Data Analytics, Machine Learning. A real business need?

There aren’t many people nowadays who would not have at least heard of the term “Artificial Intelligence” or AI, with all the frenzied media hype and the many romanticized and science fiction movies around it. AI has become a house-hold term though much less a reality despite all this hype.

The greatest impediment to AI to date has been the fact that programmers need to ‘program’ (a software) and for that software to perform a certain function. It cannot therefore think and act on its own. It will only do what it has been programmed to do and in response to certain circumstances, the so-called AI that scientists kept developing over the years could only do just that – respond to a specific set of circumstances based on what it was programmed to do. What AI could not do till now was to think on its own and respond to a new type of circumstance and to gather ‘experience’. This landscape though seems to be changing now and soon may be part of the history in pursuit of real Artificial Intelligence.

With the evolution of the ICT domain, the terms “Machine Learning” and “Deep Learning” have found their way into modern tech terminology with the discussions around their use spanning across Big Data Analytics, BI (Business Intelligence), and most importantly AI (Artificial Intelligence).

Artificial Intelligence and Machine Learning?

Think of a machine that can think and respond as a human being with zero defects in nature; it would be a perfect example of an AI in real life. Add an artificial consciousness to it to make it much more perfect.

Machine Learning itself is a form of AI, perhaps the most promising form of AI to date. This enables certain algorithms to read or observe and gather knowledge and experience much the way we do and learn and evolve to a certain extent and respond to certain situations through that self-learning without executing any pre-programmed queries like an ordinary application. This type of AI aims at the improvement of computational thinking that can self-learn and advance when new data or information is presented.

Such advanced technologies have improved or enhanced many activities that require human intervention to a level where no human involvement is required. For example, if you are using Google search that is powered by complex ML algorithms, the algorithms will enable Google to come up with new search signals and aggregations to provide an intelligent user experience that is personalized. (Google’s “RankBrain Algorithm”). This is actually in existence though it is not easy to believe and we take it for granted every time we do a ‘simple’ Google search. You will have noticed that it delivers more appropriate results within a split second: this is a ML-based AI in action behind all that apparent simplicity.

Deep learning on the other hand is very similar to Machine Learning, but with the difference that it’s designed to study and learn a very specific set of information more deeply and react more intelligently to that data. This difference between ML and DL is somewhat akin to a layperson and a trained professional doctor responding to an illness: one will look at it very generally and learn and react to a certain extent while the learning and response of the other one are at a deeper or more advanced level. The actual difference here is based on the algorithm behind the learning ability which will determine the extent of the learning and the relation.

With interconnected neural networks (a computer system sculpted basing the human brain and nervous system’s designs) and Deep Learning algorithms, Machine Learning has earned its way into modern-day business intelligence and data mining to enhance data analytics.

So how will all this affect us? Here’s how. Some examples of the applications of Machine Learning-based systems are:

  •  Improved AI – Vehicles are driving themselves with collision avoidance reducing the risk to human lives and autonomous Nano mites treating cancer cells in most complex systems in the human body such as the human nervous system.
  • Improved AI game playing – for years we are used to playing computer games with the computer as an opponent where most of its moves are predictable since they are preprogrammed. However, when an AI comes to the play backed with advanced ML the computer will be hard to predict and will give you that experience of a lifetime.
  • Complex data analysis – This will enable in-depth views of any kind of data sets regardless of the quantity or the complexity. Paving the way towards predictive and prescriptive data analytics with higher accuracy.
  • Developed InfoSec measures – there will be intelligent biometric access control which is so smart which will predict criminal behavior even before a crime takes place. (not as in the movie “Minority Report” though) We already see technologies such as “Cognitive Vision” being used to identify certain faces and objects through camera feeds.
  • Improved processes – Financial systems, banking systems, Accounting, or any data-driven system will be more efficient and with improved accuracy will give timely reports and predictions reducing any opportunity risk. Not only finance, we can expect legal court proceeding outcomes will also be predictive thanks to ML in the near future. Human judgement will easily be replaced by the machine soon.

By the time this article is getting published, you may have already witnessed the wonders these technologies can bring us. But that is just the beauty of it. Imagine if things go rogue? Yes, it will bring certain negative consequences as well. I let you take the judgement here. Businesses nowadays have already taken a step forward by adopting technologies such as Big data analytics, AI driven data science…etc. The visionary leaders behind such organizations foresee the value and the business impact such investments will bring them in the near future.

Here at Zone24x7, we work closely with such technologies building great customer centric products and services that empathize and resolve their most unique business challenges. With decades of experience in Machine Learning and Artificial Intelligence, Zone has produced a few key products/services that help any business to “Cross the chasm”;

  • The Analytics Center: A data platform to build, deploy, and manage big data solutions with AI-powered actionable advanced analytics
  • FaceAuthMe: A facial biometric authentication platform that uses advanced machine learning and AI algorithms to provide Strong Customer Authentication
  • SerendibAI: An artificial intelligence-powered cognitive vision platform that analyzes video footage to provide actionable insights
  • MATRIX24x7: An IoT driven remote monitoring and management platform that can be integrated with a range of external systems to give users centralized monitoring and troubleshooting capabilities

With these technologies any business organization will be equipped to deliver more efficient, reliable and experiential services to their clients. 

Are you ready to lead the next generation of the technology evolution of your business? It is your call now.

Thivanka Vithange

Senior Business Designer


Choosing the Right Algorithm at the Right Time – The Science of Impactful Product Recommendations

With the evolution of technology, online retail shopping has come into action, playing a major role in the modern world. A personalized recommendation system aims at identifying products that are of most relevance to a user, based on their past interactions.

This enhances a user’s intention to browse more products and makes them more likely to buy these products, effectively increasing business revenue and user experience. Hence, it is of vital importance that the evaluation of recommendations in such a context provides an end user output based on criteria which is selected in a way that maximizes business revenue and user experience. This chosen ‘most optimal criteria’ may vary due to different user preferences, seasons, and many other factors. Therefore, selecting the most optimal criteria has to be done very thoroughly, for which an effective and efficient evaluation technique is essential.


Where Do You Stand?


In this fast-moving modern world, People tend to buy online due to their busy schedules and easement and any outdated organization that doesn’t support this will be left behind. In a post Covid-19 world, online retailing and e-commerce without a doubt will increase immensely, forcing almost every organization to use online retailing for survival. Recommendation systems play a very important role in this, helping out with revenue and user experience. All the leading retailers worldwide use modern recommendation systems. It is definite that online retailers that use primitive recommendation systems will not be competitive enough to survive among the others who already use standard recommendation systems.


Multi Armed Bandit

Evaluation of recommendations can be categorized into two: offline evaluation and online evaluation. An example for offline evaluation is the Multivariate Testing Method which allows exploration of the most optimal criteria within a specific period of time, but afterward serves recommendations using the winning criteria. Hence it only provides a single cycle of exploration to exploitation, and does not allow automated further exploration cycles. This leads to a requirement of manual intervention once the criteria pass its optimal performance. These limitations bring out the necessity of online evaluation that supports automated multiple exploration cycles, which leads us to Multi Armed Bandit. The Multi Armed Bandit problem is a concept where a fixed limited set of resources are to be allocated among competing choices in a manner that maximizes their expected gain.


Multi Armed Bandit In A Retail Context

The endless expansion of e-commerce has led retailers to advertise their products by displaying. This is done via recommendation after considering various factors. Recommendation systems are growing progressively in the field of online retail due to their capability in offering personalized experiences to unique users. They make it easier for users to access the content they are interested in, which results in a competitive advantage for the retailer. Hence it is necessary to have smart recommendation systems. Recommendation systems using Multi Armed Bandit are capable of continuous learning, that is continuously exploring winning criteria and exploiting them without manual intervention.


What We At Zone24x7 Do

We excel in offering smart recommendation systems. We are well experienced in coming up with recommendation systems that give out different results to the user each day by processing massive loads of data in the intelligent back-end. We have studied every possible way to do that and selected 3 effective algorithms to the MAB problem, which are in summary:

  • Epsilon Greedy Algorithms
  • Upper Confidence Bound Algorithms (UCB)
  • Thompson Sampling


We chose Thompson Sampling for the retail recommendation system and it has been one of the highest performing solutions due to less cumulative regret. It is also the highest cost-effective solution when it comes to implementation.

Multi Armed Bandit can be recognized as the core ideology of the online evaluation system and only a brief explanation about it is given here.

To read more on this:

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Key Services, Platforms & Products :

Big Data Analytics | Data Science | Analytics Center

Umesh Perera

Software Engineer