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About The Big Data
Talent Framework

The Big Data Talent Framework is tailored to address the skills needed to effectively analyse and ultimately harness Big Data that can provide a competitive edge to organisations in this data-driven era. The Framework’s key component is to outline the data professional’s talent roles within organisations while conforming to ongoing advancements in disruptive technologies and transformative innovation to align with Malaysia’s integration into Industry 4.0.

The Framework was conceived as a touchstone for organisations' talent evaluation as well as becoming a platform for talents to measure themselves against the skill sets needed in the Big Data Analytics industry market.

WHY DO WE NEED A TRAINING FRAMEWORK?

Talent is one of the main enablers to become a
data driven organisation

Looking through a Data Driven Organisation (DDO), we have observed a number of talent-related challenges across multiple industries:

  • Unclear academic or professional definitions of data science and what is expected from it.
  • Misalignment among professional training providers and industries.
  • Undefined data and analytics roles in organisations.
  • Industries are focused on the data science roles but tended to have less of an understanding of the other roles within the data eco-system causing severe “shortage” of data related talent in Malaysia.

Misalignment amongst academia,
professional training providers
and industries

Undefined roles
in organisations

Lack of understanding of
the roles in the data
eco-system

No consistent definition of a
Data Scientist

Inconsistent job
definitions

Shortage of qualified talents
in the market for the whole
data eco-system

Poor roles / job
descriptions

This highlights an immediate requirement for a framework to help standardise Data & Analytics talent development in Malaysia and train high quality local data professionals to address the current shortage of Data & Analytics talent.

Data & Analytics
Strategy

“Implementing a data driven decision making culture and installing and effective data science team”

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Data and Analytics Strategy

Key activities:

  • Develop the data & analytics strategy in-line with the business goals and objective

  • Identify, develop and structure a data & analytics team to deliver against the business objectives

Skillsets:

  • Data and analytics strategy development
  • Team structure, key skillsets and roles & responsibilities within the data and analytics team

  • Effective organisational structure set-up and execution

Data
Architecture

“Controls, checks and balances in place to ensure data integrity, quality and auditability”

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Data Architecture

Key activities:

  • Develop and maintain an enterprise wide data infrastructure diagram outlining key systems hand-off, critical data path, storage capacities, data capture and cleansing processes.
  • All systems and systems architecture are future-proofed
    e.g. Big Data capabilities

Skillsets:

  • Infrastructure diagram development and maintenance
  • Data fundamentals and preparation

Data
Governance

“Data managed in the correct way throughout the data lifecycle”

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Data Governance

Key activities:

  • Establish a cross department committee to oversee and maintain the data governance activities including data dictionaries, definitions, data standardisation, data normalisation, PDPA compliant and security
  • Appoint a lead to champion data governance best practices across the organisation.

Skillsets:

  • DPA aware
  • Data dictionary management
  • Open data Malaysia initiative
  • Developing and maintaining an enterprise wide data dictionary

Data
Management

“Data managed in the correct way throughout the data lifecycle”

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Data Management

Key activities:

  • Understand the end-to-end data lifecycle ensuring that each stage of the data lifecycle or transformation is mapped and documented
  • Proactive data management which includes the sourcing of new data sources/types, security systems, access rights management and analytics/visualisation tools 


Skillsets:

  • Source to target mapping
  • Design extract, transform and load (ETL) process 

  • Python R SQL 

  • Able to develop and maintain a data 
warehouse including data marts and lakes 

  • Data handling techniques

Handling
Big Data

“Ability to work with large volumes of data at high speeds with increasing data varieties”

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Handling Big Data

Key activities:

  • Understand the core principles of distributed computing
  • Understand the impact of and how to handle increasing data volume, velocity and variety. 


Skillsets:

  • Python, R, CUDA

  • Apache big data framework such as: Pig, hive, MapReduce, Kafka,
  • Text analytics (NLP)
  • Hadoop, no SQL architecture and components

Reporting
and Visualisation

“Telling the (business) story from data”

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Reporting and Visualisation

Key activities:

  • Leverage on data mining or business intelligence tools to provide business information / trends for decision making
  • Assist in the qualitative / quantitative analysis of data and generate reports of analysis

Skillsets:

  • Tableau, Qlik, SAS, Excel
  • Python R SQL 

  • Effective storytelling – turning findings into business applications 

  • Presenting insight skills

Statistical
Analysis

“Understand data relationships, connections and infer causality”

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Statistical Analysis

Key activities:

  • Develop, implement, and evaluate statistical models to support predictive modelling or to describe business trends

Skillsets:

  • SPSS, SAS, Mat lab
  • Analyse report and trends
  • Evaluate testing methods for statistical model

Experiment / Testing

“Trial and error, fact based decision making”

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Experiment / Testing

Key activities:

  • Introduce a culture of continuous testing
  • Support tactical and operational business decisions through experiments and testing

Skillsets:

  • Experiment set-up and execution (control groups etc.)
  • A/B and multi-variate testing

Models
for Production

“Executing on the models”

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Models for Production

Key activities:

  • Model deployment and lifecycle management – agree model refresh interval factors
  • Understand the importance of model management for the organisation

Skillsets:

  • Python, R, SQL, Talend
  • Data Engineering skills
  • Understand collaborative version control systems
  • Model lifecycle management
  • Model update (new data or algorithms)

Business
Applications

“Executing on the models”

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Business Applications

Key activities:

  • Application of specific data and analytics techniques to address the relevant business needs
  • Map-out the analytics applications used by the organisation and its inter-dependencies

Skillsets:

  • Customer Value management model, network analysis, manufacturing line optimisation

Machine
Learning

“Taking business insights to the next level”

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Machine Learning

Key activities:

  • Understand the applications of ML in the business context 

  • Develop and implement machine learning in the organisation to support business objectives

Skillsets:

  • Python, R 

  • Ability to adapt existing models 

  • Use specialised libraries for deep learning such as Keras, Tensorflow
  • Use of specialised libraries for out-of-core learning such as SparkMLLib or Mahout 

  • Use a range of ensemble methods to increase the effectiveness of the predictor

Artificial
Intelligence

“Cognitive decisions made by machines”

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Artificial Intelligence

Key activities:

  • Develop/acquire a NLP dictionary
  • Agree the application of artificial intelligence in organisation

Skillsets:

  • Natural language programming
  • Image recognition
  • Speech recognition and synthesis

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    Terms & Conditions:

    • BDA Talent Framework is a sole intellectual property of Ansys ADAX and cannot be reproduced without prior permission from Ansys ADAX in any shape or form

    • Contribution or feedback from ADAX Partners are Big Data & Analytics (BDA) related only

    By being a BDA partner, I will include ASEAN Data Analytics eXchange (ADAX) in my press materials.
    I have read and accepted the terms and conditions.