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AI in Data Analytics!

data analytics

Before starting on anything, have look at these two facts right below,

1. The big data analytics market is set to reach a value of $275bn by 2023, growing at a CAGR of 12% between 2017-23, according to a report by Market Research Future.

2. Artificial Intelligence (AI) revenues will see a surge up to $47 billion by 2020, from $8.0 billion last year, as per the International Data Corporation (IDC).

WOW! These significant numbers are quite enough for you to realize why we are talking about AI and Data Analytics in the first place. Let’s speak from a business perspective here… What do you think worries businesses around the world? Well, you might say profit but to reach that target businesses needs to understand their customers, and how this could be achieved is by the use of data. However, with so many buzzwords flying about such as data lakes, machine learning, and artificial intelligence, it can be difficult to understand where the value is coming from and what an external provider can offer.

Understanding patterns in a complex data

Data analytics have proven to be a source of potential growth for any corporate environment because companies can learn something from every failure or successful transaction. Let’s face it, humans have their limits to which they can work but once it’s crossed, they become tired and inefficient.

Needless to say, the process for analyzing massive amounts of data is a rather mundane task and it can cause them to get bored very quickly, oh… then there’s another significant factor that needs to be looked into ‘bias’. As human’s, we tend to be influenced by our interests when it comes to making a decision no matter how much we try to avoid it.

When it comes to analytics, AI is often raised a potential solution to automatically extract meaningful patterns from large datasets for decision making. AI plays an instrumental role in the development of data engineering and analytics, and it’s primarily due to its ability to learn just like humans. It addresses the shortcomings of tiredness and lack of challenging tasks by being able to consistently draw out conclusions from varying data.

Another reason why it has proved to be extremely beneficial here is that it’s doesn’t get intimidated by the ocean of data.

Contrarily, it only gets stronger as the size of data increases because it continues to learn and add to its knowledge.

Asking the Right Questions

When it comes to Data, many people are trained to use analytics to find the right answers. But do we have enough people who can ask the right questions or how easy is it for the everyday users to benefit out of it?

When it comes to a common user in data, he/she would be basically looking for some straightforward answers from the data and what most of them complain is that “we can’t get what we want out of our database—it isn’t working right…how do we fix it??” The fact is that there’s nothing wrong with the database: the master and all its clones are just as they were designed to be, the variables are entered correctly and the reporting functions are pulling exactly what they were coded and designed to pull.


The real question here is how to transform the human thought to machines in a simplistic way. This is where the AI technologies like Natural Language Querying (NLQ) is coming in. unlike those days, now, users can just type/speak with the system just the way you are asking a question from your data scientists, In plain English, in other words just how you google your intent.

The system will understand the context behind the user’s question and convert into machine language so the system can pull the relevant information from the data.

Remember data is daunting and can have a lot of insight buried inside it. Technologies like NLQ can help an ordinary user to get the exact outcome they need from the data and at the meantime teach machines to analyze large datasets.

AI benefits

We at Zepto believe that not only the Big brand from Google to Facebook but also small and midsize businesses should be exploring AI and data analytics. There will be some bumps in the road, and there is no room for complacency on issues of workforce displacement and the ethics of smart machines. But with the right planning and development, these technologies could usher in a golden age of productivity, work satisfaction, and prosperity.