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Malaysia Airports develops data insights to optimize customer satisfaction

Airport operator company, Malaysia Airports, with interests in Turkey, India, and South Asia, is working with Fusionex on a proof-of-concept: to develop a Business Intelligence platform to further enhance Malaysia Airports’ retailer management system within KLIA and provide value-added services for travelers.


Malaysia Airports is an airport management company with a portfolio of almost 40 airports in and around South Asia and Turkey. Apart from the aeronautical business, the client also manages other portfolios including duty-free & retail operations, hotels, free commercial zones, commercial space leasing, and management of parking facilities. It also runs global training centers regarding airport management, airport fire & rescue services, and aviation security.

The Challenge

Malaysia Airports sought to enhance its customer data collection methods and determined to increase the accuracy of gathered information on the performance of its retail units within KLIA. This involves an approximately 400,000 square foot containing retail outlets at various locations including the arrival and departure halls as well as check-in counter areas. These outlets/locations see a fluctuating amount of shoppers at different times and days of the week.

Malaysia Airports aims to ramp up its data collection frequency near the shops area. A higher degree of prompt data collection would serve to accurately determine when promotional activities should be held, or if they were held – what their projected results would be. Accuracy of information garnered from this area is important to determine the customers’ hotspots and their spending trends.

A precise method to track spending trends at the terminal would allow Malaysia Airports to identify local shoppers and international travelers apart. This would also result in a more informed decision being made with regards to what the retail composition should be – whether the terminal should host more shops selling food, garments, souvenirs, or other goods base on the shoppers spending pattern and behaviour.

The Solution

A combination of the Fusionex Business Intelligence platform together with Internet of Things (IoT) devices was utilised to collect and manage the terminal’s retail data. Sensors were installed at the main terminal building’s arrival and departure halls and the contact piers for international and domestic flights.

Tracking the traveler jouney via the Fusionex BI Dashboard

Sensors are also placed in and around the airport’s retail sections to determine hotspots where the most retail activity occurred. The sensors are functioning independently without requiring wiring or lengthy installation methods. Among the sensors used were those with Bluetooth capabilities and Wi-Fi detection capabilities installed in collaboration with solution provider Tapway.

A mobile application was developed to track the basic demographic background of customers including details such as gender and nationality. A campaign was conducted to encourage travelers to sign up for the mobile app. The app could then use the demographic information to craft personalized marketing messages and promotions for the customers.

Using a wide set of connectors, various type of data set and system can be integrated into single platform with speed and ease. The dashboard reporting is then rendered automatic as well, giving the client the ability to view revenues in near real time. These endeavors were supported and co-funded by the Malaysia Digital Economy Corporation (MDEC).

The Benefits

The ability to receive performance data from their existing tenants quickly, together with traveler information from installed sensors, allowed Malaysia Airports to make more accurate decisions:

Understand traveler habits and shopper behavior, preferences. Malaysia Airports will be able to determine travelers’ flow and identify trends of unique shoppers via the installed sensors. Other than that, demographic data such as gender, date of birth and nationality will also be gathered to help Malaysia Airports understands which retail outlets appeal the most to which groups of people. This provides insights into what composition of retail outlets would be optimum in the airport – whether or not certain shops fit, and whether they fit better in domestic or international halls.

Ability to perform more accurate marketing efforts. Passengers’ traffic data gathered, which could be drilled down to daily and even hourly, could inform Malaysia Airports when would be the right time to hold promotional and marketing events within the terminal. Connectivity via travelers’ mobile devices provides a possibility for future marketing undertakings such as customer-specific push notifications. When walking near a particular shop which the traveler might be interested in, the platform pushes a notification to their phones for a promotion on items or services available.


By utilizing IoT devices and gathering useful data on a centralized system, Malaysia Airports is able to get a good overall view of its airport’s retail sector. Being provided with user-friendly BI dashboards also enable management-level personnel to get quick looks at the information to make timely and accurate data-driven decisions.

Industry: Aviation / Retail

Big Data Solutions Provider: Fusionex is an established multi-award winning IT software group that specializes in Analytics and Big Data. Their business is to help clients manage, make sense of and derive useful insights and information from the vast amounts of structured and unstructured data at their disposal. Fusionex is focused on bridging the gap between business and technology, and in doing so, providing an exceptional and positive experience to customers of various markets.

Wrangle Malaysia Interviews: How Data Science Impacts Us All

Wrangle Malaysia was held on 9 Dec 2016, and was co-organized by MDEC, Big Data Malaysia and Cloudera. It was a single-day, single-track event about Data Science across multiple data-rich industries where 10 international data scientists flew down to Malaysia to share their toughest problems, and the solutions they found for them.

We managed to interview a few attendees – from a variety of ICT and non-ICT backgrounds – on what their thoughts were on a plethora of topics such as how big data and data science applied to their organizations and individuals, the types of skill sets required to be a data scientists and how attending Wrangle Malaysia would help them think about their big data strategies and projects.

Check out these 6 awesome video interviews below.

There seems to be a disconnect between arts and science, but there is actually a very important connection. Data looks so dry when it is just numbers, but when it interacts with artists, the implication of data can be conveyed, and the way data is expressed will be different. Artists can help convey the impact of data to the public. So I think the interaction between arts and science need to be brought out. ~ Wan Zaleha Radzi – Managing Director, Asiapromote Ventures Sdn Bhd

If I have to summarize the skills that a data scientist needs – one would be the stats and math skills for developing complex models to solve problems, the other would be programming skills to convert unstructured data to structured data, and the third would be the business skills… people having business skills is difficult to find. These are the people who can bridge the data gathering and data modeling with the the right business problem. The lack of such [business-oriented] people is the reason why many do not see the usefulness of data science. ~ Fajar Jaman – Head, Data Science Indonesia

[Advice on how fresh grads can get themselves involved with big data analytics] I think technology is the current wave right now, and if you are still thinking about it, I would say just jump in and dive into it. And when you start, that’s when you start developing your knowledge and experience, and that’s where people will start hunting you for jobs. Go participate in competitions. Even if you just sit in, you would be inspired by all the great ideas. Joining online courses; you will learn something that will help in your journey as well. ~ Dr. Yap Poh Hean – Technology Consulting Manager, Accenture

In data science, it is very important that you are able to put your point across to the other person. Being able to present your idea and convey your message is something that has to be ingrained into the data scientist. ~ Ashish Dutt – PhD candidate, Faculty of Computer Science and Information Technology, University of Malaya

Over the past 2 years, the demand for data scientists has increased. The events that MDEC organized have created a lot of awareness. And now it’s up to the industry to pick it up and hire data scientists and perform more analytics. Sometimes a data scientist can be called a unicorn because it can produce magic. But a data scientist is very dependent on the domain he or she is in. In different industries, a data scientist may be doing different things… domain knowledge or expertise of the data scientist is very important. ~ Dr. Poo Kuan Hoong – Senior Manager Data Science, Nielsen

In oil and gas, there are very large volumes of siesmic and petrophysics data, but volume alone doesn’t really make the cut for big data. Big data is about the 4V’s… once you bring in streaming and realtime data, you start to add the additional V’s and the problem is compounded. It’s not only about static data for throughput and process. It’s really about making decisions on the fly with data that is hitting you very quickly. There are lots of use cases [in O&G] and pockets of data that need to be studied together in order to come to better business decisions and that’s where the whole data science play comes in. ~ Philip Lesslar – Principal Consultant, Technical Assurance – Technical Data, PETRONAS Exploration & Production

2017 Predictions for Big Data, AI and IoT

  1. Artificial intelligence will be the most exciting area of the hottest trend, and its usage will be in one-to-one conversations between buyers and marketers (in the form of chatbots and digital assistants).
  2. Hype surrounding deep learning will finally fizzle out. Deep learning will be become a reality.
  3. Those who adopt AI and machine learning early will have a huge advantage because the system would have started learning about their business sooner.
  4. Machine learning will cut across all IT functions.
  5. AI will help businesses comprehend and obtain a thorough view of their clients, vendors and partners.
  6. ETL companies will rise to help maintain accurate and high-quality data so that AI technologies can make accurate predictions.
  7. Big data projects will increasingly move to the cloud.
  8. IoT will continue to be a hype. Adoption will be sluggish.
  9. IoT will redefine how the internet works.
  10. Hackers will create the first IoT ransomware.
  11. Workers in labor-intensive jobs will communicate with AI-based supervisors.
  12. AI will extend the conventional recommendation engine to power the B2B market.
  13. Data scientists will continue to be in short supply.
  14. Chief Data Officers (CDO) will play a prominent role in enabling data access and sharing within the enterprise.
  15. Marketing teams will heavily rely on data scientists for their campaign performance.
  16. An IoT Analytics Architect will be more valuable than the data scientist.
  17. Data engineers will get poached as they become more and more prominent.

Read the full article here.

Advanced Analytics in Talent Management Strategy and Operation

The Chief Human Resource Officer of our client, a leading telco in Malaysia, overseeing over 3000 staffs is faced with a sharp rise in HR cost in recent years despite maintaining a tight control on headcount and increments. The key questions we set out to answer was why HR cost is increasing and what can be done about it. It turns out to be a case well-fitted for BIIT Analytics Framework.

Descriptive: Understand Past & Present

We firmed up the problem statement by framing staff cost as a function of key workforce dynamics (i.e. hiring, attrition, promotion and increment) and salary. Using component analysis and a fair bit of calculus, we distilled the four main drivers of cost change: cost per head, headcount, structural change and combined effect. It was then revealed that structural change has been the culprit. This is exactly why while hiring and increment were controlled tightly in the past, HR cost kept rising.

Figure 1 Breakdown of the effects of cost drivers

  • Cost per Head Effect: Change in cost if only cost per head changes at each job level while headcount and structure remain the same
  • Headcount Effect: Change in cost if only total headcount changes while structure and cost per head remains the same
  • Structural Effect: Change in cost if only structure changes while total headcount and cost per head remain the same
  • Combined Effect: Change in cost not explainable by or nonexistent without the other 3 effects

Predictive: Foresee What Is To Come

While stakeholders understand intuitively that such scenario would be disastrous if it were to continue, it is difficult to put an amount to it to compare against other priorities in the business. We studied and ran time series forecasting on the key variables to estimate potential impacts of inaction. The resulting model shows that evolving workforce dynamics will cause the workforce to be fatter at the higher end of hierarchy and thinner at the lower end. If such trend continues, staff cost will very quickly reach an unsustainable level especially when the market is expected to underperform.

Figure 2 Headcount by job level graphs showing evolution into top-heavy workforce structure

 Prescriptive: Pinpoint the Right Course of Action

The next question on everyone’s mind is naturally what should be done to contain the cost increase. To contain the cost issues driven by structural change, one can use the four key workforce dynamics as levers. We modelled staff cost as a function of the dynamics and embedded it into strategic and tactical simulators. With simulators at both levels, the HR management team can simulate scenario with different hiring rate etc., and set policies based on the results.

Figure 3 Screenshots of the strategic simulator tool for adjusting key action levers and simulating potential outcomes

Figure 4 Screenshots of the tactical simulator for line managers to simulate mid-term outcomes of their actions such as promotion, transfer, hiring & etc

Figure 5 Output of the tactical simulator shows outcomes of middle management actions at all levels of organization

Proactive: Enable Action through Technology

No strategy and policy is perfect. The impact of policies should be monitored continuously so that timely adjustments can be made when needed. On that front, we proactively designed and implemented cost tracking dashboard for managers at all levels to ensure their accountability for staff cost incurred. There is also built-in capabilities to customize and periodically push reports to individual managers.

Figure 6 Screenshot of sample cost tracking dashboards at company level. Our solution automates reporting at all levels of organization

Our Solution:

Workforce Analytics – Deep-dive analysis into workforce cost

Using historical data, we perform deep dive analysis to uncover patterns through simple data visualisation techniques. The analysis uncovers general historical trends.

 Workforce Forecasting Models – Forecasting cost and headcount movements

Using historical data, our forecast models are able to predict the following key information on workforce:

  • Headcount by job levels
  • Cost projection
  • New hires, attrition, and promotions

The forecast is able to provide organizations with the correlation between headcount, structure and cost per head impact.

Workforce Simulator – Real-time simulation of organization re-structuring exercises

  • Ability to perform real-time simulation on existing organisation structure (promotion, new hires, termination, relocation, headcount movements etc.)
  • Ability to generate reports on directional impact of the simulated changes with high-level commentary
  • Ability to load and compare multiple versions of the simulations
  • Ability to add new department structures, and new headcount on-the-fly for simulation
  • Ability to export outputs of raw data as inputs to HR systems
  • Encryption of raw data to ensure security

Workforce Cost Tracker – Management Dashboards to monitor cost

  • Ability to monitor and capture historical trends of monthly HR cost and comparison against targets
  • Ability to monitor monthly run-rates of staff cost to give managers a true picture of structural impact
  • Ability to export the dashboards at multiple levels of granularity (e.g. Company level, Division level, Department level etc.)

About us

We are a company that truly believes that insights are strategic assets which; when leveraged properly – have the potential to transform organizations.

A couple of folks who were passionate about Data & Analytics decided to help organization realize the value of analytics across their enterprise. The company was birthed with a simple mission of providing and facilitating businesses (of any shape and size) with invaluable insights through infallible information enabled by technology.

We view technology as the means to achieve the desired business outcomes, and not the end in itself. As such, we place a large amount of emphasis on helping our Clients derive business value from all our engagements.

BIIT Consulting Sdn Bhd


Diamonds are not forever according to data science

At least not for pricey wedding rings! Analytics involving 3,000 couples – married or otherwise – presented a correlation between the price of an engagement ring and the length of marriage. If you are spending a large part of your salary on your other half’s diamond ring, be prepared to be saddened. If the ring costs over $20k, you are 3.5 times more likely split up than you spending up to $10k for the ring. But then again, in data science, correlation is not always causation.

Full story by Big Think:

I don’t think anyone’s ever argued that the breadth of a hetereosexual couple’s love for each other can be measured by the size of the rock on her finger. At the same time, I wouldn’t have suspected that gaudier rings correlate to higher divorce rates. Yet that’s exactly what researchers at Emory University found in a comprehensive study released last month (though only picking up viral steam during the past couple days). Simply put,if you’re dropping major stacks on your engagement ring, you may be setting yourself up for a whole lot of disappointment.

The two Economics professors behind the study — Andrew M. Francis and Hugo M. Mialon — analyzed data from 3,000 married or once-married heterosexual couples, so long as the once-married did not include the widowed. The researchers sought to “evaluate the association between wedding spending and marriage duration.” As it turns out, length of marriage among subjects was indirectly related to the money spent on their weddings.

Kelly Faircloth of Jezebel sums up the stats really well. Women whose engagement rings cost over $20,000 are 3.5 more likely to get divorced than those in the $5,000 to $10,000 range. Men who spent $2,000 to $4,000 on their wife’s ring got divorced 1.5 time more than those who dropped between $500 and $2,000.

It’s important to note that these relationships between marriage duration and wedding cost are much more correlation than causation. There are plenty of happily married people who have the monetary equivalent of Fort Knox on their finger. There are also others clutching $500 rings who likely wish they had the kept the receipt. But with the average cost of weddings in the United States flirting with an ungodly $30,000, it’s understandable that all this ostentation might come with a little unwanted pressure.

The study was titled ‘A Diamond is Forever’ and Other Fairy Tales: The Relationship between Wedding Expenses and Marriage Duration. It successfully found the correlation between marriage success and marriage prices. Though they stated that the price tag and the marriage duration was “not or inversely associated,” they found enough correlation that one can start naming the ceiling on wedding spending.


Hong Leong Bank Uses IBM Watson for Cognitive Banking

Hong Leong bank becomes the first bank in Malaysia to utilize IBM Watson to enable customer self-service. The cognitive technology platform will also assist the bank’s call center consultants. It will scrutinize and analyze customer profiles, reports and product info to recognize a customer’s needs and offer various financial options available to them.

IBM developed Watson as a cognitive system capable of answering questions presented in natural language. Watson is also adept in analyzing both structured (data from databases and spreadsheets) and unstructured data (images, audio and video) and present the analytics through visualization.

The full press release:



IBM today announced that Hong Leong Bank Berhad (“Hong Leong Bank”), one of the leading financial services organisations in Malaysia, will leverage IBM Watson to transform its customer engagement model and internal operations to deliver a next generation customer experience.

Hong Leong Bank is the first Malaysian bank to harness the benefits of cognitive computing through the implementation of IBM Watson, a cognitive learning system, which is geared towards further enhancing customer support for its credit card services. In the initial stage of this multi-year programme, IBM Watson cognitive technology would function as an online customer self-service advisor with 24/7 support for cardholder enquiries, as well as an internal service supporting the Bank’s call centre service advisors. IBM Watson will also help the bank’s service advisors analyse large volumes of data, including research reports, product information and customer profiles; identify connections between customers’ needs and weigh the various financial options available to the customers.

ibmSigning ceremony between Hong Leong Bank and IBM

“Customers have come to expect higher levels of integrated and timely service, with an increasing need for exceptional phone and online experience. We need to ensure that our systems are able to anticipate customer needs so that we may proactively provide solutions. The IBM Watson technology provides us this advantage by allowing us to understand the context of customer needs and preferences, which will reduce call waiting time and ensure greater consistency and accuracy of information” said Edward Pinto, Chief Operating Officer for Customer Experience & Analytics, Hong Leong Bank. He added that the initiative forms an important part of Hong Leong Bank’s overall digitisation blueprint.

Through IBM Watson, Hong Leong Bank customers will have round-the-clock access via online live chat to information about general credit card terms, policies, and procedures, as well as to find the most suitable card to match their lifestyle needs.

IBM Malaysia Managing Director, Chong Chye Neo said “Cognitive banking builds upon the personalisation and digitisation trends in banking over the last decade. The future bank is knowledge driven and convenes operations and offerings around its customers and their economic choices. This strategic partnership with Hong Leong Bank Berhad allows them to anticipate and respond to the changing market; and transform the relationship between the bank and its customers.”

Machine Learning Yearning: A new and free e-book on AI technical strategy

Andrew Ng has released the first 12 chapters of his new machine learning book called Machine Learning Yearning: Technical Strategy for AI Engineers, In the Era of Deep Learning.

For AI and data science enthusiasts, Andrew Ng is a household name. He is Baidu’s Chief Scientist, associate professor in Stanford University’s Computer Science and EE departments. Andrew also co-founded the wildly popular online education platform, Coursera. He specializes in machine learning and deep learning, with over 100 papers published in machine learning, robotics and related fields.


Machine Learning Yearning helps data scientists understand how to set technical directions for a machine learning project. Your teammates may not understand why you’re recommending a particular direction. Perhaps you want your team to define a single-number evaluation metric, but they aren’t convinced. How do you persuade them? Andrew has made each chapter short so that so that you can print them out and get your teammates to read the 1-2 pages you need them to know. A few changes in prioritization can have a huge effect on your team’s productivity. By helping your team with a few such changes, he hopes that you can become the superhero of your machine learning / AI team.

Download the e-book
To ask questions, discuss the content, or give feedback, please post on Reddit at:
You can also tweet at Andrew at


Wrangle Malaysia 2016

Wrangle Malaysia 2016. A one-day, single-track event about Data Science that cuts across multiple data-rich industries. Come listen to 10 international and renowned data scientists share their toughest problems, and the solutions they found for them. Wrangle Malaysia 2016 is organized by Data Scientists for Data Scientists!


For more info and to register your interest in the 100% subsidized event:

Date: 9 December 2016 (Friday)
Time: 8.00 am to 6.00 pm
Location: Kuala Lumpur Hilton

We have confirmed international speakers from Cloudera (US), Singapore GovTech (Singapore), Intec (Japan), Entopix (New Zealand), eBay (China), CreditX (China) and more to be announced!


Multimedia University (MMU) partners with Teradata to develop data science professionals

MMU has teamed up with Teradata on data science education and R&D. This partnership also allows the university to access the Teradata University Network (TUN), an online platform consisting of teaching and learning tools used by over 45,000 students worldwide. MMU has set up a Data Science Institute (DSI) and will offer a Data Science Specialization as part of its Computer Science degree course.

Multimedia University, Malaysia (MMU) has made a new move recently to help build the next generation of data science professionals and experts in Malaysia.

MMU announced a new industry partnership with big data analytics (BDA) provider Teradata to collaborate on education and research, said the president of MMU, Professor Datuk Dr. Ahmad Rafi Mohamed Eshaq during the signing of the agreement in Cyberjaya.

Dr Ahmad said MMU’s Faculty of Computing and Informatics will offer a Data Science Specialization as part of its Bachelor of Computer Science degree.

He added that this followed the formalisation of a Data Science Institute (DSI) at MMU last month.

“This memorandum of understanding (MoU) is a timely joint initiative for the DSI and Teradata,” said Dr Ahmad, adding that MMU now has access to the Teradata University Network (TUN), a web-based portal that provides complementary teaching and learning tools currently used by more than 45,000 students around the world.


Photo – Prof. Dr. Ahmad Rafi Mohamed Eshaq (2nd from left) exchanges MOU documents with Saqib Sabah, Country Manager of Teradata Malaysia (2nd from right), witnessed by Prof. Dr. Ho Chin Kuan, Dean of Faculty of Computing and Informatics MMU (left) and Syamsul Badrin, Teradata’s Account Director (right).

“Teradata University Network benefits from input from faculty members globally to create a platform for information and knowledge sharing. Furthermore, University staff as well as students, will have access to Teradata’s rich suite of software,” he said.

Exposure to industry vital

Dato’ Yasmin Mahmood, chief executive officer of national ICT agency Malaysia Digital Economy Corporation (MDEC) welcomed the move.

“The Big Data Analytics ecosystem requires an ever-growing consistent and sustainable supply of skills expertise,” she said. “Our goal is to continue to train, develop and nurture data science professionals in collaboration with Institutions of Higher Learning (IHLs) as well as the private sector. I am confident that with this strategic collaboration, more young Malaysians will adopt the big data analytics mind-set as it becomes a game changer and key for driving Malaysia’s digital transformation agenda.”

She said that the partnership between MMU and Teradata will help to speed up the increasingly important training of Malaysians in BDA.

“This MoU facilitates greater input from Teradata, which has committed to provide experts to support exposure to our students and staff alike to its services and solutions,” added MMU’s Dr Ahmad.  “This exposure to the industry is absolutely vital as, it will help to seed ideas and prepare future generations to understand and conform to the needs of the industry.”

Saqib Sabah, country manager and director of Teradata Malaysia, said the company has a history of cooperation with the government’s digital drive, various projects and talent development.  “This [latest move] is another important strategic partnership for Teradata as we will now be able to work with MMU to jointly nurture and develop future data scientists, thereby addressing the huge skills gap in the country in this area of expertise.”

How a Malaysian bank used data science to increase its credit card spend

Client Profile: The bank is one of the largest financial institutions in Malaysia with retail branches in numerous Southeast Asia countries.

Challenge: A substantial segment of the bank’s credit card holders with high spending potential is not utilizing its credit card as their primary card.

Solution: Cardholders with spending potential were segmented based on various demographic attributes such as gender, product holdings and amount spend.

Collaborative filtering – a technique to predict user interest based on the behavior of other users – was applied to detect spending patterns. For example, the bank wanted to know how frequently merchant offerings appeared against customers with specific product holdings. And how often merchant offerings showed up against customers of a particular demographic.


Customer segmentation and collaborative filtering to forecast consumer propensity scores

Each potential customer was scored on their spending propensity for every merchant category like dining, petrol, groceries and airlines.

The bank then matched specific merchant offers to individual customers based on their inclination to spend in that merchant group.

Customer data, card transactions and the effectiveness of previous merchant offers were used to identify potential cardholders to drive higher usage of the bank’s credit cards.


Every customer is scored against every merchant category on their propensity to spend

Business Benefits: With personalized marketing campaigns derived from data analytics, the bank saw an increase in its credit card spend and utilization, resulting in a substantial increase in interchange and interest income.

Industry: Banking

Big Data Solutions Provider: Teradata Malaysia