BUSINESS ANALYTICS (BA): DATA DRIVEN DECISION MAKING (2 DAYS LEADERSHIP MASTERCLASS), DoubleTree by Hilton Hotel Jakarta - Diponegoro, Selasa, 29. Oktober 2019

WHAT YOU WILL LEARN Business Analytics uses statistical, machine learning, operations research and management tools to drive business performance. Companies such as Amazon, Google, HP, Netflix, Procter and Gamble and Capital One uses analytics as competitive strategy. The course is designed to provide in-depth knowledge of handling data and Business Analytics’ tools that can be used for fact-based decision-making using real case studies.


This workshop series is suitable for business and technology professionals, leader, director, executive, CSO, CXO,

FOR LEADERS Gain a working knowledge of data science, which will enable leaders to identify the challenges that analytics, machine learning, and artificial intelligence can solve. It will also help them make the most effective investments in people, data, systems, culture and organizational structure.

FOR INNOVATORS Explore what a sound data analytics strategy can do for you. Together, we’ll experiment, explore and embrace business innovation opportunities for growth in familiar - and completely new - areas to help you do more than you ever imagined possible.

Business Analytics may be thought of as a series of skills, technologies, processes and tools by which we can analyse and convert data not just into management information but into predictive insights and business intelligence (BI). Move beyond reporting what is happening and into why things are happening and consequently what might happen in the future. 



Module 01: Business analytics as a competitive strategy.
• Differentiate business intelligence (BI) from business analytics & decisions.
• Set objectives, use cases and goals.
• Define criteria for success and failure.
• Select methodology and data.
• Identify relevant internal and external factors.
• Validate models using predefined success and failure criteria.

Module 02: Analyzing data using statistical learning and machine learning algorithms.
• Differentiate machine learning from statistics.
• Choose between statistical modeling and machine learning.
• Use statistical methods in a machine learning project.

Module 03: Data visualization & storytelling through data.
• Understand the importance of context and audience.
• Pick the best data visualization format for your story.
• Recognize and eliminate the clutter.
• Direct audience to the most important parts of your data.
• Utilize the concepts of design in data visualization.

Module 04: Descriptive, predictive and prescriptive analytics techniques and tools.
• Learn from past behaviour to influence future outcomes.
• Identify risk or opportunities in the future.
• Foresee what will happen and when will it happen.
• Provide recommendation on how to act upon to take advantage of predictions.

Module 05: Supervised & unsupervised machine learning algorithms.
• Classification and regression supervised learning problems.
• Clustering and association unsupervised learning problems.
• Example algorithms used for supervised and unsupervised problems.
• Understanding semi-supervised learning.

Module 06: Analyze and solve problems.
• Analytical and creative thinking in problem solving.
• Systematic process of problem solving.
• Define the issue & focus on the “drivers” behind issues.
• Creative problem solving technique.
• Pareto Analysis in deciding what critical problems to address first.

Module 07: Analyse and solve problems from different industries such as manufacturing, service, retail, software, banking and finance, sports, pharmaceutical, aerospace, etc. 
• Advancement in technology and changing needs of the traditional business department. 
• Predictive insights: Combining internal and external business information. 
• News analysis (web scraping) and sentiment analysis.

Module 08: Big data analytics tools, application and best practices.
• Steps to creating effective analytics program.
• Managing data scattered silos across various units.
• Customer and product data.
• Data governance and IT governance.



STEVE REMINGTON has more than 20 years’ experience in data science, business intelligence, business analytics, operational reporting, data warehousing and other decision support projects. His background includes seven years as an adjunct academic researcher and lecturer at Melbourne Business School, Monash University and Latrobe University.

Steve mixes academia with the delivery of data science, data warehousing and business intelligence services to healthcare, transport and logistics, financial services, insurance, commercial broadcasting, utilities, state government, police and courts, agricultural management, professional services, manufacturing and tertiary education. This ensures that he stays abreast of new thinking in academia and brings that into the commercial world, developing best practices for both academia and industry. 

Steve started his business training career in the mid-1990s where he designed and delivered quality assurance and total quality management workshops in the financial services sector. Steve realised that organisations needed to improve their use of management information from simple monitoring of business processes using reports and dashboards to using data to drive business decisions. 

Steve has devoted the next 20 years of his career to educating analytics professionals and business people, both in academic and workplace settings, about the knowledge, skills and tools required for effective data-driven decision-makers.

Steve holds a Master of Business Information Systems (Honours) with first-class honours and Dux of his graduating year from Monash University, and a Bachelor of Business from the University of Newcastle. In addition, he has published several peer-reviewed and award-winning conference papers and journal articles on analytics and business intelligence.


DANIEL RODIC has been involved with ICT from 1986. His experience in the ICT field is considerable. He has lectured various ICT related courses, has developed many systems, undertaken numerous technology investigations, user requirements, architectural and technical specifications, as well as developed strategic technology roadmaps.

He has developed and implemented ICT projects in government, security, eCommerce, financial services and leisure industries. He has also founded an ICT company that was sold in 2001 to a top South African technology company. He was a member of the executive board and was tasked with new business development.

Seeking more challenge, he started and was running a specialized consulting company and has provided consulting services to a number of companies, focusing on the delivery of security solutions to mainly public sector entities. In 2014 he took a permanent position as a CTO of an international company that has deployed ICT systems designed by him in India and South Africa. In this position, he was dealing with partners such as MasterCard, Amazon, Google, iTunes, UBER and many more top global companies and brands.

In terms of a formal education, he obtained his BSc (Mathematics and Computer Science) degree in 1995, followed by BSc (Hons) Computer Science in 1997 both at the University of South Africa. He obtained his Master’s degree with distinction from the University of Pretoria in 2000 and then proceeded to complete his PhD degree, with distinction, from the same institution in 2005. His major field of study is the field of artificial intelligence, data mining, autonomous agents and robotics. He has presented papers at numerous conferences and journals both locally and internationally.

Daniel has completed many additional training courses and among others, he is a Certified Information Systems Security Professional (CISSP) since 2001, certified The Open Group Architecture Framework (TOGAF) practitioner, Microsoft Certified Professional (MCP) and IEEE senior member (IEEE SM). 

His other qualifications include full gliding instructor rating, private pilot’s licence, sailing day skipper, Reiki healer diploma and advanced scuba diving license.


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