Proposal for New Wearable IoT Product

Big Data Tools

Big Data is characterized by high volumes of data, high velocity in growth and transmission of data, and finally a wide variety of data types. This is incredibly different from simply collecting, cleaning, and processing a lot of data. There are many benefits to big data, it saves money and time, it can represent the state of the market, it builds insightful solutions to drive value. To effectively leverage all the big data collected, businesses use big data tools to take advantage of the valuable information. A big data tool is a software made to extract insights from the different data types and sets.

A great example of a big data tool is Tableau; an end-to-end data analytics platform. This is a tool because it allows users to prepare, analyze, and share their big data insights. Tableau excels in data visualization through visual analytic dashboards which involve graphs, pie charts, and pivot tables (Sharma, 2023). One of the features is real-time analytics, meaning this application can keep up with the high velocity of data and provide current reports based on the information. Technologies like Hadoop are considered big data tools because they function to store and process big data. Hadoop also manages data processing for big data applications (like Tableau) (APache Hadoop: What is it and how can you use it?, n.d.). This platform has impacted the modern cloud data lake and created an alternative to proprietary data warehouse solutions. Hadoop systematized computing power for companies in every industry to query big data sets and made it available through a simple platform. Many of these big data tools also feature data integration, meaning it can handle extracting data and interacting with other existing data sources. This proves how well big data and common data tools can be used simultaneously to further produce valuable results.

Relational vs. Non-relational Databases

Databases are sets of data stored on-premise or in cloud and usually categorized as relational or non relational. A relational database involves structured and organized data in a table that is also connected to other tables through keys. An example of this is SQL, which allows users to communicate with relational databases and store data. A non-relational database is less structured in format and does not require a schema. It offers reliability and availability because the data is not stored tabularly, rather within one data structure or document. NoSQL databases provide speed and scalability for large data at high speeds and of all types. As industries grow, the demands for faster and more contrasting insights from large datasets increase as well (Pawlan, 2022). Non structured query languages offered more flexible and scalable databases. Since almost all of IoT data is Big Data, NoSQL can sustain high performance in writing data in realtime, transforming then storing, and querying it quickly. Each option has its own benefits and disadvantages that all depend on the goals and realities of an organization. In fact, many cloud providers support both SQL and NoSQL databases allowing for companies to utilize both types.

Effects of IoT

The internet has changed and continues to evolve beyond any singular expectation. We are at a point now where the internet interacts with our everyday objects to further intertwine us to the internet. These smart devices, like fit bit or apple watch, use IoT technology, which is supported by Artificial Intelligence (Martin, 2022). It has impacted consumerism and how users interact with physical objects. IoT has allowed companies to continuously collect data on their consumers and their behavior, which enables them to develop a deeper story on their customers. All of this works together to benefit the customer's experience, which could mean more personalized ads and communication. Companies can better tailor the products or content you see based on the interest you’ve shown in the past. Overall IoT can be taken advantage of to provide better customer care to stand out against competitors in the same market (Anand, 2023). IoT has had such a huge impact because it helps companies access real-time information.

Machine Learning and AI

Artificial Intelligence and Machine Learning support IoT in improving user experience, scalability, and automation. AI is defined as the science and engineering behind intelligent machines and programs with the goal of mimicking human intelligence. While ML is a subset of AI, it enables systems to learn and improve from experience without being explicitly programmed. The two are very similar but focus on different areas. While AI empowers machines to learn how to perform tasks based on new data, ML helps computers analyze data quicker and helps with predictions. Through IoT, Artificial Intelligence can be used to improve IoT devices by automating data collection. AI makes the devices smarter by responding to human commands and performing tasks autonomously. Machine learning is concerned with the accuracy of results and patterns rather than simply maximizing the chances of success (Focaloid Technologies Private Limited, 2022). Machine Learning also supports Data Analytics by reading, recognizing, and clustering data. It also helps predictive analytics which plays a part in powering recommendation engines. Machine Learning can be extremely helpful in the healthcare industry. The health industry's equipment is being built with IoT capabilities in mind, such as remote patient monitoring tools that help doctors analyze patient data quickly (Ramakrishnan, 2023).

Impacts of AI

Technology is a world within itself, and any advancements made in one area benefit the other facets. Artificial Intelligence has been a huge supporting factor to the possibilities created in cyber security. Organizations are realizing how crucial cyber security measures are in preventing data breaches, which result in fines or expenses. AI helps in threat hunting and identifying new cyber attack techniques that otherwise wouldn't be found through traditional methods (Segal, n.d.). By combining both traditional and AI methods, companies have found a 100% detection rate and minimized their false positives. Organizations have adopted data anonymization to further protect personal information by removing identifiable markers. Usually this is done to minimize risk when data is moving from one location to another. Names, phone numbers, and passwords are considered Personal Identifiable Information (PII). This is a great practice when considering AI systems interacting with personal data to perform tasks. An example of this is fraud detection, AI systems must run risk analysis on activity and compare it to PII and past behaviors of the consumer. This also involves a level of surveillance and tracking that requires a substantial amount of monitoring and data collection (ThinkML Team, 2023).

AI and Business Strategies

Through automation, decision making, and cybersecurity, AI benefits a company's productivity levels and production. This also helps organizations garner insights that aid in marketing decisions and business strategies. In the regards of business strategies, AI analyzes and interprets massive amounts of data to find patterns to help companies make well informed decisions. This can help during marketing campaigns and advertising by revealing more of what a company's audience wants to see. By identifying and understanding customers' needs, companies can create personalized content to advertise to their consumers. The benefits can range from better budget apportions and resource allocation to heightened customer engagement and relations (Leonard, 2023). AI can improve customer satisfaction through customer service chatbots. This allows customers to get a personal experience and companies can exert more staff and energy towards other areas.

References

Anand, A. (2023, January 6). How can you improve customer's experience using IOT?

Analytics Steps. Retrieved April 16, 2023, from https://www.analyticssteps.com/blogs/how-can-you-improve-customers-experience-using-iot

Apache Hadoop: What is it and how can you use it? Databricks. (n.d.). Retrieved April 16,

2023, from https://www.databricks.com/glossary/hadoop#:~:text=Apache%20Hadoop%20is%20an%20open,can%20be%20run%20in%20parallel

Focaloid Technologies Private Limited. (2022, November 3). How artificial intelligence and

machine learning changing IOT. How is Artificial Intelligence and Machine Learning Changing IoT: The Future of Technology? Retrieved April 16, 2023, from https://www.focaloid.com/blog/artificial-intelligence-and-machine-learning/

Leonard, J. (2023, February 21). How artificial intelligence increases business productivity.

How Artificial Intelligence Increases Business Productivity. Retrieved April 16, 2023, from https://www.business2community.com/tech-gadgets/how-artificial-intelligence-increases-business-productivity-02059942

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April 16, 2023, from https://www.mytechmag.com/impacts-of-iot-on-society/

Pawlan, D. (2022, November 23). Relational vs. Non-Relational Database: Pros & Cons.

RSS. Retrieved April 16, 2023, from https://aloa.co/blog/relational-vs-non-relational-database-pros-cons

Ramakrishnan, M. (2023, January 24). Artificial Intelligence vs machine learning: 5 vital points

to know. Emeritus Online Courses. Retrieved April 16, 2023, from https://emeritus.org/blog/artificial-intelligence-vs-machine-learning/

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Sharma, R. (2023, January 24). What is tableau? why every data organization should care.

Emeritus Online Courses. Retrieved April 16, 2023, from https://emeritus.org/blog/data-analytics-what-is-tableau

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AI and Data Privacy: Unraveling the Complex Relationship. Retrieved April 16, 2023, from https://thinkml.ai/ai-and-data-privacy/#:~:text=Data%20anonymization%20is%20a%20process,sets%2C%20leading%20to%20privacy%20breaches