Computer Information Systems: Big Data and the Internet of Things

Businesses spend trillions of dollars every year on computing and information systems. Banking & Securities companies spend the most on IT followed closely by manufacturing and natural resources, government and communications media services according to Gartner. Companies are looking to data solutions to get a competitive advantage in their industry. This surge is fueled by big data and analytics tools. What does this mean for you? It means an increased need for computer technicians and help desk specialist to keep the infrastructure running smoothly.

What is Big Data?

Big data involves data sets so large and complex that traditional processing application software is inadequate to capture, curate, manage, and process the data within a reasonable amount of time. Big data can be used for predictive and user behavior analytics. Understanding the trends in a company’s industry can give them a competitive advantage and allow them to get first to market with any idea or product.

The challenges of big data can be measured by increasing amounts of data, the speed of data, and the range of data types and sources. Big data can be measured by the five Vs; characteristics that include volume, variety, velocity, variability and veracity.

  • Volume – the total amount of stored data. This can range from terabytes to exabytes of data.
  • Variety – the range of data types and sources.
  • Velocity – the speed that the data is generated and processed.
  • Variability – the lack of consistency among data gathered and processed.
  • Veracity – data quality for accurate analysis

One growing source for big data is the Internet of Things.

What is the Internet of Things (IoT)?

The Internet of Things is the network of devices, vehicles, home appliances and any item with network connectivity allowing it to exchange data. The Internet of Things is all around us now. From your cell phone to the refrigerator in your kitchen. Even toasters are connected to the Internet. Big data and the Internet of Things work together hand in hand. Data can be extracted from the Internet of Things to understand buyer behavior, track user trends and create a mapping of devices interconnectivity.

Techniques in Big Data

There are many different techniques to analyze big data, they include A/B testing, machine learning, natural language processing and data mining.

A/B Testing – a controlled experiment with two variants. Both variants are measured until a statistically significant finding can be made. Then a new variant is introduced to compete against the winner. The goal is continuous improvement.

Machine Learning – the ability of computers to learn without being explicitly programmed. Machine learning explores the construction of algorithms that can learn from and make predictions on data. Machine learning makes data-driven predictions rather than strictly following program instructions.

Natural Language Processing – the ability of a computer program to understand human language as it is spoken.

Data Mining – the process of discovering patterns in large data sets using understandable structure for later use.

Technologies in Big Data

There are many different technologies used to analyze big data, they include business intelligence, cloud computing, predictive analytics, stream processing, in-memory data fabric and distributed file systems.

Business Intelligence – strategies and technologies used for the data analysis of business information. One of the technologies used for business intelligence analysis is data warehousing, A data warehouse is a relational database used for reporting and data analysis and typically includes historical transactional data.

Cloud Computing – the delivery of computing services over the Internet using remote servers, storage, databases, networking, software and analytics.

Predictive Analytics – software or hardware that allows a company to discover, evaluate, optimize and deploy predictive models by analyzing big data sources.

Stream Processing – analyzes and performs actions on real-time data through the use of continuous queries.

In-Memory Data Fabric – the processing of large data sets by distributing the data across the memory of a distributed computer system.

Distributed File Systems – a computer network that stores data on more than one node for redundancy and performance.

Does learning about Big Data and the Internet of Things interest you? Are you looking for a rewarding career at a company that provides computer support to its employees? The Computer Information Systems/Business Administration diploma program, at Gwinnett College’s Lilburn, GA campus, is designed to train the college graduate to seek an entry-level career in office management utilizing accounting and computer information systems.

Computer Information Systems/Business Administration graduates from this program can also transfer their school credits directly into the Associate of Science Degree in Business, Computer Information concentration, program. They will need to complete four additional courses to obtain their associate degree.

Contact us today to learn more about becoming a computer help desk specialist or computer technician.