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As businesses are rapidly evolving and advanced technology is the main catalyst for this evolution, data seem to be the key feature empowering these changes. Massive amounts of data are generated every day. IoT is the main enabler for this connecting a great variety of computing device using smart sensors. Social media and social platforms also play an important role by recording human activities offering volume and variety of data. Consequently, it is quite interesting how organisations and businesses can exploit these amounts and different types of data to improve their decision-making and gain valuable insight for hidden information. Surely, big data analytics play the most important role in every business procedure today. Moreover, the concept of datafication can help addressing this statement providing the lens for that.
As data is generated every second, put together, big data is closely related to the term called ‘datafication’. As big data, datafication also aims to handle the complexity of the huge amounts of data and reduce uncertainty. Generally, datafication is the process of transforming the large amount of the data generated into computerised, machine readable data (Strauß, 2015). In other words, datafication converts aspects of human existence into data (Lycett, 2013). In order for the importance of datafication to become understandable, Twitter generates itself over 7 terabytes of data per day, while some companies generate terabytes of data per hour due to smart sensors and advanced technology (Salvador & Ikeda, 2014). Taking as an example the airline industry, the information that their frequent flyer keeps is transformed into statistics. The search and purchase history of tickets, the checked luggage, the departure and arrival date and time, the destination and departure city, the miles used, and other similar information is translated into numbers. This is called datafication. This data is then exploited by applying advanced analytics and value is created for the airline companies by making personalised offers to their existing customers or attracting new ones. This concept is then further analysed and several instances in which business value is created by exploiting big data and big data analytics using datafication are presented.
Businesses should firstly improve their big data analytics capability in order to gain priceless value exploiting them. Firstly, there is a need for a big data environment suitable for data storage and data management. Increasingly, data is unstructured, NoSQL databases offer massive scaling and flexibility focusing on high performance of all functionalities. Moreover, Massive Parallel Processing databases and Magnetis, Agile, Deep analysis skills are essential for todays’ businesses. Big data analytics can be successfully applied on data, only if it is stored and processed properly (Elgendy ; Elragal, 2014). Businesses should, consequently, decide among the great variety of choices available, which one suits them according to aim, objectives and culture. Thus, they can have competitive advantage and map their journey based on their data.
Businesses can also develop their big data capabilities by following the latest trends in AI techniques and combining them with the traditional ones. Association rules, regression, classification and clustering along with machine learning and AI techniques, like neural networks, deep learning, as well as time series analysis can give information at succession. Moreover, text analysis using NLP methods, provides powerful insight about hidden information which could not be extracted otherwise. For example, monitoring in real-time and analysing financial reports and textual information gives important feedback on stock price movement and stock price impacts (Lee et. Al, 2014). The data generated in social media is of vital important. Companies are able to leverage those by using social media analytics to understand reactions and conversations between people, a company’s customer for example. In addition, sentiment analysis using NLP decodes sentiments and opinions expressed on social platforms (Elgendy & Elragal, 2014). Companies could exploit this by finding if their customers are satisfied by their products or services analysing their opinions. Visualisation, a key component of BI, is another way to improve the big data capabilities. Such tools can be used in order for complex problems to become visually understandable and coherent by people of different backgrounds (Voges, 2015).
More specifically, big data improves businesses’ performance, and, in some cases, this is undoubtedly visible. Regarding datafication from a marketing perspective, two main opportunities are created (Jarvinen et al, 2012): access to a lot of digital tools suitable for marketing purposes, and marketing is now more measurable due to the digital environment (Pauwels et al, 2009). Big data analytics and more specifically predictive analytics, such as clustering and classification models can be applied on data generated through social media every day, as well as huge amounts of data stored in the companies’ databases regarding purchases, consumption activities, preferences and demographic characteristics. This way, consumers’ behaviour is predicted based on personalised profiles and on-line interactions among customers (Salvador ; Ikeda, 2014). Moreover, using machine-learning algorithms, patterns can be identified, so that sales people can adjust prices at a customer-product level (Ferreira et. Al, 2014). For example, Lufthansa uses a big data system for analysing and determining its price structure in real time improving its functionality and its financial performance (Himmi et a., 2017).
Businesses, and more specifically insurance companies, investment or retail banks, can benefit using big data and big data analytics in risk management and fraud detection. The appraisal of risk exposures, the likelihood of gains against losses, patterns of suspicious behaviour, transactions, as well as other types of risks can be monitored and analysed along with advanced big data analytics. This is achieved by calculating statistical parameters and looking for outliers or examining classification data grouped by specific parameters, like location (Elgendy ; Elragal, 2014). Nevertheless, as fraud detection seems to be the key for sustained economy growth, geospatial and visual analysis, discovers patterns for antifraud control (Banarescu, 2015). Furthermore, advanced deep learning algorithms can be used, so that systems act accordingly by learning new types of frauds and threats (Elgendy ; Elragal, 2014). Similarly, risk for airlines is safety issues. Nasa in collaboration with Southwest Airlines created an automated system using machine-learning algorithms, able to crunch large amounts of data in order to find anomalies regarding safety issues (Nasa.com, 2018).
Smart manufacturing consists one of the trends of the modern reality meaning the involvement of big data and analytics for the productivity to be increased and the businesses’ performance improved. Datafication exists in this sector, too, transforming the whole process into structured and unstructured data. New RFID chips using IoT record all kinds of manufacturing information, such as sales details, production date, as well as expiry date, providing transparency through the whole procedure and preventing from overstocking. Moreover, sensors can monitor the goods during the delivery phase reducing the cost of return and defective goods (Liu & Li, 2015). Refering to the chosen domain, Delta Air Lines improved its baggage handling process by connecting all the bags that should be directly transferred and monitoring them in real-time. This way customer satisfaction increased, and the mishandled baggage rate is reduced by 71% for Delta (Himmi, 2017).
All in all, big data analytics seems the biggest opportunity and challenge in modern reality. It can significantly contribute to enhanced decision-making and to quality management transforming businesses to smart ones in today’s business world. Moreover, the airlines industry is a domain with strong data capability succeeding in improving its business activity and aiming to become one of the leaders exploiting big data opportunities.

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