APAC CIOOutlook

Advertise

with us

  • Technologies
      • Artificial Intelligence
      • Big Data
      • Blockchain
      • Cloud
      • Digital Transformation
      • Internet of Things
      • Low Code No Code
      • MarTech
      • Mobile Application
      • Security
      • Software Testing
      • Wireless
  • Industries
      • E-Commerce
      • Education
      • Logistics
      • Retail
      • Supply Chain
      • Travel and Hospitality
  • Platforms
      • Microsoft
      • Salesforce
      • SAP
  • Solutions
      • Business Intelligence
      • Cognitive
      • Contact Center
      • CRM
      • Cyber Security
      • Data Center
      • Gamification
      • Procurement
      • Smart City
      • Workflow
  • Home
  • CXO Insights
  • CIO Views
  • Vendors
  • News
  • Conferences
  • Whitepapers
  • Newsletter
  • Awards
Apac
  • Artificial Intelligence

    Big Data

    Blockchain

    Cloud

    Digital Transformation

    Internet of Things

    Low Code No Code

    MarTech

    Mobile Application

    Security

    Software Testing

    Wireless

  • E-Commerce

    Education

    Logistics

    Retail

    Supply Chain

    Travel and Hospitality

  • Microsoft

    Salesforce

    SAP

  • Business Intelligence

    Cognitive

    Contact Center

    CRM

    Cyber Security

    Data Center

    Gamification

    Procurement

    Smart City

    Workflow

Menu
    • Big Data
    • Cyber Security
    • Hotel Management
    • Workflow
    • E-Commerce
    • Business Intelligence
    • MORE
    #

    Apac CIOOutlook Weekly Brief

    ×

    Be first to read the latest tech news, Industry Leader's Insights, and CIO interviews of medium and large enterprises exclusively from Apac CIOOutlook

    Subscribe

    loading

    THANK YOU FOR SUBSCRIBING

    • Home
    • Big Data
    Editor's Pick (1 - 4 of 8)
    left
    How Have Recent Advancements in Big Data Been Impacting Businesses?

    Marc Solomon, CIO, Bvn Architecture

    When Science Fiction Becomes Science Fact: An Industry Embracing Monumental Change

    Stephen Barnham, Senior Vice President & Chief Information Officer, Metlife Asia

    The Inherent Necessity of Big Data as a Strategic Factor

    Esteban Remecz, CIO, Asia Pacific, ZF Group

    Big Data and Credit Scoring in Indonesia

    Darmawan Zaini, Chief Technology & Product Officer, UangTeman

    Experience on Data Analytics

    Kee Siang Lee,

    Building a Smart City by Better Connected World

    Barry Lerner, South Pacific Regional CIO, Huawei Technologies

    Survival of the Fittest in a Data-Driven World: The Secret is in your Software

    Jason Jackson, Field CTO and Director, Advanced Field Engineering, Pivotal APAC

    Big Data Initiatives need Innovative Thinking to Make Things Happen

    Kah Chai Tan, Group CIO, Sime Darby Berhad

    right

    Data Driven Business Insights, Big or Small: We Like them All

    Pedro URIA-RECIO, Head of Axiata Analytics Center, Axiata Group Berhad

    Tweet
    content-image

    Pedro URIA-RECIO, Head of Axiata Analytics Center, Axiata Group Berhad

    When I worked as a McKinsey consultant, I served the CEO of a bank regarding his small business strategy. I wanted to run regressions on the bank’s data but I was advised against it: “They don’t even understand statistics. How are you going to explain a regression to them?”

    CEOs have always needed to deeply understand human intelligence and emotion to manage enterprise teams. Now machines and algorithms are increasingly becoming part of these very teams. The job of a CEO in the age of artificial intelligence (AI) is to design new combinations of technologies, assets as well as human and machine skills to revolutionize business models. Understanding human intelligence alone is no longer enough.

    Most recent advances in AI have been achieved in the area of machine learning (ML). Vision systems, speech recognition technologies or recommendation engines would not have been possible without ML. ML is a technological breakthrough which allows data scientists to write relatively generic software that can learn how to solve a problem without needing to specify a myriad of detailed step-by-step instructions.

    There are three major types of ML methodologies: supervised, unsupervised and reinforcement learning.

    Supervised Learning: Preparing Math Exams

    One learning method I enjoyed at university was reviewing solved math problems to prepare for the exam. Similarly, supervised learning algorithms learn how to solve a problem by looking at a lot of examples of the correct answer.

    Regressions are by far the most popular supervised algorithm. Regressions try to fit a formula, for example linear or quadratic, in your data. If you think there is a linear relation between your sales and factors such as competition prices, your advertising spend, promotions or even the weather, you can train a linear regression algorithm with historical data for which you already know your sales. The algorithm will learn the right parameters of the formula, which will be used to predict future sales.

    A special kind of regression, called logistic regression, which uses a simple exponential formula, can be used to categorize items into groups instead of providing a continuous numeric prediction. For example, a logistic regression could learn to estimate whether a potential customer will default on a loan or not, by looking at characteristics of historical customers and their loans, for which we already know the outcome.

    Without any doubt the most interesting kind of supervised algorithm are neural networks, which mimic how our brain works.

    The job of a CEO in the age of artificial intelligence is to design new combinations of technologies, assets as well as human and machine skills to revolutionize business models

    Neurons are organized in a network of layers. Neurons in each layer are getting some input from neurons in the previous layer and are giving some output to neurons in the next layer. Complex neural networks with a large number of layers are called deep neural networks. The performance of simple networks improves with more solved examples but only up to a point after which additional data does not lead to better predictions. However, deep neural networks do not seem to have this limitation.

    There are two kinds of neural networks. Firstly, convolutional neural networks make each prediction independently of former predictions and are typically used for image processing, for example, to diagnose cancer based on a medical image, to find your logo on social media or in street images or to detect defective products from images from your product line. However, some problems require remembering the context because the value of each prediction depends on previous inputs or outputs. This second kind of networks is called recurrent neural networks and is typically used for time-series applications such as language processing/translation or video interpretation.

    Unsupervised Learning: Recognizing Cats Or Dogs

    Humans are excellent unsupervised learners. We can learn to tell the difference between a cat and a dog with no training, just by looking at many of them and identifying patterns. But, it is difficult to build a machine that works this way. Unsupervised learning is far less developed than supervised learning for the time being.

    Unsupervised learning is typically used when you do not have solved examples and you need the algorithm to find patterns and classify data for you. For example, unsupervised algorithms such as K-means can be used to segment customers. K-means will group your customers in a predefined number of clusters in a way that clusters are as different as possible from each other and customers inside each cluster are as similar as possible. Other unsupervised algorithms like multivariate gaussian distribution can be used to identify anomalous behaviors such as fraud or machinery faults.

    Reinforcement Learning: Playing Video Games

    Some of my friends in the 90’s managed to pass video games such as Mario or Sonic in a few days by trial and error for hours and hours. Sometimes the only way you can learn about the environment is by interacting with it. Reinforcement learning algorithms learn to perform a task simply by doing it and by trying to maximise a reward. You will need to specify the current state and the goal, the list of allowable actions and the constraints, and most importantly, the reward system.

    For example, Google’s AlphaGo used reinforcement learning to play the ancient Chinese game of Go and eventually defeated South Korean champion Lee Sedol in 2016. Other applications include optimizing a trading strategy or a real-time pricing approach and managing exceptions caused by storms in the maritime industry.

    Conclusion

    Machines can be exceptionally good, sometimes far better than humans, doing very specific tasks, such as identifying cancer in a medical image, but this does not mean computers can understand what cancer is, how it develops or how to stop it. A machine’s narrow understanding of a very specific problem does not involve broader intelligence. At least not yet. You still need a doctor for that.

    Likewise, humans and algorithms will collaborate together in the enterprise. Instilling a data-driven culture whereby the first reflex to solve a problem is looking at data, requires the CEO to understand at least the very basics of the technologies that make it possible to learn from data.

    tag

    Machine Learning

    Weekly Brief

    loading
    Top 10 Big Data Solutions Companies – 2023
    ON THE DECK

    I agree We use cookies on this website to enhance your user experience. By clicking any link on this page you are giving your consent for us to set cookies. More info

    Read Also

    Streamlining Operations and Empowering Teams in Facilities Management

    Streamlining Operations and Empowering Teams in Facilities Management

    Shaye Rogers, Workflow Support Manager, Cushman & Wakefield
    Technocreativity: The Synergy Of Technology And Creativity

    Technocreativity: The Synergy Of Technology And Creativity

    Tran Nguyen Phi Long, Group Head Of Retail Marketing, Pnj Group
    Leading It And Digital Transformation At Ikea: Insights From An Industry Veteran

    Leading It And Digital Transformation At Ikea: Insights From An Industry Veteran

    Sigit Triwibowo, Head Of It And Digital, Chief Technology And Digital, Ikea
    Executive Leadership And Digital Transformation In The Global Fashion Industry

    Executive Leadership And Digital Transformation In The Global Fashion Industry

    Eiko Ando, E-Commerce And Digital Director, Pvh Corporation
    Digital Transformation in Fashion Retail - From Efficiency to Experience

    Digital Transformation in Fashion Retail - From Efficiency to Experience

    Le Van, CTO, YODY Fashion
    Driving IT Transformation at Lactalis Australia

    Driving IT Transformation at Lactalis Australia

    Sabina Janstrom, Chief Information Officer, Lactalis Australia
    AI Adoption in Hospitality: Striking the Balance Between Innovation, Excellence and Trust

    AI Adoption in Hospitality: Striking the Balance Between Innovation, Excellence and Trust

    Phiphat Khanonwet, Head of IT, Onyx Hospitality Group
    The AI Rat Race - Keeping Up with New Technologies or Waiting for Maturity?

    The AI Rat Race - Keeping Up with New Technologies or Waiting for Maturity?

    Andreas Kurz, Global Head of Digital Transformation, ALFAGOMMA Group
    Loading...
    Copyright © 2025 APAC CIOOutlook. All rights reserved. Registration on or use of this site constitutes acceptance of our Terms of Use and Privacy and Anti Spam Policy 

    Home |  CXO Insights |   Whitepapers |   Subscribe |   Conferences |   Sitemaps |   About us |   Advertise with us |   Editorial Policy |   Feedback Policy |  

    follow on linkedinfollow on twitter follow on rss
    This content is copyright protected

    However, if you would like to share the information in this article, you may use the link below:

    https://bigdata.apacciooutlook.com/cxoinsights/data-driven-business-insights-big-or-small-we-like-them-all-nwid-5701.html