Big Data Analytics Correlation Taxonomy
Abstract
:1. Introduction
2. Background
- Healthcare: clinical decision support systems, individual analytics applied for patient profiling, personalised medicine, performance-based pricing for personnel, analysis of disease patterns and improvement of public health.
- Public sector: creating transparency with accessible related data, discovering needs, improving performance, customisation of actions for suitable products and services, decision-making with automated systems to decrease risks, innovating new products and services.
- Retail: in-store behaviour analysis, variety and price optimisation, product placement design, improve performance, labour inputs, optimisation, distribution and logistics optimisation, Web-based markets.
- Manufacturing: improved demand forecasting, supply chain planning, sales support, developing production operations, web-search-based applications.
- Personal location data: smart routing, geo-targeted advertising or emergency response, urban planning, new business models.
3. A Narrative Prospective for Big Data Analytics Methods
- Descriptive analytics looks at data and analyses past events for insight as to how to approach the future; it asks, “what has happened?” An example is to categorise customers by their product preferences and life stage.
- Diagnostic analytics at this stage, historical data can be measured against other data to answer the question of “why it happened?” providing a possible way to find out dependencies and to identify patterns. For example, a retailer filters the sales down to subcategories. Companies employ diagnostic analytics, as it gives them in-depth insights into a particular problem. At the same time, a company should have detailed information at their disposal; otherwise, data collection may turn out to be individual for every issue and therefore time-consuming.
- Predictive analytics turns data into valuable and actionable information; in other words predictive analytics determines the probable future outcome of an event or a likelihood of a situation occurring; i.e., “what will happen?” For example, for an organisation that offers multiple products, predictive analytics can help analyse customers’ spending, usage and other behavior, leading to efficient cross sales, or selling additional products to current customers. This directly leads to higher profitability per customer and stronger customer relationships.
- Prescriptive analytics automatically synthesises big data, mathematical sciences, business rules, and machine learning to make predictions and then suggests decision options to take advantage of the predictions, asking “how to make the best of it?” An example is to determination of the best pricing and advertising strategy to maximise revenue.
4. Research Methodology
4.1. Stages of Big Data Analytical Methods
4.2. Correlation between Big Data Analytical Methods and Techniques
5. Big Data Analytics Correlation Taxonomy
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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DSRM Activity | Research Application |
---|---|
Identify problem and motivate | The existing problem was identified from existing literature and previous research projects. The research problem identified was the lack of realising the actual correction between big data analytic methods and its associated techniques based on a known approach. |
Define objectives of a solution | To develop a structural model combining analytic methods used for big data and its recommended techniques based on research approaches that are compatible for various sectors. |
Design and development | Several phases were required to fully develop a taxonomy aimed at formatting and structuring big data analytics techniques according to research approaches. The full understanding of each phase evolves and improves the taxonomy concept. The taxonomy was reviewed against big data analytics techniques (and methods) creating finer taxonomy. |
Demonstration and evaluation | The demonstration and evaluation formed the basis for the design phase of the following iteration. For the future a final evaluation scenario will be used in order to raise the discussion on the importance and limitation of the presented correlation taxonomy in this research. |
Communication | Research published in academic papers. |
BDA Method | BDA Techniques |
---|---|
Descriptive and Exploratory | Mathematical Calculation Visualisation |
Predictive | Machine Learning |
Linear and Non-Linear Regression | |
Classification | |
Data Mining | |
Text Analytics | |
Bayesian Methods | |
Simulation | |
Prescriptive | Stochastic Models of Uncertainty |
Mathematical Optimization Under Uncertainty | |
Optimal Solutions |
Big Data Analytic Method | Sample of Techniques | Visualisation Software | Research |
---|---|---|---|
Descriptive | Data collection | ExcelPro SCADA | [35,36] |
Diagnostic | Clustering techniques | MATLAB | [37] |
Predictive | Modelling classification | Excel, and Simulink | [38,39,40] |
Prescriptive | Optimal solutions | OPNET | [41] |
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Husamaldin, L.; Saeed, N. Big Data Analytics Correlation Taxonomy. Information 2020, 11, 17. https://doi.org/10.3390/info11010017
Husamaldin L, Saeed N. Big Data Analytics Correlation Taxonomy. Information. 2020; 11(1):17. https://doi.org/10.3390/info11010017
Chicago/Turabian StyleHusamaldin, Laden, and Nagham Saeed. 2020. "Big Data Analytics Correlation Taxonomy" Information 11, no. 1: 17. https://doi.org/10.3390/info11010017