In today’s hyperconnected world, every second to generate huge data quantities for Big Data. Every digital act brings us data: a social post, shopping slip sensor reading a subconscious activity or apparently hard data pollution from the environment. All of these different types make up the content which now we call big data. Whereas many see this plethora of information as a great opportunity, others view it more as giant workload for managing. Therefore what is called for is methods or means by which organizations can grasp and extract useful meaning from out of their wealth (or ocean) of raw data streams. The way forward, then, must be what is now known as Smart Data.
This is an evolution in quality from Big Data and is carried on by Artificial Intelligence (AI).The Age of Big Data: Opportunities and Challenges The expression “Big Data” evokes any large, complex dataset for which traditional data processing technology is useless. Its three Vs–volume, velocity and variety–are what make it Big Data. For example, there is an endless supply of data, created like nowhere before: from patient records and online transactions, for instance, the rates of those that man cannot even begin to analyze at least over five years.” Information on this scale offers potential in almost every industry.
In medicine it is possible with Big Data to learn what practices produce the best outcomes for patients or to save on development costs for new drugs, for example; While companies are able using Big Data technology track customer behavior and better target the right kind of marketing pitch at their market.From mingling information together like this you won’t receive anything useful. The real issue is how one turns this mass of data into value. Without specialist tools, Big Data can easily turn into “data mess”–where there is simply so much information that usable lessons are lost in it. At this stage let’s introduce Smart Data. This is a improved version of Big Data in which just the relevant, correct and action-oriented entities have been laid bare.In come Artificial Intelligence.
Artificial Intelligence in the Big Facts Age: Artificial intelligence is on the move again as a catalyst. Yet no matter their size or count each individual thing is as well delineated and placed in relation to another element which is its own. “Quantity” in Big Data-its sometimes called by a slogan succinctly put that an image comes to mind of a person standing high up in the air. He can see everything simultaneously. The very things are countless and yet without beginning nor end.
Quality is now referred to as Smart Data in today’s Big Data where quality obviously matters. For example AI (Artificial Intelligence) Business Intelligence BIlanguages like Axiomatic Dumpling Induction Grown Networks are made to provide Smart Data.
Large-scale datasets beyond the human perception are identified for patterns and relationships using AI-based algorithms. In order to enhance the accuracy of results as time goes by, learning systems (LS), now known among westerners by one appellation ‘machine learning‘(ML) have subsequently developed into an important subfield of AI.
With AI, raw data becomes Smart Data-refined, organized and structured in a way that people can understand and act on.
Data Filtering And Noise Reduction This is the greatest quick win for AI in Big Data. Get rid of what you don’t need or things that are wrong; with AI done well, you gain huge rewards. Energy finance Frequently a database of enormous size may contain only a tiny bit that can work for decision-making. The key is therefore to rid oneself of everything else, and AI will take care of the rest. It rationalizes this mass into the few facts that are needed. Known as noise reduction this operation changes your data feeds into tightly-focused and on-the-ball insights.
Advanced Analysis and Predicted Insight Traditional statistical methods for analyzing data are reactive. But AI make predictive analysis possible – and even predicts the future by learning from history. By modeling on historical data, AI can predict customer actions such as whether a buyer will cancel-off a purchase or make spontaneous purchases. It can presage shifts in the market of a whole category. AI is able to perceive potential danger where none has yet arisen, preventing problems before they start. Such changes turn Big Data from descriptive statistics into a weapon of attack strategy. It is not merely a paper tiger now, but delivers hard punches at the enemy’s weak points through its pinpoint precision combined with overwhelming mass. Unlike old-style statistical analysis, we can see IData – that’s the most important change of all.
AI: Instant Decision-Making With Big Data Real-time analysis is another way AI Smartens Big Data. Whether it is banking, healthcare or other industries, the time at which decisions are made is important for very different reasons in different sectors. By packing Computational Intelligence into the Internet itself, AI systems process data in real-time and can offer immediate insights of their own. On the other hand, AI trading systems can react to market changes in milliseconds. Clinical trials using AI assistance to help clinicians diagnose patients’ symptoms and the state of one’s disease on a daily basis are but a few more examples.
Natural-Language Processing and Unstructured Data A very high proportion of Big Data is in the form of unstructured text documents, emails, social media posts and videos. Traditional analysis on such data was difficult or impossible. But advances in Natural Language Processing (NLP) means that AI can now understand and interpret unstructured data, deriving useful insights out of speech; written text or even graphical images. By transforming these unstructured data into Smart Data, AI opens up an immensely rich source of previously untouched information.
Industry applications of AI and smart data: The AI revolution has already spread to many areas, turning Big Data into Smart Data.
Medicine: In medical care, data is being translated into information. AI systems let doctors predict patient outcomes, suggest treatments and pick up the first hints of illness by analyzing medical records and genetics, or monitoring wearable sensors. Examples abound.
Finance: AI is used in finance to process huge amounts of data in real time so as to uncover new investment opportunities that would be hard for even the most capable or devoted human investor–let alone one with a whole array of other tasks on their schedule–to spot. AI algorithms impute anomalies from the flow of fund-transfer transactions, which are clear evidence of fraudulent behavior, in a timely way and before this can develop into major financial crime.
Marketing: At the same time that AI is changing how consumers are seen by companies, it also lets marketers fine-tune their understanding of individual customers’ tastes. By pulling in social media interaction data, consumer transaction records, and even website utilization habits in study after hypothetical study, AI can build an extremely precise model for each particular person bought to mark being able that company’s marketing outreach resonates more effectively with its target audience at discounts far lower than otherwise could be expected. This kind of thing should not only bring higher margins but also stave off bankruptcy.
Manufacturing: In manufacturing, AI already performs diverse tasks from optimizing supply chains to predicting when machines will need maintenance and inspecting for defects. At smart factories, an AI system that can take data from sensors located inside the machines themselves on site takes over predictive maintenance, obviating costly downtimes before they happen.
The future belongs to AI and Smart Data
In the future, as artificial intelligence becomes ever more sophisticated, when everything–from the life sciences experiments with millions of data points to climate effect analysis traversing the world–is collected into datasets of increasing complexity for AI to interpret; it is easy to see that whether called Smart or Big Data will not only be a major issue in computer science alone but indeed all human endeavor. The marriage of AI and Smart Data will mean that every single aspect of an entire economy has both intelligence and data-driven decisions
But at the same time, as AI technologies such as deep learning and reinforcement learning develop machines will not only learn from incoming accepted information, they will start zeroing out whole densities of new knowledge all by themselves. Effectively these systems will be increasingly involved in identifying emerging patterns, risks and opportunities, so that Smart Data continues to grow stronger still.
Conclusion As it changes from Big Data to Smart Data, it represents a massive shift in the management of information. Before Smart Data, when faced with Big Data. We are all just supporters of AI technologies: They offer tools that can process “big” tables (sets of data) efficiently and precisely
Today as AI advances further, converting raw data into real-time intelligence will certainly prove easy for companies, government agencies and individuals. Of a world awash in data AI is the only way to change that torrent of meaningless information into useful knowledge you can actually use.
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