first and main one is a weak ability to identify problem. Data analytics serves as a solution to a problem, since the it is shaped by the formulation or definition of the problem. Now, many believe that the absence of ‘bottleneckaAZ in their business process is a sign that there is no problem. Others believe that a reasonable growth or profit is a sign that there is no problem.
These cannot be further from the truth. Almost all of the disruptions in Industry 4.0 target those businesses that tend to be ‘complacent’ or easily satisfied. These businesses consider that processes that have been going on are those that should have been going on, when this is not necessarily true. Therefore, to identify problems, businesses have to be critical of themselves and always test their business processes. After all, changes that take place from within is always better than those that are forced from outside.The second challenge is the absorption of general statistical principles. Statistical principles are needed in data analytics since they are able to guide us in understanding the problem, getting the right information, understanding the results, and taking conclusions. This does not mean that everybody has to take a course in statistics or become a statistician. However, there are some general principles in statistics that are useful to deal with the above challenges.
The third challenge is the information technology infrastructure that are in urgent need to be further developed, especially in the developing world. In the era of big data, technology is critical for data analytics and all other activities that support data analytics, including cloud technology, network, storage, etc. They make sure that data, as information, can flow seamlessly and data analytics can be performed while their results, in themselves are ‘new’ data, can be communicated to relevant parties.
The last challenge in data analytics is the data governance and legal aspects of data. Some countries have different levels of privacy setting to protect their citizens. It is therefore still a challenge to provide adequate level of privacy protection in some of them (Sadeghi & Wachsmann 2015). European countries have set an excellent example in this aspect where they recently implemented the general data protection regulation (GDPR), in which the data governance is outlined in detail (EU Commission
2016). With this regulation, every party recognise their share of responsibility in ensuring that data privacy is protected while at the same time allow data analytics to be conducted.
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