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
    • Cognitive
    • 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
    • Cognitive
    Editor's Pick (1 - 4 of 8)
    left
    Agile Transformation Journey

    Sachin Nair, VP CIO, Khan Bank

    Responsible AI: The Human-Machine Symbiosis

    Sal Cucchiara, CIO & Head Of Wealth Management Technology, Morgan Stanley

    Seamless Integration into Networking Industry

    Robert Lewis, CIO, Assurant

    Enhancing Customers' Experience through Technology

    Marc A. Hamer, VP & CIO, Babcock & Wilcox Enterprises, Inc.

    Digital Transformation in Fashion Retail - From Efficiency to Experience

    Le Van, CTO, YODY Fashion

    Making Sense of Artificial Intelligence

    Joe Zirilli, Vice President, Artificial Intelligence, Parsons

    Revolutionizing Architecture and Construction: The Synergy of Artificial Intelligence and the Internet of Things (AIoT) in Building Smart Structures

    Raymond Kent, ASTC, Assoc AIA, LEED AP BD+C, Senior Technology Design Leader, Principal, DLR Group

    A Record of RPA

    Osmond Li, Senior Manager, Head of Technology Innovation, Dah Chong Hong Holdings Limited

    right

    Smarter Information Supply Chains

    Pete Crawford, Head of Data, Analytics & Automation, Sensis

    Tweet
    content-image

    Pete Crawford, Head of Data, Analytics & Automation, Sensis

    Over the past two years we have witnessed a proliferation of open-source prediction APIs and ‘out-of-the-box’ cognitive technologies packaged via cloud computing platforms, enterprise CRMs or other solution providers. Examples include Google Cloud AI, AWS Comprehend, Azure AI and Salesforce Einstein. Consequently, the adoption and integration of machine intelligence into product development or marketing automation operating models has wider ramifications for data management and data literacy across global industries. Although much has been written on the role of big data in stimulating a shift in the way predictive analytics is viewed, the key drivers for many companies are still centred on existing data platform integration and the explicit inclusion of ‘external’ data sources.

    However, the evolution toward smarter information supply chains and a ‘data economy’ (irrespective of concurrent labour force adjustments) is often unclear and the business benefits ill-defined. Several forces are shaping how business strategy adapts to the emergence of cognitive technology platforms.

    FORCES SHAPING THE DATA ECONOMY

    Regulatory Compliance and Explainable AI

    On either side of the Atlantic, government bodies are currently exploring adjustments to consumer data protection policies to strengthen the rules and structures which may demand the disclosure of automated decision making. For instance, Article 22 of the European GDPR directly addresses safeguards around profiling – an issue which has received publicity in the US from analysis suggesting inherent biases in prediction software connected to criminal recidivism based on racial grounds. Meanwhile, in the UK a Centre for Data Ethics and Innovation Consultation has been established to monitor the applications of data-driven and AI-based technologies. This leads to the prospect of greater regulatory expectations and auditing around translating machine learning models so that the intent behind the systems; the data sources feeding it; and other input rules are ‘explainable’.

    Of equal importance will be trade-offs between personalized predictions and regulation of private data. As data becomes a more tradable commodity, existing data management notions of data cataloguing and data lineage are receiving greater focus.This is one area where machine learning is very much helping classify and organise data assets across multiple platforms in support of data governance, compliance obligations or collaborative data modelling.

    Democratization of Data Science

    The notion of automating a data science pipeline – from obtaining data to cleaning and normalising data through to model selection, evaluation and result interpretation – is a topic of deep contention.

    As data becomes a more tradable commodity existing data management notions of data cataloguing and data lineage are receiving greater focus

    Indeed, there is more to data science than combining programming skills with statistics. What is typically missed is an ability to observe and ideate with respect to business problems. There is a real risk of misinterpretation based on the assumption that ‘the data will tell me the answers’ as promises of autonomous analytics are promoted by platform providers or new automated algorithm selection or tuning frameworks such as DataRobot and Auto-sklearn. It is also likely that greater stratification and industry specialisation will occur as data science tools become more ubiquitous. An addition risk occurs with the definition and remuneration of a typical ‘data scientist’. Recent studies show that data scientist salaries have declined, which reflects the somewhat unrealistic financial outcomes expected from application of data science to business problems. At present data science is overtly orientated towards developing engineering solutions of business problems which are often undefined or poorly defined. The risk of over-promising goes hand-in-hand with the risk of undervaluing the human factors in data science.

    The most immediate impact in this space is access and ready implementation of data agnostic geo-spatial and temporal analytic visualization tools. One example would be Uber Engineering’s open-sourced Kepler.gl. This heralds that maps are becoming a common layer to rapidly communicate or compile real-time streams of data to customers, partners and suppliers.

    Data Marketplaces and Data Protectionism

    Data has an inherent combinational value. This means that the ability to provide ‘insights-as-a-service’ APIs based on predictive or prescriptive analytics is contingent on both data quality and compounding data sources. In most cases, businesses only benefit from this ‘compounding factor’ by integrating open or licensed data with their own proprietary sources. In short, cognitive technology and more accurate predictions are powered by broader and deeper data. This exposes a major tension between data acquisition and data protectionism.

    One one hand, data marketplaces, or platforms for securely and conveniently buying and licensing data or data models are proliferating. For instance, Microsoft, Adobe and SAP have recently announced an open data initiative together. From a business perspective this can allow the trading of aggregated merchant data about localised consumer purchases, insurance claims or even anomaly detection of financial transactions. However, this is tempered by a trend toward data localization whereby nations (such as China and India) or provinces are regulating cloud data to localise it at the point of origin.

    This clash amongst public, government and technology companies will greatly influence how cognitive technology can take advantage of information supply chains.

    Conversational Analytics

    Natural language processing (NLP) is now well established across various consumer devices with estimations that already over 25 percent of queries on Android devices are voice-based. The new frontier for NLP is analytical insight tools. Reporting tools such as Tableau have recently released a product that use plain language to query complex combinations of data. The implications here are that analytics can become more accessible across the organisation driving broader adoption. Once again, the need for a data literate workforce – especially around framing inputs and interpreting outputs will be paramount.

    IMPLICATIONS

    It would be naïve to assume that cognitive technologies alone will seamlessly offer automated analytical capabilities and business transformation. As ever, CIOs need to balance rapid experimentation and judicious evaluation with a willingness to understand the cultural and political factors which are shaping information supply chains and the data economy. An approach which acknowledges a need for a data-literate workforce and an awareness of greater regulation will best serve the opportunities that machine learning and predictive analytics offer.

    tag

    Data Management

    Machine Learning

    Financial

    Predictive Analytics

    Big Data

    Cloud Computing

    Adobe

    AWS

    Weekly Brief

    loading
    Top 5 Cognitive Solutions Companies in Hong Kong - 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

    Advancing the Chemical Industry through Digital Transformation

    Advancing the Chemical Industry through Digital Transformation

    Jan Mandrup Olesen, Global Head of Digital Business, Indorama Ventures
    Cultivating a Sustainable Future through Collaboration

    Cultivating a Sustainable Future through Collaboration

    Jiunn Shih, Chief Marketing, Innovation & Sustainability Officer, Zespri International
    Mastering Digital Marketing Strategies

    Mastering Digital Marketing Strategies

    Tasya Aulia, Director of Marketing and Communications, Meliá Hotels International
    Building a Strong Collaborative Framework for Artificial Intelligence

    Building a Strong Collaborative Framework for Artificial Intelligence

    Boon Siew Han, Regional Head of Humanoid Component Business & R&D (Apac & Greater China), Schaeffler
    From Legacy to Agility Through Digital Transformation

    From Legacy to Agility Through Digital Transformation

    Athikom Kanchanavibhu, EVP, Digital & Technology Transformation, Mitr Phol Group
    Change Management for Clinical Ancillary Teams: Aligning Practice with Policy and Progress

    Change Management for Clinical Ancillary Teams: Aligning Practice with Policy and Progress

    Ts. Dr. James Chong, Chief Executive Officer, Columbia Asia Hospital – Tebrau
    Digital Transformation: A Journey Beyond Technology

    Digital Transformation: A Journey Beyond Technology

    John Ang, Group CTO, EtonHouse International Education Group
    Building A Strong Data Foundation: The Key To Successful Ai Integration In Business

    Building A Strong Data Foundation: The Key To Successful Ai Integration In Business

    Richa Arora, Senior Director Of Data Governance, Cbre
    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://cognitive.apacciooutlook.com/cxoinsights/smarter-information-supply-chains-nwid-6317.html?utm_source=google&utm_campaign=apacciooutlook_topslider