INFORMATION SYSTEMS & BIG DATA

Blog No. 1

Introduction

This blog is the first of a two (2) part blog series about Information Systems and the role that it plays in today’s businesses. Briefly outlining what information systems are and how its usage has grown in day-to-day business operations. Some of the wider areas included in information systems are artificial intelligence, robotics automation and social media marketing, etc. However, in this blog, the focus will be on information systems and big data.

What is the meaning of Information Systems?

Information Systems (IS) is the use of multiple pieces of technology for collecting, processing, storing and dispersing information. Businesses use information systems to interact with their customers, suppliers, and business partners. Some examples of information systems are networks, databases, smartphones, and computers. Some ways they use these devices range from interacting with the digital marketplace, evaluating profitability, managing the supply chain response to client demands, improving financial management, and delivering essential and timely information to banks, partners, investors, and other stakeholders. Business information systems provide managers with the information they need to function efficiently and successfully. Beyond basic data, information systems can tailor information to the needs of users, facilitating decision-making and action planning.

Types of Information Systems

They are several types of information systems in business.

· Decision Support Systems.

· Operations Support Systems.

· Transaction Processing Systems.

· Management Information Systems.

· Executive Information Systems.

Information systems come in a variety of shapes and sizes, each serving a specific purpose. Financial transactions are transformed into useful information by an operations support tool like a transaction processing system. Similar to a management information system, a database can be used to generate reports that can be used by users and enterprises to make better decisions.

In a decision support system, data is gathered from multiple sources, examined by managers, and then used to inform decisions. To quickly obtain customized strategic information in summary form that can be evaluated in more detail, users of an executive information system are beneficial for analyzing business trends.

Let’s look at Big Data Analytics.

What is Big Data?

Big Data is a term used to describe vast collections of various types of data that are continuously produced at a rapid rate and in enormous volumes, including structured, unstructured, and semi-structured data. Today, a rising number of businesses use this data to gain insightful understanding and enhance their decision-making, but they cannot store and analyze it using conventional data storage and processing devices.

Big Data Analytics

Big Data analytics encompasses mining enormous databases for patterns, trends, and relationships that can’t be found using conventional data management methods and technologies.

The Traditional Approach: - Analytics often occur after a specific time period or occurrence. If you run an online business, you may look at the data that has accumulated over the course of a week or month and then analyze it. For instance, you figure out which products clients mostly purchased and which products received positive or negative reviews, etc.

Big Data: - Analytics is typically performed in real-time as data is generated, and insights are presented practically instantly. Assume you have a delivery service business with a fleet of 100 vehicles and require real-time information on their exact location and route delays.

Below is one example of how Big Data is used in the day-to-day function of a business.

Data Processing

Data processing is when data is collected and converted into usable information. This task is usually done by data analysts, data scientists, etc. Data processing must be correctly done so that the data doesn’t negatively affect the data that is outputted.

They are six (6) stages of data processing

1. Data Collection: - Data is collected from various sources (Data Lakes, Cloud, Data Warehouses) to name a few.

2. Data Preparation: - Data preparation better known as data cleansing is when the collected raw data is clean, organized and checked for any errors. This process eliminates bad data, redundant data, incomplete data, and incorrect data.

3. Data Input: - The cleaned data is then sent to the department or location where it can be processed.

4. Data Processing: - Data is processed using algorithms, artificial intelligence, or machine learning to examine patterns, trends, etc.

5. Data Output: - It is at this stage that the data is ready to be presented to management or other members of staff in the form of graphs, charts, videos, images, text etc., for decision making.

6. Data Storage: - This data is then stored to be used at a future date by the company when needed.

Conclusion

In conclusion, we discussed what information systems are and the various types of information systems processes used in business today. We looked at big data, describing what it is and how it is also used in today’s business and how vital of a role it plays by outlining and going through big data analytics and the data processing cycles. Although Information Systems and Big Data are broad topics, the intention of the blog is to help those clueless about these areas to gain a brief understanding of what they are and some of the ways they can be used in business today.

References

Bourgeois, D., 2014. Information systems for business and beyond. The Saylor Foundation.

Stair, R. and Reynolds, G., 2020. Principles of information systems. Cengage Learning.

Deng, Y., Han, Z., & Yan, J. (2022). Applications of big data in economic information analysis and decision-making under the background of wireless communication networks. Wireless Communications & Mobile Computing (Online), 2022 https://doi.org/10.1155/2022/7084969

Sann, R., Pei-Chun, L., Shu-Yi Liaw, & Chi-Ting, C. (2022). Predicting online complaining behavior in the hospitality industry: Application of big data analytics to online reviews. Sustainability, 14(3), 1800. https://doi.org/10.3390/su14031800

Siahaan, B. P., & Prasetio, E. A. (2022). Understanding customer insights through big data: Innovations in brand evaluation in the automotive industry. The Asian Journal of Technology Management, 15(1), 51-68. https://doi.org/10.12695/ajtm.2022.15.1.4

Sung-Un Park, Jung-Woo, J., Ahn, H., Yoon-Kwon, Y., & Wi-Young So. (2022). Big data analysis of the key attributes related to stress and mental health in korean taekwondo student athletes. Sustainability, 14(1), 477. https://doi.org/10.3390/su14010477