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The demise of big data and the emergence of smart data are helping comply with demands

The era of bigdata has come to an end. Data has been a vital component of creating strong, long-lasting, mutually beneficial relationships between businesses and customers for a long time. They provided a way for business executives to better understand the customers and fulfill their needs based on previous preferences. But very quickly, the night has changed. We have already bid farewell to bigdata in business, so what next? Yes, it is smart data. The emergence of smart data has leveraged a stronghold on companies to equip them to the improving demand.

‘Bigdata,’ the term that opens the door to over 800 million results in Google search has been ruling over us for the past two decades. Starting from offering a cost-efficient means of insight to optimizing return-on-investment and growth, bigdata in business has played a significant role. Unfortunately, all along the way, marketers had a hard time solving the riddles of data. Whether it is accumulating the right data, organizing it so it can be easily analyzed, or being able to extract useful insights, there are a number of challenges that data engineers are undertaking to extract insight. The technology sector is evolutionary. While some technologies garnered peak popularity at one instance of time, they gradually went on to evolve after being perceived that their data were over. The same thing is happening to bigdata. As the technology has reached a form of saturation, it is turning to be smart data. Data scientists have long been complaining about spending around 80% of their time cleansing, verifying, and preparing data. Fortunately, as smart data comes in handy with a well cleansed, verified and fertilized mechanism, the world finds it easy to handle.

Cremating big data

Bigdata is a great marketing term, but in reality, that’s all it is. The enormous amount of data is nothing useful without getting good insight from it. As companies become more familiar with data processing and service providers abstract away more complexity, bigdata in business will just become big. But data is not dead. It is just transforming to an easier form. By 2025, it is predicted that the global data sphere will be 175 zettabytes, up from 50 zettabytes in 2020. Henceforth, we are preparing for a future where the data explodes exponentially and we have sources to store and get the best out of it.

Why is bigdata becoming useless? Let me explain this with an example. Recently, the volume of data is drastically growing. People also rely on data for many situations. According to a survey of Fortune 1000 executives by the Harvard Business Review, reliance on bigdata initiatives is on the rise. Unfortunately, there are some cases where data reliance has impacted badly on certain scenarios. In a data wrong gone incident, the portal was using more data than was called for. OfficeMax has sent a letter to an individual in Illinois, addressed to Mike Seay. The letter reported the death of his daughter in a car crash. But the worst case was that Seay’s daughter was killed in a car crash one year earlier. The scenario was even ruined as Seay was on his way to attend a counseling group of grieving parents when he received and read this letter.

The rise of smart data

Smart data is digital information that is formatted so it can be acted upon at the collection point before being sent to a downstream data analytics platform for further data consolidation and analytics. The term smart data analytics is associated with the Internet of Things (IoT) and most of the data is extracted from smart sensor-embedded devices. To get the maximum out of smart data, one has to better understand the clues in the question around data. Besides from making data-driven decisions, smart data analytics pushes us to make creative initiatives.

Data analytics were dependent on the famous Vs (Velocity, Variety, and Veracity) that big data carried. However, smart data also revolves around Veracity and Value. With smart data, we focus on valuable data and often smaller datasets that can be turned into actionable data and effective outcomes to address customer and business challenges. By putting in the context of purpose and context, smart data analytics makes the analysis and interpretation of data easy. For example, smart data is used in Open banking where UK-regulated banks have to give customers the option to allow access and control of their personal and financial data to TPPs.

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