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?

pexels-photo-186461.jpeg

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 carol.hargreaves@dataanalyticsexperts.com

Business Data Analytics Solutions

Analytics

 

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 http://www.dataanalyticsexperts.com See link below: https://www.youtube.com/watch?v=qVJ6tmlO5_8

 

 

Are you Making Good Use of your Data?

 

 

Analytics

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 https://www.dataanalyticsexperts.com

 

 

 

 

 

What makes a Good Analyst?

_techniques

 

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 http://www.datanalyticsexperts.com or contact me.

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

 

_techniques

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.