Machine Learning

Patterns are inherent in everything we can see, feel, touch, and taste. The reverse is also true for the things that are incorporeal too, I know for the average mind it is weird and far-fetched. In a semiconductor laboratory in one of the Universities, I attended the Professor in charge of the Laboratory explained the concept of sensors to detect smells of vegetables for example onions. About 90% of the class did not understand the whole concept but it works. At a point during the lecture, sensing our confused minds, he jokingly said, “you have to be either a witch or wizard to understand”. Sneering with laugher as he proudly looked at the long linear algebraic equations used in explaining the concept, he said “one day you will understand if you think hard enough”. In the ensuing, while mused over the theories and concepts, I personally concluded that it is all about patterns based on mathematical derivatives.

The Influence of Data Science and Machine learnings in businesses

Leveraging on Data Science and Machine Learning, businesses can do well. These days, one can give predictions that are nearly perfect. The patterns hidden in any data for any given situation could be harnessed and used to predict accurately future likely outcomes. Through the application of this knowledge in our business, we can eliminate errors that can lead to the collapse of businesses, deaths, accidents due to human errors or inconsistent behavioral patterns in humans.

The Story of Janitor and the Security Officer (Amateur Basketball Professional)

On the 12th, of March 2009 in Portugal, a short sketch written and directed by Nuno Rocha (Rocha, 2009). This movie featured two artist Ricardo Azevedo and João Marçal duration for the movie lasts for about six minutes, with the caption 3 x 3. It offers a lot of parallel lessons on how we can use applied science to change unpleasant outcomes in our endeavors.

The skill of Security Officer (Amateur Basketball Professional)

In the movie, a security officer whose duty was to keep sentinel on a basketball facility spends his idle moments on the basketball court pitch practicing throwing the balls into the basket. His continuous time on the basketball pitch has made near expert like a professional basketball player. His throws were always on target through the hoop or ‘the basket’. During one such idle moments, the feat of the security officer won the admiration of the janitor who works at the facility. He harbored the ambition of becoming like him. Will his intended desire become a success?

The trial and error of the Janitor

After the departure of the Security officer to his post where he watches over the facility via CCTV and a tinted glass window, the Janitor decided to emulate him but he failed in all his attempts The psychological evidence of losing which characterized by sadness, dejection, and strong negative emotional outburst of anger and fury was clearly expressed in the demeanor of the Janitor as he missed his third attempt. This situation happens in the world of business all the time. Business targets are often missed because the information to put you ahead is missing or has not been properly applied.

Data Wrangling

Adopting some form of Data Science, the Janitor changed the outcome of his story.
All these times, the security officer sat at his post and observed with rapt attention through the CCTV camera and the glass window laughed at every miss attempted by the Janitor. These misses in the business world can translate into dollars and cents or fatal situation that involves in the life of people.
These “score misses” can be translated to benefit our businesses and daily lives through the application of scientific knowledge. According to Ronald Coase, a British economist and author and a Nobel laureate, when data is tortured, it will confess to anything, which is very essential in the growth of our business. (Landy & Conte, 2016). In the analytics community, extract, transform, and load (ELT) has been used for a number of years enabling analysis of business information but data wrangling is very popular in recent time.
In the application of data wrangling, raw data undergoes the ELT process, not ELT. Data is extracted, loaded, and transformed hence ELT (extract, load, and transformed). Data wrangling also known as data munging, is a method of transforming and mapping data” raw” data form into another format with the sole purpose of transforming “raw” data into appropriate valuable information for a variety of downstream purposes such as analytics.

Taking advantage of data science

In his desperation, he relied on data. He sampled data of the basketball pitch and things used in the game of basketball by using measuring tools such as level, measuring tapes, ladder to collected the data. The height of the hoop from the floor was measured, the diameter of hoop, level measuring was used check level points, and checked the elevations of the basket. The distance from the hoop to the three-point line was carefully measured. He used plastic cones to check the effectiveness of the line from the hoop to the three-point line. He used a table a reference point of the three-point line. Finally, the pressure of basketball was checked. With all this information at hand. He transformed the sampled raw data into a mathematical model. The security officer sat at his post and laughed all the time but the application of his derived mathematical model, the story changed! Three things were clearly evident after the application of the model.

1. The outcome of his “throw-ins” changed. He never missed a ball. The application of data science changed the losses to gains.
2. The Security Officer was in awe of the newly found winning ways of the Janitor it moved him to his feet. The application of knowledge will change the opinion of your business competitors.
3. In the end of the video, the janitor was seen changing his janitorial clothes into a baseball outfit. The security officer was also unwrapping similar clothes. Applying data science to your business can change your business outlook. It will cause you to swim with sharks in the deep waters of business recognition. You will be the best or ranked among the best.

Real-life scenarios in African Business setups

When I visit shopping centers in Accra in Ghana, I wonder what the apathies of the owners of these big shopping outlets are when they fail to deploy data science techniques to improve the outcomes of their businesses and also the management of personnel.
For instance, even the positioning of products can be used to improve sales. On many occasions when I shopped at Accra shopping mall and purchase a large quantity of bottled water for events. I left the supermarket without anyone asking for our contact information, the reason for the purchase.
With such a high volume purchase, it is easy to get details of any customer with the discount bait! Although there are many simple ways of collecting customer data, the discount bait is one of the commonest! Data collection from customers can help businesses with the main purpose of transforming these raw data into useful information which translates into insightful winning stakes like the janitor in the short movie sketch.

Some lessons from European Business

Some European Supermarkets have mastered the placement products so that upon entering a particular shopping outlet one spends less time and also enjoys the shopping experience. For instance, I know where to pick sardines in any Aldi or Spar shopping center in the UK, Germany, Austria, or Holland. There is a science behind the position of these items and the psychology behind it works in a subtle manner.
In some of these shopping centers with multiple outlets, data collection is very important. The collected data enables them to target customers with specific adverts based on historic patterns. Customers are therefore motivated by small discounts to purchase a card which their Business intelligence units use to prepare campaigns and targeted advertisements. Apart from these benefits, the card which is linked to a database can be helpful in diverse ways. For instance, supposing the main warehouse of these retail outlets want to distribute their products, relying on historic data and buying trends of consumers and spatial information of these outlets, products can be distributed based on buying trends and also the necessary channel advertisement selected for maximum sales throughput.

Conclusions

To be a winner, our game plan must always be robust and ruthless to aim at only winnings. To achieve this purpose, our business strategies must go beyond the normal strategies. According to Garry Kasparov the world Chess champion, “humans are not consistent, we cannot play under great pressure. Our games are marked by good and bad moves – not blunders, just inaccuracies” (Kasaprov, 2019). For our business to meet its targeted goals, we must leverage on AI. Garry Kasparov states authentically that “The future belongs to human and computer cooperation.” (Kasaprov, 2019).
There are a lot of platforms and technologies on which gathered data can be used to transform these data into meaningful information. There many software and tool to transform data into meaning information according to Ubuntu pit, there twenty commonly used it by professionals in the field, namely Google Cloud ML Engine, Amazon Machine Learning (AML), Accord.NET, Apache Mahout, Shogun, Oryx 2, Apache Singa, Apache Spark MLlib, Google ML Kit for Mobile, Apple’s Core ML, Matplotlib, TensorFlow, Torch, Azure Machine Learning Studio, Weka, Eclipse Deeplearning4j, sci-kit-learn, Microsoft Distributed Machine learning Toolkit, ArcGIS PredictionIO and many more. (Ubuntupit, 2019) However, one of the platforms I highly recommend is Microsoft which has a range of powerful tools like Power BI® and Azure Machine Learning Studio®. Azure Machine Learning Studio is really commanding because of its simplicity, scalability, and its cutting-edge touch ahead of the other technologies. The Machine Learning Studio on Microsoft Azure AI dev stack is a powerful data science tool. This Machine Learning Studio is a powerful yet simple because it is browser driven, visual drag-and-drop authoring environment where no coding is necessary. This allows you to transform your data into meaningful information and deploy with few clicks.

Reference

Kasaprov, G. (2019, 05 228). About Deep Chess Blog. Retrieved from Deep Chess: https://deepchess.org/blog/f/dont-try-and-beat-ai-merge-with-it-says-chess-champ-g-kasparov
Landy, F. J., & Conte, J. M. (2016). Work in the 21st Century: An Introduction to Industrial and Organizational Psychology, 5th Edition. New York: Wiley.
Rocha, N. (Writer), & Rocha, N. (Director). (2009). 3×3 [Motion Picture]. Portugal.
Ubuntupit. (2019, June 19). Retrieved from Top 20 Best Machine Learning Software and Tools To Learn in 2019: https://www.ubuntupit.com/top-20-best-machine-learning-software-and-tools-to-learn/