Data

How Data Analytics and Machine Learning are Changing Our World?

Interesting fact- Every 2 days we create as much data as we created since the inception of mankind up until 2003. This fact underscores the value of Data analytics and Machine learning in recent decades. The amount of data being produced is absolutely mind-boggling. With the epiphany of the value of data, organizations around the world started to change. Companies started to invest in the data analytics infrastructure to harness the power of data.

As we are amassing this huge mountain of data for ourselves and using it to improve our decision-making capabilities and reinvent the way we present and consume these products and services, humans now recognize the inherent value of Data Analytics and Machine Learning. Machine learning and data analytics are the gateway for humankind to transcend to a greater, more advanced society that can solve almost any problem with almost no physical effort using data and AI analytics.

Data analytics along with machine learning is changing the world and people like you and us are the witnesses of this revolution. Even though we cannot comprehend the scale of this revolution because we only see what’s in front of our eyes. But the moment you start contemplating and connecting the dots, the change has been nothing less than a miracle.

As the world was evolving in terms of data creation and storage, data analytics as a tool has also evolved drastically. The process of data analytics has been turned upside down by the advent of machine learning and AI-based analytics tools. The efficacy and efficiency of data analytics have attained new heights.

Let’s track the evolution of data analytics and the integration of AI and ML into the process.

The Conventional Method of Data analytics

The process of data analytics was manual for the most part and the only integration of automation was the analytic tools used by the analyst to identify patterns and draw insights from the data. 

The fundamental objective of  Data analysis is to answer a question. The analyst sources the question from any function of an organization or company including the HR team, technical team, business team, or the upper administration. But the steps involved in the conventional method and the AI-based method differ. 

Data analytics

The steps involved in the conventional method are as follows:

  • The analyst garners all the relevant documents, spreadsheets, and reports from the database manually.
  • The analyst superimposes all the documents on one another to identify common strings and outliers.
  • Analytics tools filter the outliers and use the remaining data to draw insights.
  • Once the tools reveal these trends and patterns, the data analysts devise some hypotheses to address the pain points of the question at hand.
  • The analyst communicates the insights and conclusions to the stakeholders and decision-makers of the organization so that they can make a solid decision to address the issue.

Limitations of Conventional Data Analytics

But the fact that the process evolved implies that there were some limitations in the process. The time limit binds the Data analyst. Therefore, we rarely achieve the complete analysis of data. The efficiency of the process was very low as the results did not convey the entire picture. And the analyst had to rely on the initial hypothesis to come up with a plausible argument.

Limitations of Data analytics

The entire process of data analysis was turned on its head by the introduction of AI-based analytics tools and machine learning software The use of AI-based tools paved the way for a more efficient and effective analysis of data under serious time constraints. A major part of the process was automated using these tools and the entire process was re-engineered to improve efficiency.

Machine Learning Analytics

The entire process of Machine Learning Analytics is different from that of data analytics. The step where the data analyst collects all the relevant documents from the databases and analyzes these documents to draw valuable insights is now done by AI-based tools.

Machine Learning analytics

The connections and trends are all identified, all possible hypotheses are tested and only the most concrete results are shared with those asking the questions. And the most impressive thing about all of this is that everything occurs in a matter of a few seconds. So, the time constraint is not an issue anymore all thanks to the extreme computing power at our disposal. 

Also, the efficiency of the entire process has increased exponentially as a result of automation. The analyst can now work on connecting the dots and painting the complete picture for the stakeholders and decision-makers. To help them address the focal issues and resolve the problem.

Advantages of Machine Learning Analytics

There are certain advantages of using Machine learning analytics over traditional data analytics-

  • AI and Machine Learning tools can determine commonalities and group objects like people together based on certain traits that may not be intuitive to the analyst doing it manually.
  • If many factors are changing simultaneously in a process, then the AI tool can attribute a result to the cause. Albeit it may seem ambiguous to a person.

Machine learning analytics can even predict future events with vast amounts of data. An AI-based report in Canada predicted the Coronavirus outbreak in the country 6 months before the actual outbreak. The system predicted the outbreak by analyzing the prevalence of the virus across different countries in the world. And then predicted the outbreak based on the movement of people in cities across Canada.

Machine learning analytics is one of the most powerful tools at our disposal. And without these tools, companies, and organizations all across the world would just be sitting with disparate information.

How do Famous Companies use Machine Learning and Data Analytics?

Some of the most well-known companies rely heavily on machine learning and data analytics. Some of the examples are-

  • Hubspot uses Machine Learning Analytics for language processing in its internal content management systems to pitch sales to the right people and serve their existing customers better.
  • Pinterest recently acquired Kosei, a company specializing in Machine learning and AI. To implement Machine Learning analytics in all of their operations ranging from content management to spam control.
  • Twitter uses Machine Learning analytics to evaluate tweets that have the potential to drive greater engagement and place them higher on the feed.
  • Google is the perfect example of a company that has harnessed the power of Machine learning analytics to optimize performance and streamline operations. The laser-sharp targeting capabilities of Google are all thanks to their strong machine learning and AI-analytic tools.

Companies Using Machine Learning analytics

Millions of companies around the world are using Machine Learning analytics to improve their operations and target their existing audience. But we barely know the real potential of Machine learning analytics. Only 0.5% of all the data that is created and stored is being analyzed to produce results.

Imagine a time when we will analyze more than 10% or perhaps 20% of this data. The results would be far beyond human comprehension. So it can be safely deduced from this information that data analytics is not going anywhere anytime soon. Also, the data that we need to analyze is only going to increase exponentially.

To explore the scope of Data analytics in the corporate world you can refer to our article on Data Analytics and why is it of interest to companies.

Will Machine Learning Replace Data Analytics?

The machine learning tools surely make things easier for the data analyst but the entire process cannot be automated because there’s a limit to how much a machine can do. No doubt it can process data much faster than us, but at the end of the day, someone needs to consume the results of the analysis to make sense out of it and communicate the actionable insights to the concerned stakeholders and decision-makers.

So the speculation that machine learning will eat away all the jobs is baseless. In fact, machine learning and data analytics are complementary to each other and work hand in hand around us. 

How Machine Learning and Data analytics are changing the world?

In today’s digital age, social media is one of the most powerful platforms in existence. And all social media platforms deploy machine learning analytics in all forms and with great accuracy to collect as much data from the users as possible and profile them. The data they collect from you reveals everything about you. Your product preferences, your hobbies, your interests, the type of music you like, the kind of posts that you interact with, and a million other things about you. 

Machine Learning Analytics and the world

And they tailor the content you consume according to the data that they collect from you. This content sometimes consciously and sometimes unconsciously influences our choices.

It’s fascinating how machines are processing data and suggesting our preferences to companies. Dictating the products we use, the type of content we consume, and the kind of world we live in.

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