Intro to Data Analytics and Singapore’s Smart Nation Strategy

This informative piece summarises the National Artificial Intelligence Strategy and provides an overview of data analytics. Singapore is embarking on a Smart Nation Strategy for the nation to realise productivity […]

November 15, 2022

This informative piece summarises the National Artificial Intelligence Strategy and provides an overview of data analytics.

Singapore is embarking on a Smart Nation Strategy for the nation to realise productivity benefits for the overall workforce and to create new growth opportunities. Singapore’s smart nation program aims to go beyond just adopting technology and rethink business models so that the nation can make impactful changes to reap productivity gains and create new growth areas.

Singapore’s Smart Nation Program

Singapore aims to build a vibrant and sustainable ecosystem to drive AI innovation and adoption.

Five critical ecosystem enablers are identified:

  1. Triple helix partnerships between the research community, industry and Government enable the rapid commercialisation of fundamental research and deployment of AI solutions.
  2. Talent and education address the need to develop homegrown talent across AI-related job roles and help Singaporeans prepare for the future AI economy.
  3. Data architecture enables quick and secure access to high-quality datasets across various sectors.
  4. A progressive & trusted environment is essential for test-bedding, developing and deploying AI solutions.
  5. International collaborations to drive and support the sustainable development of AI with multi-national researchers, businesses and governments.

Singapore has also identified seven National AI Projects that can deliver strong social and economic impact for Singapore and Singaporeans:

  1. Healthcare – Chronic disease prediction and management help with faster detection and treatment of such diseases.
  2. Innovative estates – Municipal services are delivered more responsive, reliable and timely for citizens.
  3. Education – Personalized education through adaptive learning and assessment helps teachers customise and improve their students’ learning experience.
  4. Border security – Border clearance operations strengthen security while improving travellers’ experience.
  5. Logistics – Intelligent freight planning optimises freight movement for greater business productivity and traffic efficiency.
  6. Finance – Growing Singapore into a global hub for financial AI solutions.
  7. Government – Leveraging AI to transform government services to deliver high-impact outcomes for citizens and businesses.

With this backdrop, we go into detail about what data analytics is.

What is Data Science

Data science is the scientific management of data and the methodologies, techniques, and skills connected to data that are used to extract useful information, discoveries, and knowledge from data from numerous domains. Data science is becoming essential due to the increasing value of data and associated procedures.

It also refers to applying many data processing techniques, such as data collection, extraction, purification, manipulation, enumeration, tabulation, combination, inspection, interpretation, simulation, and visualisation. The numerous approaches and methods for handling data come from various fields, including computer science, mathematics, and statistical analysis. But it’s not just restricted to these fields. Similar and significant applications can be found in social science, medical science, architecture, business management, and national defence and safety areas. These areas include marketing, production, finance, and training and development.

Data science is a broad phrase that encompasses all techniques and technologies used to extract meaningful information from data.

What is Big Data

Big data is frequently referred to as data with “large volume, enormous variety, and high velocity.” Big data is the vast collection of information that businesses have gathered from various sources, including:

  • Internet and social media
  • Mobile applications
  • Remote sensing and radio-wave reading equipment
  • Wireless sensors
  • Smartphones and other multimedia devices
  • Remote sensing and location tracking equipment.

According to the leading worldwide research and advisory company Gartner, “big data” refers to high-volume, high-velocity, or high-variety information assets. These assets necessitate efficient, cutting-edge information processing methods to improve insight, decision-making, and process automation.

Structured, unstructured, and semistructured data sets are the three types of big data. Well-organised and systematic data are referred to as structured data. Unstructured data refers to information that has not been organised or given a common framework and is stored in its raw form. Between these two is semistructured data, which has both amorphous and structured elements.

Other data sets can be divided into historical (or past information data) and current categories (novel and most recently collected information data). Data sets can be categorised into first-party data (controlled by the company directly from their customers), second-party data (bought from another organisation), and third-party data depending on the source of data gathering (the composite data obtained from a market square). Businesses frequently store specially designed software for keeping big data, which can be quickly computed and analysed to find exciting trends from information on numerous stakeholders.

What is Data Analytics

Data analytics examines data sets to find trends and draw conclusions about the information they contain. Data analytics applies algorithmic methods and code languages to large amounts of data to make valuable and appropriate decisions. As a result, using the analytical component of data science on big data or raw data to extract insightful findings and information is referred to as data analytics. It has received much attention and practical use across sectors for making strategic decisions, developing, testing, and refuting theories.

Data analytics focuses on the logical deductions made following the execution of analytical algorithms. Considerable data manipulation is required for data analytics to extract contextual meanings that can be used to develop business strategies. Organisations combine machine-learning algorithms, artificial intelligence, and other systems or tools for data-analytics tasks to make insightful decisions, plan innovative designs, and provide the best possible customer service.

What is Business Data Analytics

Business data analytics is the data analysis process to understand, interpret, and predict patterns in business and then use those data-driven insights to enhance business practices. A company enterprise can find valuable pearls by knowing what they want their data sets to address. Their discovery may benefit businesses in terms of productivity, revenue generation, and profitability. Business analytics applies various data analytics tools, techniques, and systems to an extensive data set to produce fascinating insights, simulation models, strategic judgments, and tactical plans. Businesses can avoid future problems operating in a competitive environment by properly and strategically using analytics.

What are the applications of Data Analytics in Business?

Management of Production and Inventory

  • Product development – to gain knowledge about consumer needs and wants preferences, and the latest trends
  • Supply chain management – to keep the flow of inbound logistics
  • Inventory management – to maintain economic order quantity, just-in-time purchases, and ABC analysis of stock items
  • Production process – to seek productive efficiency gains from the resources put to use

Management of Sales and Operations

  • Retail sales management – for product shelf display and replenishment, running special discount sales and loyalty programs.
  • Outbound logistics – to ensure proper physical distribution to different business locations
  • Warehouse and storage management – to maintain proper upkeep and ready-to-serve features

Price Setting and Optimisation

  • Price determination of goods and services – for analysis of the indicators like factor input costs, competitors’ price lists, and price elasticity trends
  • Tax and duty – adjustments regarding different duties, levies and taxes, computations, and calculations
  • Determining features like discounts, rebates, special prices or coupons
  • Optimisation of input costs and overhead costs for maintaining sustainable profitability

Finance and Investment

  • Stock market – to track stock performance, future trends, and the company’s future earning potential
  • Capital budgeting decisions – for making investment decisions, dividend decisions, or determining the valuation of a firm
  • Investment banking – for lead book-running, arriving at mergers, and amalgamations decisions
  • Credit rating generation, financial fraud detection or prevention, portfolio creation, the management or diversification

Marketing Research

  • Segmenting, targeting, and positioning strategy formulating.
  • For the search engine optimisation process, to return the best and relevant results from search queries run in real-time.
  • Advertising – from the idea conceptualisation to content creation and designing banners or billboards or directing the advertisement.
  • Creating a recommendation system in this era of e-commerce so that products or services reach the appropriate and targeted audiences
  • Consumer-relationship building activities by maintaining close links and contacts with consumers, for personalised marketing activities for brand loyalty, and to constantly better the business in providing memorable consumer experiences

Human Resource Management

  • Recruitment and selection for conducting background checks, screening candidates, and calling eligible candidates for interviews
  • Training and development schemes for building and polishing the skills that employees lack or for the infusion of new skills as per trending needs
  • Compensation management for successful motivation, retention, and satisfaction of employees by giving them a good mix of both financial and nonpecuniary motives
  • Performance appraisal for seeking information regarding employee promotion and transfers, career development, and attrition rate

The benefits of insights derived from Data Analytics

In this digital age where consumers express their preferences at a click or tap, each click or tap speaks volumes about valuable insights. That is to say, every tap or click reflects usable information for the business firm and thus becomes potential data for business analytics. For example, business analytics can yield important information like the picture of the segmented or target market or how to position the brand message in a specific segment or target market. In addition, consumer likes, comments, or reviews can serve as usable data sources. By tapping the data regarding a consumer’s likes or comments, the marketer can form an understanding of their demographic or psychographics. The marketer can use the generated insights to hone future consumer experiences or pass on the insightful knowledge to other advertisers for better consumer connections.

Machine Learning and Artificial Intelligence

Machine learning is the machine’s ability to keep improving its performance without humans explaining exactly how to accomplish all the given tasks. Thus, when a machine learns to perform some functions on its own, barring the need for overt programming, to improve the user experience, it is referred to as machine learning. Furthermore, computer programs automatically improve machine learning through computer algorithms.

Machine learning comprises three types:

  1. Supervised – where the data analysis groups the output under already labelled patterns
  2. Unsupervised – where the data analysis groups the result under novel ways in an unlabeled manner
  3. Reinforcement – where the data analysis happens by constantly taking cues from the environment while continually learning to extrapolate for new outputs 

Machine learning has evolved to become a dazzlingly magical buzzword in the business world thanks to the capabilities and advancements it offers. With the advancement of technology and changing times, it has recently taken on a new meaning.

Artificial intelligence refers to a human-made way of acting, comprehending, or performing actions within a system. Artificial intelligence, or AI, is used when human-like intelligence is added to machines or computers to carry out tasks or activities.

Businesses are now actively collecting consumer data using machine learning and AI to enhance brand experiences in the future. While machine learning is a step in the direction of AI, the field is vast. Extensive data analysis allows for exploring new trends and minute details that can be used to actuate strategies.

Machine learning analyses the data patterns to automate the functions, increasing efficiency and effectiveness. At the same time, artificial intelligence (AI) makes a computer do brilliant work solving multiplex problems with human-like intelligence. In contrast to machine learning, which typically relies on predetermined algorithms, AI runs on the central theme of spontaneity. However, both are essential decision-making tools for developing business strategies.

Benefits of data analytics to Singapore businesses

The main objective of data analytics is to support individuals and organisations in making well-informed decisions by identifying patterns, behaviours, and trends in a data set. This translates into various advantages for businesses, such as pinpointing client preferences and purchasing habits, locating weak spots in a business’ infrastructure to streamline internal procedures, or forecasting future trends to inform overall business strategy.

E-commerce is where data analytics is used to the advantage of Singaporean platforms like Lazada and Shopee. It offers priceless insights into consumers’ purchasing patterns and behaviour, enabling a better shopping experience through predictive analytics by making practical product recommendations, shortening the time to purchase, minimising cart abandonment, and replenishing popular items on the schedule.

Singaporean SMEs can use data analytics to enhance their operations. This includes improving services and products based on sentiment analysis derived from data analytics, creating consumer personas using demographic data for better advertising, optimising their websites for SEO conversion for increased traffic, and more. Brand awareness and recognition are also increased.

References

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