MIS Report: Exploring Wearable Tech, Big Data, Mobile Strategy

This Management Information Systems (MIS) report analyzes several key technology trends. It begins by exploring the potential of wearable technologies, including their impact on productivity, payment processing, and employee satisfaction, while also acknowledging their limitations such as distrac...Read More

Management
Information
Systems
Name of the Student
Name of the University

Wearable Technologies and the Future
 Examples of smart wearable technologies include smart
jewelry, fitness trackers and smart clothing with in-built
technology
 Leads to better bottom line and higher productivity
 Ease of processing payments such as PayPal app in
Samsung Gear 2 Smart watch (Pothukuchi, Duffy & Sacks,
2020)
 Improve productivity in workplace by 8.5% and employee
satisfaction rate by 3.5%

Advantages and Disadvantages of
Wearable Technologies
 Advantages of wearable tech includes –
 Helps in increasing productivity
 Creating fit employees in the workplace
 Makes the most benefits from other tech investments
 Disadvantages of wearable tech includes –
 Creates distraction among users (Wang, Yang & Dong,
2017)
 The tech products are not cheap and not affordable
 Battery and size limitations prevailing

BI for Reporting and Data Analytics
 BI systems aims towards performing data visualization,
thus enhancing decision-making and data quality
 Offers ease of performing data analytics
 Streamlines the business processes (Gandomi & Haider,
2015)
 Helps in spotting business problems and identification of
market trends
 Identification and setting benchmarks for vast processes

Big Data and Impact by Spotify on
Customers
 Big Data discusses on the large and complex data sets
 Helps towards addressing wide number of business activities,
from analytics to customer experience (Matz & Netzer, 2017)
 Spotify has employed Seed Scientific, a data analytics
provider in 2015 (What Is Big Data? | Oracle, 2020)
 Use of Big Data help them towards creating hilariously specific
ads
 Data-driven marketing provides for unique messaging for
retaining clients

Big Data for reducing Crime Rates
 The New York Police Department (NYPD) has
implemented data-driven approach for predicting and
fighting against crime
 Predictive policing works by NYPD aims for regularly
recording of crime data using sophisticated algorithms
and models (Brayne, 2017)
 CompStat supports a dynamic approach for crime
reduction and improving personnel management

Ethical or Security Issues posed by Big
Data
 Data masking and anonymity can be made possible
 Informed consent while using Big Data in research
 Vulnerability towards fake generation of data
 Troubles raised from cryptographic protection (Salleh &
Janczewski, 2016)
 Mining of sensitive information

Mobile Technology and their
Importance
 Mobile technology refers to cellular communication
 The future of computer technology majorly rests on
wireless networking along with mobile computing
 Telecommunication applications includes video
conferencing, e-mail, instant messaging and others thus
proliferating the organizational growth (Ooi & Tan, 2016)
 They encompass multiple service providers, cable system
operators while also including software-based
applications for end-to-end communication services

Mobile Strategy used by Tech Giants
 Google - They primarily focus over winning customer
heart’s based on providing cheap mobile products. It
thus acts as a two-tiered sleeper cell (Hwang, Lai &
Wang, 2015)
 Apple – The company focuses on their UVP , from which
it focuses on their marketing strategy . They also focus
on social media, which further provides growth to their
market share
 Facebook – They focus on providing an unique
experience to customers, which cannot be experienced
on desktop (Farley et al., 2015)

Challenges from Internet and
Networking
The challenges that are posed with the impact of Internet
and Networking are discussed as:
 Poor network performance leads to degrading
performance users
 Security poses another challenges for users (Sood, Yu &
Xiang, 2015)
 Configuration management offers certain challenges to
network

Security Threats to Cloud Data
The threats to cloud data includes: Data Breaches, Data
loss due to low backup facilities, Insider threats, DDoS
attacks, Account hijacking and Insecure APIs.
The best advices for companies to protect cloud data are:
 Cloud provider should be chosen carefully
 Consideration of regulatory demands
 Identification of security gaps (Khan & Al-Yasiri, 2016)
 Utilization of file-level encryption

Who should hold responsibility for
Cloud Security
 Cloud security is considered as a team effort
 Cloud vendors should offer identity and access
management during providing service (Hendre & Joshi,
2015)
 Companies should also implement appropriate
certifications such as COBIT, SOC-2 and many others
 Cloud vendors should also offer exclusive features such
as vulnerability detection, application security
management and others

Security Control and Data
Management
 Encrypt every resources held within the company
Data Management forms a tremendous impact on
organization’s survival because:
 Leads to minimized errors
 Improvements in efficiency
 Improvements in data quality (Jaradat et al., 2015)
 Protection from Risks and Data Related Problems

References
Brayne, S. (2017). Big data surveillance: The case of policing. American sociological review, 82(5), 977-1008.
Farley, H., Murphy, A., Johnson, C., Carter, B., Lane, M., Midgley, W., ... & Koronios, A. (2015). How do
students use their mobile devices to support learning? A case study from an Australian regional
university. Journal of Interactive Media in Education, 2015(1).
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International
journal of information management, 35(2), 137-144.
Hendre, A., & Joshi, K. P. (2015, June). A semantic approach to cloud security and compliance. In 2015 IEEE
8th International Conference on Cloud Computing (pp. 1081-1084). IEEE.
Hwang, G. J., Lai, C. L., & Wang, S. Y. (2015). Seamless flipped learning: a mobile technology-enhanced
flipped classroom with effective learning strategies. Journal of computers in education, 2(4), 449-473.
Jaradat, M., Jarrah, M., Bousselham, A., Jararweh, Y., & Al-Ayyoub, M. (2015). The internet of energy: smart
sensor networks and big data management for smart grid.
Khan, N., & Al-Yasiri, A. (2016). Identifying cloud security threats to strengthen cloud computing adoption
framework. Procedia Computer Science, 94, 485-490.
Matz, S. C., & Netzer, O. (2017). Using big data as a window into consumers’ psychology. Current opinion in
behavioral sciences, 18, 7-12.
Ooi, K. B., & Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation using mobile users
to explore smartphone credit card. Expert Systems with Applications, 59, 33-46.
Pothukuchi, A., Duffy, S., & Sacks, J. (2020). How Wearable Technology Can (And Will) Change Your Business.
Retrieved 10 February 2020, from
https://www.salesforce.com/blog/2014/07/wearable-technology-business.html
Salleh, K. A., & Janczewski, L. (2016). Technological, organizational and environmental security and privacy
issues of big data: A literature review. Procedia Comput. Sci, 100, 19-28.
Sood, K., Yu, S., & Xiang, Y. (2015). Software-defined wireless networking opportunities and challenges for
Internet-of-Things: A review. IEEE Internet of Things Journal, 3(4), 453-463.
Wang, Z., Yang, Z., & Dong, T. (2017). A review of wearable technologies for elderly care that can accurately
track indoor position, recognize physical activities and monitor vital signs in real time. Sensors, 17(2), 341.
What Is Big Data? | Oracle. (2020). Retrieved 10 February 2020, from
https://www.oracle.com/big-data/guide/what-is-big-data.html