Often the words such as data, information, and insight are used interchangeably, but these words are not similar, they have different meanings. Understanding those differences can help you tailor your program to benefit your business.

What is data? How does that turn into information and what kind of insights does it yield?

These differences are confusing right! But the distinctions are the simple ones you see how they work together. Let’s see what their definitions actually mean.

What is Data?

Data is raw with unprocessed facts that we capture according to some agreed standards. Data can be in the form of numbers, images, audio, transcriptions, etc. While working on analytics projects, the first task is to go through the client’s data structure or even normalize it.

What is Information?

Information is a collection of data points that we can use to understand something that needs to be measured. It is data that is processed, aggregated into a manner humans could read and understand. The common ways to present information are through data visualization, reports, and dashboards.

What is Insight?

Insights are gained by analyzing data and information to understand and draw conclusions that can benefit the organization while decision making.  These are the final outputs of the data in a usable form.

How they work together

The data, information, and insight work together to yield a complete analytics package. In an organization the data is collected in a raw manner, later it is converted into a readable format that is the information, this information is later processed to insights which are highly valuable in making big decisions of the firm. If one is absent or inconsistent that means it can affect the overall functioning of the company.

Impact of Insight on Business Decisions

Data-driven marketing is the latest word that is heard everywhere, but insights are more powerful than that. Most of the brands already have data but couldn’t find the right insights. Insight can help in talking about the value of your business more than data does.

Any business decisions depend on insights not the raw form of data. These insights can make a wide difference in the strategies and performance of businesses. Great insights can help you overcome any kind of market problems such as competitors, and consumer behavior.  All kinds of evolutionary changes can occur only with the insights that can help decide better in the market.

According to Global Web Index, insights are four kinds, they are Human, Universal, True to Brand, and Targeted. In other words, a great insight reveals a story that is unique to the brands. Insights help the company and customers by coming up with new and fresh ideas.

The road to getting out valuable insights is through adequate research. Here are a few steps that can help you put information for the benefit of your business.

Goal: Set the right goal and objectives for the organization to achieve through the research. This can ensure you understand better what the company actually needs.

Collect: Once you’re done researching, the next step comes a collection of the data, whether through quantitative or qualitative.

Analyze: Here the collected data is analyzed to get valued insights. It can be about your customers, employees, products, and satisfaction.

Action: The last and final step comes where the insights are gained from analysis and these are put into action by the businesses which can help in a better decision-making process.

Now, hope you got what is the difference between data vs information vs insights and also understood how insights can impact business decisions.

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Anish Agarwal, Director, Data & Analytics, India at NatWest Group discussed how quantum computing plays a vital role in the advancement of artificial intelligence.

Machine Learning Developers Summit (MLDS 2021) is one of the biggest gatherings of machine learning developers in India. With more than 1,500 machine learning developers, 60 speakers from around 200 organisations, the conference corrals India’s leading Machine Learning innovators and practitioners to share their ideas about machine learning tools, advanced development and more.

Anish Agarwal, Director, Data & Analytics, India at NatWest Group, talked about “The Interplay between Quantum Theory And Artificial Intelligence” at MLDS 2021.

The session started with an introduction to emerging technologies like artificial intelligence, a brief on quantum computing, different forms of quantum technology used for various military as well as civilian applications, how it is different from the classical computers as well as how quantum computing plays a vital role in the advancement of artificial intelligence. 

In the field of quantum computing, Agarwal discussed the technique of quantum artificial intelligence, how it can be used for computation of machine learning algorithms and what makes this technology unique. 

Quantum AI can help in achieving results that are impossible with classical computers. He said, as per reports, 25 percent of fortune global 500 companies will have a competitive edge from quantum computing by the year 2023. Tech giants like Google, Microsoft are doubling down on quantum computing.

He then explained the possibilities of applying quantum computing in AI:

• Quantum Algorithms for Learning: Specifically for machine learning, quantum algorithms for learning can provide possible speedups and other improvements in a deep learning training process. The contribution of quantum computing to classical machine learning can be achieved by presenting the optimal solution at the base of artificial neural networks. 
• Quantum Search: Quantum search algorithm can be described as a database search algorithm. 
• Quantum Algorithms for Decision Problems: Quantum algorithms based on Hamiltonian time evaluation can solve problems faster than classical algorithms.

He said, “Quantum machine learning (QML) is not one settled homogeneous field. This is because machine learning itself is quite diverse in nature.” He added, “Quantum Machine Learning is simply the field exploring the connections between quantum computing and quantum physics on one hand and machine learning and related fields on the other hand.”

Agarwal then deliberated on Quantum Game Theory and compared it with classical game theory. He said quantum game theory can be used to overcome critical problems in quantum communications.

He also discussed the advantages of quantum AI:

• Quick resolution of complex problems
• Optimisation
• Ability to spot patterns in extremely large datasets
• Integrating data from different datasets

Agarwal concluded the session by touching upon the key applications of quantum artificial intelligence. Lastly, he mentioned some of the critical milestones for quantum AI and busted a few myths related to quantum computing techniques.  

The critical milestones include:

• Less error-prone and more powerful quantum computing systems
• Widely adopted open-source modelling and training frameworks
• Substantial and skilled developer ecosystem
• Compelling AI applications

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