Business Analytics

How Big Data Analytics Will Transform the Shipping Industry

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* Dr Theodosis Mourouzis is the Programme Director of the MSc in Business Intelligence and Data Analytics at the Cyprus International Institute of Management (CIIM) and a Research Fellow at the UCL Centre for Blockchain Technologies.

Big Data Analytics is the science of uncovering insights, patterns and correlations and discovering meaningful information from mounds of data that exists in various forms, structured and un-structured, using techniques from different scientific fields such as statistics, data modelling, machine learning, mathematics, computer science, neuroscience, visualisations, business intelligence and many others. It could be characterised as the process of turning data into insights and insights into meaningful actions, enhancing in this way the decision making capabilities of a company and as a result reducing costs and improving upon performance.

Data science or data art could be characterized as an attempt to shift away from the traditional empirical-based reasoning to a formal, scientific, data-driven way of thinking and operational tactic. The research firm IDC’s estimate of the size of big data market for last year, 2016, was $136B and this is expected to grow exponentially in the coming years with more and more companies deploying data science related projects and new startups that offer products based on data rising. Data science has already proved itself and analytics are extensively applied across many different sectors and industries such as retail, banking, financial services, security, telecom, healthcare, shipping and many others.

Data analytics is driving incremental value for ship owners and charters by influencing decision across different business, tactical, operational as well as strategic functions of the marine industry. As more and more data are collected, stored and analysed, shipping companies are beginning to appreciate and thus aim to utilise the value of this data in order to make informed decisions, managing in this way the company in a better and more efficient way. The shipping industry is inevitably undergoing a massive but beneficial change driven by Big Data capabilities across different areas:

  • Fuel consumption: Combination of the appropriate sensors and optimisation techniques can be applied in order to understand under what conditions a given ship has optimised fuel consumption at maximum performance. This can be translated into huge savings.
  • Route and supply-chain optimisation: Advanced analytics and optimisation techniques can be applied on the data related to the routes followed by the ships in order to derive an optimal strategy related to the order of the different destinations across different routes to be followed.
  • Operational efficiency: Optimize marine operations, manage staff time efficiently and identify cost savings through comprehensive maritime data that include information about ships, ownership, builder, company, ports and route details.
  • Threat management: Identify companies that pose credit and security risks, with extensive ship, company and Automatic Identification System (AIS) data.
  • Market size and competition: Understand the world fleet, ship and ship ownership information, as well as new markets.
  • Maintenance prediction: Through sensors on the ships combined with advanced predictive analytics techniques can be applied in order to identify which areas of the ship need priority in terms of maintenance. This will ensure that maintenance is considered at the optimum moment, preventing delays, increasing efficiency and reducing the time required for a ship to be in maintenance mode.
  • Cargo tracking: A big problem in shipping industry is that many shipping containers are lost every year due to different factors. This costs a lot of amount of money and time for investigation. A solution is to apply data analytics on a datasets related to these lost containers and derive some special characteristics or features about those containers and their environment. This might help to reduce similar problems in the future and thus avoiding extra costs due to losses.
  • Regulatory compliance: Use ship and ownership/registration data to determine any connection to sanctioned countries or countries posing legal or financial risk.

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Shipping industry would start competing on analytics and embracing the new science of winning by investing in data science capabilities within their enterprise. The ideal candidates to drive this revolution are the so-called data scientists. Among the responsibilities of a data scientist is empowering management and officers to make informed and potentially better decisions, direct the actions based on trends which in turn help in goals definition, promote best practices in the fields of business intelligence and data governance, transform the decision making capability into a quantifiable data driven procedure, quantification and redefinition of the enterprise’s strategy and deployment of analytics models within the enterprise’s pipeline. A data scientist shall have skills spanning across many diverse fields such as statistics, software engineering, machine learning, data analytics and mining, data visualisation and very importantly communication skills. It is important that enterprises and organisation interested in competing on analytics to promote data-drive culture within their organisation and invest in educating their existing employees with respect to this field.

CIIM’s MSc in Business Intelligence and Data Analytics is a unique and innovative-by-design postgraduate degree that combines both managerial and technical aspects around the data science field and it is designed to equip the candidates with the necessary knowledge and a diverse set of skills required throughout the data analytics lifecycle. This skillset includes business data requirements, data acquisition and integration, data storage, data processing, data analysis, insights derivation, and ultimately, the business deployment of derived insights in a meaningful and successful manner. If you are interested, you can apply here.

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Analytics in Financial Services: Time to appreciate the value

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Around 1980s-1990s, Information Technology (IT) Systems transformed every manual operational and business bank process to a digital one, reducing in this away unnecessary costs and improving upon efficiency and speed. Today, the Financial Services Industry is undergoing a major transformation driven by big data technologies and predictive analytics, moving towards a formal, critical and data-driven decision making approach and shifting away from empirical and ad-hoc procedures. According to Toos Daruvala, director in McKinsey’s New York office, “Every single major decision to drive revenue, to control costs, or to mitigate risks can be infused with data and analytics”.

The adoption of new technologies such as cloud computing, mobile technologies, mobile applications, wearable devices, Internet-of-Things, Social Networks led to a massive grow of the overall available data. These mounds of data, if mined in a proper scientific way they might reveal very useful insights, patterns and correlations that could potentially increase revenues, improve the efficiency, reduce the costs and guarantee the sustainability of enterprises and organizations.

Today, the development of big data storage and processing technologies, such as Hadoop and Apache Spark, allows banks to store, process and analyze data from multiple sources, structured and unstructured, that arrive at different formats and speeds. Thus, financial services are equipped with the necessary tools to answer very complex questions from the data they store. Data are available from social media activity, mobile interactions, server logs, real-time market feeds, customer service records, geolocation data, transaction details, existing databases and many other sources. Below we refer to several applications of data science in financial services sector and especially in banking:

  1. Improved Customer Insights & Customer Acquisition: Clustering techniques can be used to identify clusters of customers representing different behaviors and interactions and thus being in position to offer customized services to the customers based on their real needs. In addition, sentiment analysis or natural-language processing techniques can be applied to social media data, such as Tweets, in order to identify pros and cons of an enterprise and at the same time identify those people that can promote and those that have negative opinion about an enterprise. In this way, banks and financial services can provide customized product offerings and services, improve existing relationships with customers and design better marketing campaigns.
  2. Automated Risk Credit Management: One of the major sectors that have seen unprecedented new solutions leveraging big data is lending and credit scoring. Clustering and classification techniques can be applied in order to compute probabilistic models that would allow an enterprise to identify the credibility of a customer requesting a loan.
  3. Maintaining regulatory compliance: Capabilities of descriptive analytics applied on data at large scale lead to better compliance against internal policies and local or international regulations, especially with regulations related to Know Your Customer (KYC) and Anti-Money Laundering (AML).
  4. Combating cybercrime and fraud: Predictive analytics techniques can be applied in order to identify money laundering practices, fraud possibilities in online banking that is a result of malicious activities such as social engineering, phishing and malware attacks.

In order to make sense of those mounds of data, enterprises are turning to data scientists who are experts capable of getting the right answers out of huge amounts of data. Data scientists are the new weapons of the financial sector that drive innovation and automation, transforming the industry and reinventing practically every facet of banking. These people are tech-savvy business professionals with unique skills and knowledge in statistics, machine learning, artificial intelligence, computer science – especially programming and databases, data management, business intelligence and many others. Among the responsibilities of a data scientist is empowering management and officers to make informed and potentially better decisions, direct the actions based on trends which in turn help in goals definition, promote best practices in the fields of business intelligence and data governance, transform the decision making capability into a quantifiable data driven procedure, quantification and redefinition of the enterprise’s strategy and deployment of analytics models within the enterprise’s pipeline. It is considered among the top jobs in demand for the 21st century.

CIIM’s MSc in Business Intelligence and Data Analytics is a unique and innovative-by-design postgraduate degree that combines both managerial and technical aspects around the data science field and it is designed to equip the candidates with the necessary knowledge and a diverse set of skills required throughout the data analytics lifecycle. This skillset includes business data requirements, data acquisition and integration, data storage, data processing, data analysis, insights derivation, and ultimately, the business deployment of derived insights in a meaningful and successful manner. If you are interested, you can apply here.

* Dr Theodosis Mourouzis is the Programme Director of the MSc in Business Intelligence and Data Analytics at the Cyprus International Institute of Management (CIIM) and a Research Fellow at the UCL Centre for Blockchain Technologies.