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Essential Steps to Transform Big Data Into Actionable Insights



Essential Steps to Transform Big Data Into Actionable Insights

Transforming raw data into actionable insight is about integrating and analyzing data from all sources to determine measurable business results.

No matter how vast your big data sources are, if your company does not devise the right methods for garnering meaningful insights from it, the data is of no avail.

The entry of big data has revolutionized the way businesses work. However, till today, a large number of decision makers are confused on how to extract the right insights from big data. This is mainly because businesses embark on this journey without checking if they have all the parameters in place. Majority of big data projects are implemented after insight expiry or with defective strategies. Before tackling a voluminous amount of data, it is crucial that businesses formulate an apt big data initiative to suit their needs. In our experience, these 5 ways are common to successful businesses and are an effective guide for turning big data into big insights.


1. Determine What’s Actionable

Before you start extracting insights from big data, you need to have a clear understanding of the things you want to achieve from it. Distinguish between the strong areas of your business and those that need re-consideration. Before diving in for answers,it is important to have the right set of questions for big data and its analytics. Address those questions first that you know are bound to lead to economic opportunities and are practically actionable. It is easy to get distracted by the vast availability of big data and its exploration. Thus, narrow your approach to core business problems. Set achievable parameters, otherwise, you will risk the wastage of manpower and valuable resources.

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2. Assemble a Smart Team

The second step is to assemble a team of skilled professionals. Actionable insights can only be garnered from big data effectively with the assistance of intelligent humans. There needs to be a presence of creative personnel who can formulate new ideas, develop technological strategies, and effectuate efficient implementation. Look for individuals who have fair knowledge in the fields of AI, machine learning, big data and its analytics, automated support systems, and the like. Look for team players; people who are adaptable and receptive to the continuous change in data and technology. Big data is useful, but without any humans in the loop, it may end up creating more problems than it solves.

3. Understand Customer Needs

How will you extract insights from big data if you don’t have an idea of your customer requirements and your business needs? Before digging for insights, you need to focus on gaining qualitative customer insights. Thus, businesses must consider the challenges their audience is facing. This means interacting with people who use your product or service, recording their responses, and channeling those responses to improvise your product or service. Organizations must conduct studies and research for predictive analysis. Everything starts and ends with the customer. Thus, businesses must identify the key barriers that are preventing them from achieving their goals and then formulate decisions that will help achieve insights on how to maximize customer satisfaction.

4. Focus on the Right Sourcing

While a company’s main aim is to build insights from a range of data sources, it is crucial to focus on the types of data sources that will aid in the progress. The perfect dataset doesn’t exist. Start with analyzing data from a data mart. Most businesses are confused when it comes to the difference between a data mart and a data warehouse. A data warehouse is obviously an essential asset in any company, but a small and selective data mart produces quicker insights and prevents you from getting mired in complexity. Over time, you can then broaden your horizon and focus on additional data sets.


5. Enhance Speed and Delivery

Speed is a key factor for productive action. For successful execution from insights, you need to act quickly. If you spend a long time discussing and analyzing big data in the hope of acquiring near perfect insights, all your efforts will end up futile. When it comes to big data and its analytics, it is crucial to focus on quick decisions and execution. Today, successful companies like Amazon and Microsoft have one thing in common – they make their decisions from 70% of relevant data available. If they, too, would wait for perfect information for perfect insights, their outputs and revenue streams would face the threat of paralysis. Information that is substantial should be used to drive insights and specific actions, rather than waiting for more comprehensive options. The mark of a successful business is when a business can embed data and insights into its core processes and its everyday decision-making. Over time, this integration will make businesses more receptive to bigger decisions and greater change. 

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Artificial Intelligence in the 4th Industrial Revolution



Artificial Intelligence in the 4th Industrial Revolution

Artificial intelligence is providing disruptive changes in the 4th industrial revolution (Industry 4.0) by increasing interconnectivity and smart automation.

Industry 4.0 is revolutionizing the way companies manufacture, improve and distribute their products. 

What Makes Artificial Intelligence Unique?

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.

It allows computers to think and behave like humans, but at much faster speeds and with much more processing power than the human brain can produce.

AI offers advantages of new and innovative services, and the potential to improve scale, speed and accuracy. 


There are 3 types of artificial intelligence:

  • Artificial narrow intelligence (ANI), which has a narrow range of abilities.

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  • Artificial general intelligence (AGI), which is on par with human capabilities.

  • Artificial superintelligence (ASI), which is more capable than a human.


Artificial intelligence can also be classified as weak or strong. 

Weak AI refers to systems that are programmed to accomplish a wide range of problems but operate within a predetermined or pre-defined range of functions. Strong AI, on the other hand, refers to machines that exhibit human intelligence.


Artificial intelligence has several subsets:

Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing.

What is the Fourth Industrial Revolution?


The Fourth Industrial Revolution is the current and developing environment in which disruptive technologies and trends such as the Internet of Things (IoT), robotics, virtual reality (VR) and artificial intelligence (AI) are changing the way modern people live and work. The integration of these technologies into manufacturing practices is known as Industry 4.0. 

The first industrial revolution used water and steam power to mechanize production.

The second used electric power to create mass production.


The third used electronics and information technology to automate production.


The fourth Industrial revolution is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres, with rising emerging technologies, as real AI, Narrow AI/ML/DL, robotics, automation, materials science, energy storage, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, neurotechnology, cognitive technology, and quantum computing. It implies radical disruptions to everything, industries, jobs, works, technologies, and old human conditions. In its scale, scope, complexity, and impact, the AI transformation will be unlike anything humankind has experienced before.

The Role of Artificial Intelligence in the 4th Industrial Revolution

Artificial intelligence is making companies make the best use of practical experience, even displacing traditional labor and becoming the productive factor itself. 

It offers entirely new paths towards growth for manufacturing, service, and other industries, reshaping the world economy and bringing new opportunities for our societal development.

As AI begins to impact the workforce and automation replaces some existing skills, we’re seeing an increased need for emotional intelligence, creativity, and critical thinking.

Zvika Krieger, co-leader of the World Economic Forum’s Center for the Fourth Industrial Revolution.

Deploying AI requires a kind of reboot in the way companies think about privacy and security, As data becomes the currency of our digital lives, companies must ensure the privacy and security of customer information.

Businesses will need to ensure they have the right mix of skills in their workforce to keep pace with changing technology. 


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