BUS610: Advanced Business Intelligence and Analytics

Data was stored in file-based systems before the advent of database systems which were developed/created because of the disadvantages of the file-based system. This included integrity problems, isolation of data, and security issues. How might a small agricultural business use a database system to gain a competitive advantage in the marketplace? Think about in what areas and how a database system would be used. For example, how could a homeowner implement a database to finesse their home management?

The main real-world datasets used in the studies analyzed for this paper were sensor data, image metadata, website publications, and electronic documents. Most of the studies analyzed did not document the specific languages they used to model their data or the tool they used. But due to the need to analyze large volumes of data with various structures, which arrive in high frequency, database research became more focused on NoSQL than relational databases. Why might a NoSQL vs. Relational approach be best for database management, according to growing trends captured in this review of research?

This article explores the various tools and technologies currently being leveraged (like Hadoop, which is useful for developing applications that can perform absolute statistical analysis on vast quantities of data) and the issues faced when using them (heterogeneity and timeliness, security, incompleteness and scalability of the data are the biggest obstacles when analyzing big data). What are some additional areas where big data utilization can grow? What needs to improve? What other technologies do you envision being used in collaboration with big data in the future, and in what ways?

This article lists the various computer information systems/storage types and how they work. This article includes definitions of various types of storage, from hard drives and flash memory, such as USB drives and solid state drives (memory cards), to optical discs and smart cards. We currently use smart cards more than this article suggests.

This paper explores what a Database Management System (DBMS) suited to the future may look like based on issues that can be seen today, as well as emerging trends and how this system may be created. An apt example includes a system that allows efficient and continuous querying and mining of data flows that can be employed on media with different computing capacities. What human-to-machine communication and interoperability do you think was most beneficial? Consider how, for example, an individual embedded medical device will be included in DBMS as processes get more complex and storage facilities become more distributed. What are some key aspects of DBMS that could benefit future architectures?

Businesses and institutions must collect and store temporal data for accountability and traceability. This paper highlights an approach to dealing with transaction lineage that considers how data can be stored based on timestamp granularities and methods for refreshing data warehouses with time-varying data via batch cycles. Identify three ways transaction lineage can be used and how this is relevant to temporal data. What industries do you think transaction lineage will always be relevant in? How?

This case study provides insight into how a data warehouse was built for a firm in the financial sector using its existing Microsoft technology. It touches on the current form of "static reports" currently used within the company, which we have identified as problematic. This case study showcases a step-by-step method of how this DW is built. After reading, you should understand the theory and practical application of the DW approach. How would you apply a similar framework to a large department store chain's supply chain?

"Good" hardware is integral for a data warehouse and its software to function efficiently, and the architect of the warehouse must be "hardware aware". As each hardware and software technology advances, so do data warehouses with the advent of, for example, new nonvolatile memory (NVM) and high-speed networks for base support. This article focuses on the need to develop and adopt new management and analysis methods.

Big data and business analytics methods for improved business decision-making, technological approaches, applications, and open research challenges. Big data has brought companies in developed countries many positive effects, which those in emerging and developing nations may replicate. However, big data's many challenges include data security, management, characteristics, compliance, and regulation. This paper contains a neatly wrapped breakdown outlining the structure, components, and tools that provide effective and efficient processing for the Hadoop ecosystem.

This toolkit was developed with the World Bank to teach and provide tools for entrepreneurs to collect data. Business analysis for tech hubs is difficult because the hubs simultaneously influence and are influenced by their local ecosystems. Areas in which tech hubs may benefit from business analytics include finding focus, sharing success with customers, and fundraising.

Imagine you are setting up a tech hub using the framework provided in this toolkit. Make a plan of how you would effectively collect the data. How would you decide what to measure? What resources will you need to effectively implement, monitor, and report the services your tech hub offers?

This article highlights use cases of ocean observation to explore how cloud computing can be improved to handle increased data flows. As the amount of data ingested increases, the cloud could replace traditional approaches to data warehousing. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations near the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Apply this structure in your industry regarding how to get data, store data, organize it, and conduct analysis and visualization in the cloud. What are some potential problems for large datasets? Think about how you would overcome those challenges. How would "sandboxes" provide some security when testing a system?

Cloud computing is useful in providing easy-to-access high-performance computing, networking, and storage via the net. Future work should be geared toward working on data science/AI/ML services to protect user data to make data more secure. What are the three service models of the cloud? How do they differ for the consumer? Identify, define, and provide some examples of issues with potential solutions you may have experienced with your social media accounts.