Each passing year brings up endless technological advancements and opportunities for the business revolution on adopting innovative solutions. Businesses are employing technologies to revamp the operations and marketing strategies that could result in effective revenue generation and streamlined processes. From digital businesses to logistics, technology adoption is getting faster.
Technologies such as Artificial Intelligence, Machine Learning, Neural Networks, Big Data, Blockchain, IoT, etc are trending individually or with the blend of others. The use-cases solved with these technologies make them crucial for various business practices that vary from security and pattern detection to data collection and automation. Hence improve revenue, marketing, and performance. For instance, many online businesses ensure online security by detecting fraud in the financial system using AI-powered solutions.
Let’s first understand the terms individually…
Artificial Intelligence: In the field of computer science, artificial intelligence (AI) or machine intelligence corresponds to a device that perceives accordingly with the environment and takes action and maximize the chances of achieving goals successfully. AI is used to describe machines that simulate cognitive functions in such a way that they associate with human actions and mind, they learn and act accordingly while solving a problem.
Blockchain: A blockchain is originally a chain of blocks that hold records and linked with each other using a strong cryptographic hash function. In each block, a cryptographic hash of the previous block, the transaction information and timestamp are stored. The main advantage of blockchain is that it is a protected way of storing confidential data because no unauthorized entity could access or modify the data that is stored on the blockchain.
Big Data: It is a field that deals with a huge dataset, its analysis, and ways to extract information from the web. It also covers data processing using innovative techniques instead of using traditional data processing software. In big data, there are three types of data that are collected; structured, semi-structured and unstructured. Big data trends include predictive analysis, advanced data analytics strategies, and user behavior analytics.
Internet of Things (IoT): It is a network of interrelated computing devices, digital machines, etc that are used to transfer data from one source to destination by collecting it in real-time. In this process, no human-to-computer or human-to-human interaction would be needed. Devices that are connected with each other are assigned unique identifiers and data transmission is done to central hub of organizations.
Edge Computing: A distributed computing paradigm in which computations are done and data is stored to the closest location where it will be used to save the bandwidth and response time. Using virtualization technology, edge computing is becoming easier to deploy in various industrial applications and edge servers. Networks in edge computing evolved to application components and hosts at edge servers.
All these technologies face some traditional challenges. For instance, in AI research a vast knowledge is required that is based on knowledge, reasoning, planning, representation, natural language processing, learning, perception and huge datasets for model training and testing. The discrepancy in any methods leads to severe circumstances such as model failure. Moreover, AI includes several approaches to meet the long-term goal of problem-solving. These approaches include algorithms for computational intelligence, statistical methods, and traditional symbolic AI. the development of neural networks based on the dataset is done to teach the system how to react in particular circumstances.
Blockchain technology also has several challenges that vary from its scalability to interoperability, standardization to limited developer supply in blockchain and its energy-intensive PoW to strict regulations in place. In big data, major challenges include data collection, capturing, its storage, security, analysis, transmission, and data source. There are three concepts behind big data that are variety, volume, and velocity. Data analysis is one of the biggest challenges and goals in big data technology. In IoT, data security is the major concern of industries deploying it. Data security of central hub as well as the data in transit to avoid cyberattacks that could compromise confidential data. The data in transit is vulnerable and can be scrapped by cyber hackers.
Blend of Technologies
For a comprehensive understanding of how these technologies are interconnected and how their pillars collectively contribute in the digital world, below is the detail;
Starting with the Internet of Things that adhere to the goal of big data by collecting data from various sources. Computing devices communicate with each other and on collecting data from a particular source, transmit it to other nodes, huge data when generates falls into the category of Big data.
Big data then needs to be refined, filtered out, labeled, and processed. For this various data analytics tools are used that identify the nature of data and assign them to label according to their most suitable category. These tools help clean out the data and filter out the ones that are necessary and the one that is garbage.
The data that is collected needs to be transmitted in a secure manner and stored as well. Here comes blockchain that provides a highly secure way of protecting sensitive information over an immutable ledger. No unauthorized entity could access, alter, or delete the data until or unless it is registered. In this way, the purpose of data security is achieved in the whole scenario.
Data analysis is the step, where comes in the game AI algorithms and techniques. With huge datasets, the AI and machine learning models are trained and tested using another huge data chunk to analyze the performance of the model. Based on previous knowledge, big data is analyzed using built neural networks. The nature of data is analyzed and labeled accordingly for better understanding and decision making.
The More, the Better!
More data would be, the more powerful the neural network would be and precise results will be given that eventually help businesses in interpreting the future trends and hence improvements are done accordingly.
Data is analyzed using AI algorithms and underlying technologies and is given the shape in the form of graphical representations, flowcharts and other illustrations depicting the current and future analytics that help business executives make decisions in the next possible improvement in business norms and services. Big data is a big game that in collaboration with other technologies contribute to the betterment in future trends.
In business marketing, these technologies play a vital role, they interpret the interests of people by gathering data from their online activities, social web and shopping, and browsing which helps them get an insight about what people like the most, what type of services they are opting to take in future that is presented in a reformed way that ultimately increases business revenue.