Not using machine learning in your business? You are about to be crushed by your competitor

Many people say nowadays that data is a new oil. Actually, it is better than that. Data is everywhere. Any company on Earth can find it and use it. It is certainly true that more companies are starting to understand this. If you just start thinking about it, be sure that you are definitely not the first in your field. But it is not so bad.

The adoption of a new technology is always connected to high costs of implementation and lack of skill-set. Often it seems like an art. You spend a lot of money in this stage, but your results are not superb. Typically, barriers to entry are really high. You try to find those new alchemists, who are really highly paid, to help you.

The same thing is happening with actionable data insights. Those new alchemists are called data scientists. If you want to earn money from using data, you should hire them and be ready to spend a lot of money. But will it last forever? Can we scale data scientists in line with business needs?

To answer this question, we need to know how they do their job. The main tool in their hands is machine learning. It is a combination of different algorithms which help to predict some variable from source data. It can predict both categorical and continuous variables. These algorithms can perform classification, regression, clustering as well as recommender system tasks. For example, machine learning algorithms can predict gender, age category, marital status, interests or salary of a future employee, price of raw materials, sales in next month, etc. Using those predictions, companies make better decisions and optimize their businesses.

Here are a few widely publicized examples of machine learning applications that you may be familiar with:

  • Self-driving Google car. The essence of machine learning.
  • Online recommendation offers in Amazon and Netflix. Machine learning applications for everyday life.
  • Knowing what customers are saying about you on Twitter. Machine learning combined with linguistic rule creation.
  • Fraud detection. One of the more obvious, important uses in our world today.

The problem is that those algorithms have many options and knobs. It was true in early days of computers. In those days, you had to be a programmer if you wanted to process some information. Now it is available for almost everyone.

The same thing is happening with machine learning now. It is being democratized. We are entering an era where these complex Machine learning algorithms become more accessible to business analysts/ business users. Machine learning due to these new business friendly tools, which make data more useful, more understandable and more actionable, becomes scalable. Without these automated solutions, an average machine learning model could take two or three weeks but now, there can be hundreds of models being created at the same time without writing a line of code. It gives the power to make better decisions in real-time. The speed of changes inside an organization can increase drastically and that is a crucial condition to survive in this competitive world.

It can be given to any employee because it is able to understand natural language. For example, when a salesperson checks her pipeline status within her CRM application, she’ll be able to ask, “How am I performing compared to my colleagues this quarter?” and “Where should I focus my efforts to ensure I reach my quota?” The application will be able to respond immediately, with an explanation and sound recommendations. She doesn’t have to interact with the alchemists to get the answers.


Now is the time for early adopters and early majority as depicted in product adoption curve. Before, machine learning and data science needed manual algorithms and coding. Now, we are entering the era of automated tools for machine learning which are more convenient, useful and user-friendly. They cost less money and, moreover, they are scalable.

It is not too late to implement machine learning in your business. It is the right time. The businesses who will adopt these solutions powered by intelligent algorithms which can enable more effective business decisions will have huge competitive advantage over others who don’t. So, the question is – are you ready to crush it or prepared to be crushed by your competitor? Are you ready to be part of early adopters/early majority or late majority/laggards?

About the Author
Ajay Khanna is  CEO and Founder of Tellius with vision to re-define data intelligence by combining power of search with predictive analytics. Ajay has background with building and growing successful innovative startups. He is a passionate Tech innovator with experience in building new technologies and disruptive business models. Ajay was previously CTO of Celcite, the leading company to build and launch  SON (Self optimizing networks) solution and played key role in scaling the company with successful exit. At Tellius, we love data and we believe it can transform your business – ask us how. Contact us to learn more about how we can help you make your data work for you.


Read Similar Posts

  • Leveraging AI Analytics for Inventory Optimization
    Machine Learning

    Leveraging AI Analytics for Inventory Optimization

    AI analytics can help significantly improve inventory management analytics by introducing advanced capabilities to enhance accuracy, adaptability, and efficiency.

  • 10 AI Analytics Myths, Demystified
    Machine Learning

    10 AI Analytics Myths, Demystified

    Here's how analytics and business leaders can gain a better picture of AI’s strengths and weaknesses when it comes to analytics uses.

  • BI & Data Science: Two Sides of the Same Coin
    Machine Learning

    BI & Data Science: Two Sides of the Same Coin

    Tellius offers a robust machine learning layer where users can train, assess, and apply predictive models. Read the advanced approach to customer segmentation based on an unsupervised machine learning clustering model in Tellius.