Author: carolhargreaves

AI: Good, Bad or Scary?


Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include speech recognition and problem solving through ‘learning’.

Machine learning is a core part of AI. There are two types of machine learning, supervised machine learning (numerical regressions) and unsupervised machine learning (classification). Classification determines the category an object belongs to and regression deals with discovering functions and  generates suitable outputs from the respective input

Unsupervised machine learning  is learning without any kind of supervision, insights and patterns in the data are identified in streams of inputs using statistical techniques such as clustering and factor analysis. Supervised machine learning is learning with adequate supervision, using historical data records as the target variable of interest.

AI is progressing rapidly, from self-driving cars to SIRI. While AI is used mainly for helping businesses to become more efficient and more digital, there is evidence that AI can also be harmful. As we hear each day, more and more companies data are being hacked by people who are misusing AI.

There is now a growing interest in AI safety and AI ethics as:

  1. AI has the potential to become more intelligent than any human
  2. AI algorithms can compute much faster than the human brain, AI algorithms can do multi-task learning and multi-modal learning. AI has becoming more and more, part of our daily lives. For example, we have apps that remind us we need to go for our run; While running, the app tells us to slow down, as we are running too fast; or tells us to run faster, as we are running a bit too slow, etc. We are becoming more and more reliant on AI algorithms to tell us what to do.
  3. Another example, the other day, I was looking for a particular data science book to read, and while searching for the book, two books were recommended to me. I actually, bought, not only the book I set out to buy, but also bought the two recommended books. And guess what, the two recommended books were much more interesting than the book I set out to buy. My message to you, is that the AI algorithms are getting so intelligent that their recommendations are very accurate and we are trusting the  algorithms more and more, which is a good thing. But, what happens when the AI algorithm starts learning new information much faster than you expect and starts making recommendations and doing new things that are not in your control or harmful to you or your business.
  4. AI algorithms on the not so good side, is that, AI algorithms are sometimes being developed by analysts who are not always properly trained in the field. The analyst  learns how to code using a book or online course and then obtains a job as a “Data Scientists” but really, they do not quite understand whether the models they build are good or not good, whether the models when run on their company data, produces an optimal result or not. As long as familiar output is seen by the coder, and the overall accuracy of the model is 99.1%, the analyst thinks he has produced a model that the company would be happy to use. No testing is done, no validation is done, the company trusts the analyst and deploys the model into the market because the overall reported accuracy of the model was 99.1% by simply copying and pasting the code for their company tasks. It is usually later discovered (when the company is losing money) that the data was unbalanced and the actual accuracy (“Sensitivity/Recall”) was 25%.
  5. As there is a large shortage of trained “Data Scientists”, many businesses are taking the talent that they can find, usually very junior and inexperienced. It is scary that AI algorithms are being used by people who do not know or do not understand how the “black-box/neural network” works. Many analysts do not analyze the impact of the AI algorithms on the business. For AI to work well, communication on what the algorithm does, how the algorithm does it, why it does it that way, what the decision making strategies are, what is the impact to the business, how will the business operations change, have to me understood before the AI Algorithm is deployed into the market.

Yes, we are living in an exciting time, where we can obtain answers to our questions very quickly, through the use of AI. But, I think we are not asking ourselves enough questions. Questions such as:

  1. Do we have a framework, set of standards as to check whether the AI algorithm is acceptable or not?
  2. Do we have a framework, set of standard competency skills in AI that will examine and certify analysts as “Qualified Chartered Data Scientists” based on their work experience tasks and education.
  3. AI can be harmful. Who will be responsible for the harm? The analyst who wrote the software code, or the owner of the company who employed him to write the code, or the person who bought/licensed the software code? We have no idea how bad AI programs can become, and when it will be dangerous and out of control. It could occur today, next week or net year.

I am sure there are lots of AI experts discussing and working on the above questions and many more questions on the ethics of AI. Our lives are no longer private. AI is clearly part of our daily lives. We search for something online and within a few seconds we obtain email advertisements or social media advertisements based on our search. In some cases our personal data is sold for purposes other than the purpose it was provided for. How do we control the use and misuse of our own data?

I can go on and on…but really, it is time for us to take charge. We are living in a world that is getting disruptive with new processes, new ways of doing things, quickly and efficiently. But, how efficient are we? Doing things quickly may be good, but how accurate or how profitable are your AI algorithms?

Most AI algorithms are probably effective, particularly in the beginning. But, extra care needs to be taken to monitor, modify and in some cases abort your AI algorithms because they are no longer as effective as the were a few months ago.

In a nutshell, AI is for a large part, is good and efficient, and aids businesses with their processes and decision-making, leading to more profitable and efficient businesses. I am more concerned about the cases where AI algorithms are either used for the wrong/bad purposes and there are not enough rules in place to guide analysts and businesses on the ethics and safety of AI.

Let me know your thoughts on AI: Good, Bad or Scary. In particular, if you know of any industry where there are good ethical/moral codes of conduct for AI, do post them to me.

Can Businesses Measure their Risks? Can these Risks be Managed?



All businesses have some level of risk. Whether it be customers who do not pay their credit or customers who stop buying from them.

Can risk be measured? Risk of course, has a level of uncertainty attached to it, and may seem difficult to measure.  Many people find it difficult to believe that Risk can be measured.

Risk can be measured. We are fortunate, in today’s times, with advanced technology and historical data, we can use data to recognize historical cases that put businesses at risk. Exploratory Data Analysis and techniques such as Machine Learning can help businesses to identify patterns in the data that are associated with Risk. Using statistical significance testing we are able to identify which factors scientifically contribute to the risk under study.

Of course, not all cases will be identified as ‘Risk’ but some cases will have a higher probability of risk than other cases. This means that a business would need to have a strategy in place to manage the different levels of risk. Some “Risks” are “critical” and need urgent response and action, while other “Risk” are important but less urgent.

In the case where your business provides credit to customers, it is important to identify which customers are highly not likely to pay their loan and the size of their loan. As some customers will have a loan size of $1000 with a probability of 90% chance of not paying back the loan. Another customer may have a loan size of $10 Million with a probability of 60% chance of not paying back the loan. Businesses often forget to look at the loan size and only focus on the probability of someone being a credit risk, when in fact, the higher loan amount is most times more critical that it be paid back than the smaller loan sizes. Businesses need to have different risk minimization strategies based on “Size of Loan” and “Probability of Credit Risk”.

When Businesses have a Strategy for Risk Minimization, they can integrate their credit data analysis results with their risk minimization strategy and automate it into a complete decision support system for their credit officers.

Fortunately, with the large volumes of data, businesses can use machine learning techniques to crunch customer transactional, demographic, social media, and lifestyle data, etc to score customers faster and smarter giving businesses the enhanced customer experience and competitive edge in decision making.

Why Aussie Stock App?

My name is Carol Hargreaves. I am developing an Aussie Stock App that focuses on rating stocks on their financial health on a scale of 1-10 and rating stocks on the likelihood of their stock price going up on a scale of 1-10. Learn more at

Here’s my story…5 years ago I didn’t know anything about technology or developers. But one thing I did know, is statistical analysis, and how to rate products on the likelihood of being sold or how to rate customers on the likelihood of being a credit risk or how to rate a transaction on the likelihood of being fraudulent.

Another, thing about me, I enjoy challenges. Selecting which stocks to buy is challenging as there is a large amount of uncertainty in the stock market. But, I have a passion for stock trading & analytics, so even though stock trading is challenging, I still enjoy it. So, too with analytics, even though problem solving is challenging, I enjoy the challenge!

So, with my statistical and analytical knowledge, I started to analyse all the stocks in the Australian stock market before buying a stock. I could see that I had something going…my stock trading strategy and selection of stocks were helping me to selection stock portfolios that outperformed the market.

The problem I had, it took a long time to analyse all the stocks in the Australian market, and in the process, I was missing out on some opportunities in the market because I was manually analysing the stock data.

As scoring customers, or scoring products using analytical techniques is my daily job, I decided, why not start scoring stocks using these same analytical techniques. So, I build a stock trading system that automated my analytical process and helped me to save time in analysing the data. My stock trading system uses sophisticated statistical techniques to identify the trends and patterns in the stock data which indicate which stocks are likely to go up in price. The Aussie Stock App also uses machine learning techniques to identify which stocks are financially healthy.
Over the past 18 months, I worked with data analysts to back-test and refine my trading strategy and, also worked with developers to build my Aussie Stock App (Minimum Viable Product (MVP)).

My goal is to help beginner stock traders to make informed decisions when selecting stocks. I believe that by analysing the stock data, we make unbiased, robust decisions about a stock, based on what the stock data tells us. My stock trading method is short term. I would say typically 0 – 3months.

If you want to learn how to trade stocks in the ASX market, you can use Aussie Stock App to help you choose good stocks and then ‘paper trade’. In other words, no actual money is required. All you do, you assume that you bought the stock and then to manage your risk of losing money in the stock market, you place the stock in a watch list, you will set your target price (maybe 50% more than the current stock price) when to sell and your stop loss price (maybe 3% less than the current stock price) when to sell. This way you will gain experience and confidence in stock trading without losing any of your money.

If you are interested in the Aussie Stock App, it is free. After 1 month, there are optional in-app payments for the Stock Financial Health Ratings (scale 1-10) and the Stock Rating for the stock price likely to go up (scale 1-10). You can learn more about Aussie Stock App at


If you have any questions or feedback, please do not hesitate to contact me on

Artificial Intelligence: Is it all good? Are there no Risks?

Artificial Intelligence

Artificial Intelligence is growing in its application and is going to grow even more….it’s going to be big! Every organisation and every industry will be applying Artificial Intelligence to help them make smart decisions, faster & cheaper.

Artificial Intelligence helps all businesses to reduce or get rid of mundane manual processes. It also improves the business operations and helps to reduce processes and costs.

There are many use cases of Artificial Intelligence across all different industries, and to-date, the use cases are extremely valuable to businesses. But is it possible that one day, the Robots or Machine Learning Algorithms can get so smart that it may be to our detriment?

Let’s get the discussion going…Do you know what is #Artificial #Intelligence? Is your organisation or industry applying Artificial Intelligence to different aspects of its business? What do you find interesting about Artificial Intelligence? Is Artificial Intelligence working for your company? Do you think Artificial Intelligence can get the human race in trouble one day? What’s your thoughts on Artificial Intelligence?

#AI @DataAnalytic @DataAnalyticx

Should you be Learning more about Machine Learning Algorithms?


More and more businesses are switching from reacting to situations to anticipating them. How do they do this?

In order to anticipate or predict a situation, an organization must have a business question in mind. Questions such as:

Is this a fraudulent transaction or not?

Will the stock price in the next week go up or not?

Is this particular person a credit risk or not?

Will this customer buy item ‘A’ or not?

How many units will be sold next week?

What will the stock price be tomorrow? etc

The best way to make business decisions is to analyse the relevant historical data and to identify the patterns in the data. To identify the relevant data, it is important that the analyst speaks with the domain experts to help them identify the relevant data. Further, it is not only the analysis of the historical data (being reactive always) that can help answer the above business questions. It is also important that businesses use predictive data (being proactive and sometimes preventing negative activity) to help answer the above questions.

Machine Learning is a method for data analysis, in particular, Machine Learning Algorithms focus on using the relevant data inputs to make Predictions for the business or Classify the business products, services or customers.

Machine Learning is an algorithm that explores the data for patterns and identifies the key data (factors) that will help the business to predict/classify its target outcome of interest (like the above business questions). Machine Learning uses statistical techniques (such as the logistic regression, neural networks, decision tress, etc) and computer programming languages (such as Python, R, SAS, Matlab, etc) to automate the predictive/classification models using algorithms that iteratively learn from the data and find the hidden insights that humans would not usually have known about. These hidden insights are like ‘gold for the business’ as they usually improve the business predictions/classifications without being explicitly programmed where to look. The Machine Learning Algorithms further improve when they are regularly updated with new data and knowledge.

For Machine Learning Algorithms to be effective, it is important for businesses to be able to capture the relevant, good quality data in real time or almost real time and to allow the right people in the business to apply the predictions/classifications timely and thereby gain advantage over their competitors.

Are you interested in learning more about machine learning algorithms and how your business can improve its sales/predictions and become ore efficient? Contact me at

Business Data Analytics Solutions



Every company has lots of customer data, product data, business services data, social media data, website data, financial data, etc. Data is extremely valuable today as it allows businesses to make decisions smarter and faster. But, are businesses using their data wisely? Can businesses answer questions they have fast?

The Application of Statistical Techniques to relevant data is the core method for understanding business data and solutions for business challenges. Do organisations have statistcially trained talent? How are decisions being made in organizations today? Do organizations have an in-house Analytics Team to help them make decisions faster and smarter?

Business Data Analytics Solutions Pty Ltd has just been launched to help organizations build up their Analytics Team and to assist organizations in understanding and solving their business problems.

Do you want to know more about how Business Data Analytics Solutions Pty Ltd can help you to build your in-house Analytics Team or provide you with the relevant Analytics Training which your organization needs, contact us at See link below:



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.