September 28, 2018

Why Most Businesses Fail to Incorporate Machine Learning?

Machine learning is a branch of artificial intelligence that automates the process of data analysis. Machine learning is based upon algorithms paired with the rights processes and tools. With the help of machine learning, a system can analyze data, identify patterns and take relevant decisions without any human intervention.

Influenced by its ease and efficiency, more and more businesses are adopting this rising technology often referred to as deep learning. However, 70% of businesses fail to incorporate this technology. Here we have listed some common mistakes that most businesses make:

1. Misinterpreting the Issues:

Machine learning operates on data interpretation and if you define the problem in a wrong manner and provide the wrong data base then you cannot expect the algorithms to function properly. You need to take into consideration all the aspects related to a particular problem to find its cause and then feed the relevant data to the ML algorithms.

2. One Size Fits all Approach:

The common mistake that most businesses make is that they use one single algorithm for everything. That ‘a single model would serve all purposes’ is a very bad assumption and an organization should test different models for the data they possess for different purposes and compare the results and then select the most suitable algorithm for a particular process.

3. Not Providing Enough Data to the Algorithms

Most organizations just build and leave ML algorithms. They do not realize that their ML platforms are not of any use without the relevant data base. ML operates on the principle of data interpretation and if there is no data, there cannot be any interpretation.

4. One-time Investment

Most businesses think that ML is a one-time technological investment which will control all its data in the future. They don’t pay attention on developing and updating it with information and features that it lacks and therefore, suffer to keep pace with the changing scenarios where the world is subjected to a new problem every day.

5. No Strategy Developed for ML

Most firms do not have any strategy for collection, storage, management and analysis of data. Because of this, the process of data management becomes highly error-prone and time consuming. This lack of intelligence strategy makes their ML programs lag behind.


ML is a buzz word in today’s world when crypto-trading is changing the whole financial landscape. The businesses need to develop efficient machine learning and artificial intelligence strategies to keep pace with this rising technology. However, most businesses fail to adopt these technologies. They need to dig deep and see where all they are lacking.

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