Are you Making Good Use of your Data?




Every business has lots of data…the question that needs to be answered is, “Are you making good use of your organisation’s data?

The best way to make good use of your organisation’s data is to use it to solve the business problems of today. When there is a business problem, brainstorm and understand what could be causing this problem…and then identify the relevant business data that can help provide insights & solve the problem.

Extract the relevant data a, perform Data Visualization to better understand when the problem is occurring, which location, for which products, which customers are being affected, etc and then measure and compare the insights , using appropriate analytical techniques such as Descriptive Statistics, Hypothesis Testing, Correlation Analysis, Regression Analysis, etc to help solve the business problem.

Evaluate the analytical results and validate that the solution is accurate and meaningful. Then take action, this is the most important step…TAKE ACTION and monitor and manage the results of your action. The last and final step is to demonstrate and communicate the analytical value to your organization. Whether it be a Reduction in Costs, Increase in Revenue, or Reduction in Time, these are essential communication messages for the Business Operations and Strategy Team to understand how Business Analytics can help solve business problems using a data driven strategy.

If you would like to learn more or are interested in projects like the one described above, contact us at Business Data Analytics Solutions Pty Ltd at






What makes a Good Analyst?



To be a great analyst you need to be able to think clearly and ensure that you understand the problem at hand well. A great analyst will ask many intelligent questions with regards to the problem and would be able to determine through speaking with a domain expert, which variables will be relevant for solving the problem. A great analyst is a very detailed person who would ask lots of questions and ensure that key information has been considered and collected.

In addition, a great analyst would select the right visual display to demonstrate the results of his/her analysis. Good communication skills are also a key trait as often the statistical results are too complex for the client to understand. So, the analyst needs to present the results in an easy to understand manner. Analysts who present the results in a story like manner with great visuals and good use of technology will be well liked for their presentation skills.

Further, accuracy is very important in the analytical world. So a great analyst will build predictive models as part of their solutions, but will always compare and evaluate the accuracy of their model with a few other models and also ensure that the accuracy is of a reasonable and acceptable level. Further, a great analyst will test their model results by validating the model against unseen data and against the actual data when it becomes available.

In a nut shell, a great analyst is passionate about solving challenging problems and works hard to ensure that the analytical solution while being highly accurate is at the same time making business sense and offers business value for the client. A great analyst will always exceed expectations and deliver more insightful information than the client expects and is paying for.

If you have any questions, please do not hesitate to ask me. You are also most welcome to share your thoughts here, on what makes a good analyst. If you would like to learn more about ‘Analytics’ or how to start ‘Analytics’ in your company, learn more at or contact me.

My First Job in Analytics – A Walk Down Memory Lane….



My first industry job, I worked as a Biostatistician at the National Health & Medical Research Council at the Clinical Trials Centre in Australia. The work was very challenging as I was working with real data, and even more stressful, I was working with patient data.

As a Biostatistician, I had to work closely with the oncologists, surgeons and doctors, and determine the Statistical Analysis Plan for the Clinical Trial. Will the Trial be a Double Blinded or Triple Arm Study, Will a Placebo be used, How many patients do we need on the trial, What are the inclusion and exclusion criteria, When will we stop the trial, etc. All these questions were answered using rigorous statistical techniques based on clinical knowledge and historical study results.

I would often share my solution with my manager, Val Gebski, who too often challenged my solution by throwing a lot of “what if” scenarios and getting me to think on several possible outcomes. We often spoke and debated for hours, sometimes the whole day, about different approaches that could be taken.

I remember some nights, in the middle of my sleep, I would get up to write out a method to use the next day to justify my solution. And, first thing in the morning I would run my method on some data and be very excited when the results agree with the typical method and in this way validates our new innovative approach.

I have many examples that I can share but I would say that problem solving is what I enjoy as it challenges me to think of new methods on how to approach a given problem.

I strongly believe and have seen that statistical/analytical techniques are powerful in producing repeatable and consistent results that are accurate when validated and, in a nut shell, Analytics is all about understanding a problem, understanding the relevant data that can help you solve the problem, asking lots of questions and then applying statistical/analytical techniques to make comparisons, predictions and finally optimizing your solution.

How Did I Start My Career in Analytics?



People often ask me, “How did I start my career in Analytics?”. It was actually, quite by accident. In my first year at university I wanted to do the key science subjects such as Mathematics, Physics, & Chemistry but I needed a fourth subject and based on my time table options, Statistics was the only science course available for me. I had no idea what Statistics was about but I fully believed in myself that I could do it, even though there were many stories that Statistics is not easy to pass and the pass rate was only 40%.

I quite enjoyed the Statistics lectures and my professor, Prof Calitz told interesting stories and often challenged us with tricky problem solving questions. I worked hard at solving these problems and often went to my professor to share my solution and ask questions.

Prof Calitz never gave me a straight answer, he always rather probed me with one question after the next and sent me back to complete my solution. At that time, I thought Prof Calitz was a hard man and not very helpful but looking back, I can say that it is through him that I now have a good knack of asking lots of questions while problem solving. And you know what, during my university studies I had to work hard at my other courses such as Mathematics but didn’t really open a book for Statistics and still passed it with a Distinction!

I am today, very passionate about Analytics and enjoy working through business problems, which are quite challenging and more complex.I am very thankful to Prof Calitz for his good teaching approach!

5 Tips for Building a Powerful Dynamic Customer Analytics System


  1. Ask the right questions to achieve your business objective:Begin with the end in mind when asking questions, gain clarity and focus on what you need to know to achieve your objectives. If our goal is to increase our product sales to achieve a financial return of 80% say, To gain clarity we need to ask ourselves the typical 5 questions: Who?, What?, Where, How? Why. For example, who is buying our products, what products are they buying from us, where are they buying (which geography)? How are they buying our products (which channels are they using)? Why did they buy our products?Brainstorm with “what if” scenarios. Understand what the best case could be in terms of product sales, what could be the most likely case and what could be the worst case.

    Companies across industries are trying to understand and connect to the consumer at a more personalized level.There has been a strong shift from the business-to-business (B2B) model to the business-to-consumer (B2C) model.

2. Track the Metrics crucial for your organisation – Use industry KPIs

A good metric/KPI is:

  • A number that drives change you are looking for
  • A good metric is comparative
  • A good metric is understandable
  • A good metric is a ratio or rate
  • Quantitative
  • Actionable
  • Leading Metrics
  • Causal Metric

Below are some examples of good KPIs:

  1. Sales Growth 
  2. Leads 
  3. Customer Lifetime Value (CLV) 
  4. Cost of Customer Acquisition (COCA) 
  5. Website Traffic to Website Lead Ratio


3.Count People and number of unique Visitors instead of:

  • Number of Hits
  • Number of Page Views
  • Number of Visits.
  • Number of Downloads


4. Ensure that your Dynamic System is Data Driven:

To develop a Dynamic Data System, you need the right ‘People’, Processes’ and ‘Technology’.

The key people for a dynamic system is firstly, a Domain Knowledge person – understand the business and data available for answering the business questions,

Secondly, you need a Statistician who understands the statistical techniques that need to be applied to the data to discover insights and patterns that help to solve the business problems. The statistician is also able to provide data visualisation fast using drag and drop type tools for visualisation (Tableau, QlikView, SAS or R) to help the business user to make valuable decisions.

The third person you need is an Information Technology Specialist who ensures that the data definitions are consistent and that all the data is in one place and easy to retrieve for analysis in real-time thereby providing and effective enterprise dynamic system.

For Processes, we need to ensure that we have the right processes in place and that they are continuously improved, which means, we need to track process time and ensure that they are reasonable or even aim to shorten process times.We need to track customer complaints to better understand which processes require improvements and remove the processes that are not necessary.

5. To get the Dynamic System Right – Ask Lots of Questions

Starting with a clear objective is essential and then we still need to ask lots of questions once we have identified the objective. Question such as,  ‘What do you want to Achieve?’, ‘New Customers?’ or ‘Increased Customer Loyalty’ or ‘Increased Revenue?’. Then for example, how will you get the increased revenue? Will you run a promotion? How much increased sales do you expect from the promotion? Which customers are likely to buy from that promotion? When will these customers buy? How will they buy and which stores will they buy from? All these questions can be answered using statistical modelling and classification techniques that can be linked from one to another and in real-time the promotional offer is made to the right customer at the right time with the right product.




Dynamic Analytics: What is Dynamic Analytics?

What is Dynamic Analytics? To answer this question, let’s take one step back and first ask ourselves, what is Analytics?

Analytics in a nutshell is about solving a business problem through asking yourself lots of questions that will allow you to better understand the business problem at hand.

Once the business problem is understood, it is for the analyst to work with the domain expert in gathering the relevant data and then to summarise the data numerically and visually to better understand the data at the aggregate level. Further, many hypotheses will be tested through comparing different groups and thereby gaining insights as to where statistical differences exist between various groups, customers and products.

Some correlation analysis tests between different variables will be made to understand the statistically significant relationships between the many business variables.In most cases, predictive analytics will be performed to allow businesses to plan their resources and strategy based on the predictions of whats likely to happen in the future. And the final step will be for the business to take the predictions made and to look at their resources to determine what is the best that the business can do to enable the prediction to take place.

Dynamic analytics is learning what customers want faster. Advances in Technology allow businesses to learn what customers want faster. Businesses also need to be aware that not all customers are the same. Customers differ on their demographics, their lifestyles, and their buying behaviour. Different groups of customers are interested in different products and services. Businesses therefore need to gather lots of data about their customers in almost real time to better understand when customers need, want and buy their products and services.

It is only with analysing customer data, can businesses better understand what customers buy, how often they buy, how much they buy, how they buy, what they buy together and when they buy. By using customer’s click stream on the web businesses can learn faster, almost in real time what customers want.

To learn what customers want faster, businesses need a social strategy, local strategy and a mobile strategy. Why do I say this? Well in today’s time, everyone is almost always on their mobile, so businesses need to analyse customer’s mobile usage and understand when are customers using their apps, how are customers using their apps, etc.

Research has also shown that customer’s research the products and services they are interested in, on their mobile while travelling on the bus, train, car and then share their knowledge with their friends, family, or colleagues through social media. Therefore businesses should have technology designed to listen to their customers when they interact socially and will then learn very quickly what customers want.

Lastly, research has also shown that most customers use their mobile to find products and services close by (local). Therefore, businesses should make sure that all their details, location, open and closing hours, information about their company, products and services are easily found when customer’s search for the nearest….restaurant, bank, petrol station, etc

Dynamic Analytics is ensuring that the business provides their customers with the right product at the right time, at the right place, with the right price & channel. Dynamic Analytics allows businesses to delight their customers by getting the next best offer right, that is , “offering your customer your product” before your customer even knows they need it!

Singapore – An Analytics Hub?

Singapore ranks amongst the most advanced & competitive IT services markets with high levels of technology adoption. Adoption of newer technologies ranked as very important or important among 73% of Singaporean respondents. More than 95% were looking to invest in newer technologies in the next 12 to 24 months. More than 75% of respondents are looking to have customer intelligence, predictive analytics & sentiment analysis in place in the next 12-24 months.

More than three million people in Singapore use Facebook and more than 900 000 use Twitter. This gives businesses a great opportunity to connect with & better understand their consumer base at a low cost.

Business Analytics has been identified as an important growth sector for Singapore.There are currently more than 100 apps developed using government data by the private sector & community groups. These range from car park availability to clean public toilets. Singapore moved forward with open data initiatives (, a one stop data portal with more than 8600 data sets from more than 60 public agencies. Singapore provides data sets for crowd source analytics solutions, resulting in rapid prototyping, piloting & developing proof of concept.

Is Singapore an Analytic Hub? Most certainly. Analytics goes hand in hand with high technology. With high technology, data is everywhere and needs to be analysed  in almost real time…many apps are being developed each day. Many hand held technologies that provide business insights in real time are becoming the order of the day.

Because of the reams of high speed data being generated from technology apps, decisions can only be made through measuring and analysing this data. It is absolutley essential to use this data in business decisions and for business growth.

There is a shortage of business analytics talent, to narrow the gap, software vendors are collaborating with academic institutions to develop curriculum that reflects the mix of technical & problem solving skills that is necessary to prepare students for business analytics careers. If you need more information on business analytics and the topics covered in the different business courses, do not hesitate to contact me.