EDITOR ’ S QUESTION
FADY RICHMANY , SENIOR DIRECTOR & GENERAL
MANAGER – UAE , DELL TECHNOLOGIES
With the developments being made in the field of Machine Learning today , the practical uses in enterprises are endless . Machine Learning systems can be used to help anticipate trends and identify problems , thereby playing an important role in supporting decisionmaking processes . Enterprises can also use Machine Learning for customer retention , since Machine Learning systems can study customer behaviour and identify potential steps for customer retention . Additionally , they can make use of Machine Learning to help with market research and customer segmentation . This allows them to deliver the right products and services at the right time , while also gaining valuable insights into the purchasing patterns of specific groups of customers to better target their needs . Furthermore , enterprises can also increase their operational efficiency by deploying Machine Learning to handle day-to-day routine business tasks , thereby speeding up operations , freeing up their employees for more innovation and creating new business opportunities .
With some vendors often claiming to have some Machine Learning secret sauce in their wares that will revolutionise an enterprise ’ s business , enterprises should be careful when selecting the right Machine Learning systems . Choosing the most suitable Machine Learning system is critical to the success of a Machine Learning project . There is no one-size-fits all approach , as selecting the best Machine Learning algorithm will depend on the type of data available and how it will be mined and used . The first step any business should take before searching for any kind of Machine Learning system is to clearly identify the problem , divide it into segments and formulate a clear problem statement . This will give enterprises an idea of what type of algorithm is needed to solve the issues at hand .
Different types of Machine Learning algorithms exist , such as supervised learning , unsupervised learning and reinforcement learning . The next step would be to choose the right Machine Learning system that will align with their specific business priorities and goals .
That being the case , companies today know they need to increase their investment in new technology , however they are hesitant of change . Executives may not be fully aware of the benefits Machine Learning technology can provide to the business . Therefore , before making a case , CIOs and IT decision-makers must analyse the organisation ’ s vision and goals , and look for the IT solutions that will support in achieving these goals . They must make the case for Digital Transformation through a mission driven and business value perspective , which will allow key decision-makers to develop a better understanding of what the business is investing in . This could include a focus on revenue generation , profitability , as well as employee efficiency and productivity . CIOs must also take security into account at every step of the way and have a solid security plan in their proposal .
As Machine Learning systems are complex by nature , it should come as no surprise that deploying a Machine Learning system across an organisation comes with challenges . As Machine Learning requires large sets of data , moving around large quantities of data can be costly and time-consuming without proper data storage and management . Organisations must also be wary that the data sources used are of exceptional quality or else this would hinder model accuracy . Once relevant data is gathered and selected , creating Machine Learning models , training and retraining them to ensure high levels of accuracy can be a time consuming and intricate process .
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