Data Holds the Golden Key of the Future


By Thulasyammal Ramiah Pillai

Senior Lecturer, Taylor's University

Products, businesses, healthcare, automobiles, education, the environment, and many more other areas exploit the data to shape their future. Data literacy is the mother of necessity in the future as the world has progressively achieved digital literacy.

Apple’s sleep tracking application of the Apple watch and the smart cars with various sensors tracking user behaviour utilising the generated data. Business analytics uses the data to make decisions for companies.

Patient care data can be utilised to offer patients with wholesome care, share information to improve patient health and assist in diagnosis.

The automobile manufacturers must have a robust supply chain of the components of the cars. Furthermore, this industry manufactures data-driven smart cars, and their predictive analysis facilitates the automotive industry to ensure better maintenance of the vehicle.

Educational data implements the educational process, producing outputs that include student progress, success, achievement, and personalised guidance.

Meanwhile, environmental data has been an important tool in combating climate change. The customer financial and preferential data enable the businesses to market their products and services to the targeted customers.

These are the factors highlighting that the world is moving fast towards a data driven world.

Facets of Data

These different sets of data had been described as big data, fast data, and actionable data.

Big data contains greater variety, volumes, velocity, intrinsic value, and veracity. Fast data promotes real-time interactions.

International Data Corporation (IDC) predicts that 30% of the global data will be real-time by 2025. Actionable data bridges the gap between big data and business value. This data can be processed and analysed to extract meaningful insights. There are various tools to process and analyse these data.

Tools and Skills

1.      Data Pre-processing Tools

Data fabric is the data integration and management solution. Its components are unified architecture, and software that allows consumers to integrate data from multiple sources which facilitates seamless access and data sharing from a distributed network.

Augmented data management utilises AI to automatically regulate metadata, data quality, database administration and data consolidation. This helps the workers to be more productive, reduce manual tasks and reduce human error in the workplace.

2.      Analytics Tools

Descriptive Analytics describes the data using numerical measures, tables and graphs using various data visualisation techniques.

Predictive analytics predicts future events using historical data, statistical and mathematical modelling, data mining methods and machine learning algorithms.

Prescriptive analytics analyses data and offers recommendations on how to optimise business practices to match the predicted outcomes.

X analytics trains and tests any type of analytics on structured and unstructured data, no matter where or in what format that data resides (Gartner).

Augmented analytics provides instant AI-powered insights by using natural language processing, and machine learning algorithms to produce data-driven solutions.

3.      Intelligence Tools

Continuous Intelligent channels real-time analytics into business operations, processing data, analysing incoming data against historical data, and offering innovative ideas instantaneously.

Business Intelligence makes data-driven decisions, using business analytics, visualisation, and infrastructure to optimize the performance of a business.

Explainable AI visualizes a model and explains the advantages, disadvantages and how it performs in a situation. Explainable AI builds the trust of the consumers.

Digitization and Digitalization

Digitization and digitalization have been accelerated by the pandemic in the data driven world.

Digitization is the process of changing analogue processes into digital form. Digitalization uses technology to innovate and reinvent the products and services.

The “Digital Transformation” uses the technology to enhance and restructure the business framework. Gartner projects that, by the end of 2024, 75% of organisations will fizzle out from pilot programs and will bring about full-fledged big data strategies.

This paradigm shift will increase the streaming data and analytics facilities at the rate of 500%.

Hence, MDEC has been assisting the companies in Malaysia with their digital transformation plans via eRezeki, Global Online Workforce, Go eCommerce and Malaysia Digital Hub.

Skills Needed

The top 3 scarcest skills needed for the “Digital Transformation” are data analytics, security, and AI. Hence, Taylors’ University has been producing graduates with these skills to support the MDEC initiatives.

Taylors’ University undergraduate and postgraduate students had designed and developed various AI products. The postgraduate students had developed predictive models to quantify the health impact of Malaysian citizen due to air pollution in collaboration with Ministry of Health Malaysia (MoH).

The undergraduate students had developed various applications for the industry via their final year project namely Facial Recognition Hotel Check in Application, Online assessment in educational System, Pocket-Money tracking Application, Teaching Statistics through Gaming and many more.

The undergraduate students also do their data science and data analytics projects in their Statistics and Data Science Principles modules.

Every organization is packed with data that should be exploited intelligently to stay competitive in the market. Data holds the golden key of the future for the sustainable development of any organisation.

Author’s Profile

A 27-year experienced lecturer, fully committed as an academic professional who has deeply moved oneself in lecturing and tutoring students from various social and cultural backgrounds at higher institutions.

She is specialised in Applied Statistics. She has acquired a deep understanding in Data Analytics and Machine Learning.

Currently she is experimenting the application of gamification in the teaching and learning of Mathematics and Statistics.

She is involved in a multi-disciplinary research group in the field of Data Analytics, Big Data, IoT, AI, Health, Environment, Transportation, Travel Behaviour, Smart Sustainable Cities and Business.

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