- Internal Code :
- Subject Code : MA609
- University : Melbourne Institute of Technology
- Subject Name : Business Analytics
Business Analytics and Data Intelligence - Answer 1
There are certain factors that define the quality of data as usefulness in accuracy, consistency; completeness etc. data must be qualified to be of usefulness. Thus data must satisfy the intended purpose.
The seven stages of data mining process are as below-
- Data cleaning- It is the stage of filtering the raw data as using such data can cause confusions. This step carries routine cleaning work as filling the missing data and removes the noisy data.
- Data Integration- When multiple and heterogeneous sources like database, data cubes and files are combined to analyze, the process then called as data integration. It can help improve the accuracy and speed in the mining process. Data integration can be performed with tools like Mi SQL and Oracle data service integrator.
- Data Reduction- This technique is used in the process of data analyzing from the data collected. The size at this stage has been reduced to much smaller while maintaining integrity of the data. Data reduction can be performed through methods such as decision trees, neural networks etc.
Some strategies that can be included in data reduction is reducing the number of attributes, replacing original data by smaller filtered forms and compression of data.
- Data Transformation- In this stage, data is changed into a suitable form of the data mining process. Data then is consolidated so that the mining process is efficient. It involves Data mapping and process of code generation. Some strategies included in data transformation are smoothing, aggregation, normalization and dicretisation.
- Data mining- This is the stage o0f identifying interesting patterns and information fro large amounts of data. In this stage, intelligent patterns are used to extract data. The data is represented in patterned form and clustering and classification are used in it.
- Pattern Evaluation- The criteria at this stage is interestingness, thus interesting patterns are identified using this criteria. Summarization and visualization methods are used in this sage.
- Knowledge Representation- This is a step where knowledge representation techniques and data visualization are used to represent the data. Reports and tables are formed at this stage.
Business Analytics and Data Intelligence - Answer 2
The big data revolution has given birth to different kinds, types and stages of data analysis. The key is to gain the right information which delivers knowledge that gives businesses the power to gain a competitive edge. The main goal of big data analytics is to help organizations make smarter decisions for better business outcomes.
Three Analytics Models
Descriptive analysis- This is the most basic form of analytics. It tells the retriever what actually has happened. This model analysis the data in real time and older data for the insight of how to react in the future, to find reasons of success of failure in the past. Majority of big data analytics use descriptive model of data analytics.
Predictive Analytics- Predictive analysis is a step of data reduction. Analyzing past data patterns and trends can accurately inform a business about what could happen in the future. This helps in setting realistic goals for the business, effective planning and restraining expectations. It tells us the about what can happen in the future based on past incidents. Organizations collect contextual data and relate it with other customer user behavior datasets and web server data to get real insights through predictive analytics.
Prescriptive Analytics- Prescriptive analytics is the next step of predictive analytics that adds the spice of manipulating the future. Prescriptive analytics advises on possible outcomes and results in actions that are likely to maximize key business metrics. It tells a business what should it do? It is based on optimization to achieve best outcomes and how to achieve them and uncertainty identification to make better decisions.
References for Business Analytics and Data Intelligence
Shahu, H., Sharma, S. & Gondhalakar, S. (n.d.). A Brief Overview on Data Mining Survey. International Journal of Computer Technology and Electronics Engineering 1(3).
Sivaraja, U., Kamal, M.M., Irani,Z. & Verrakkody, V. Critical analysis of big data challenges and analytical methods. Journal of Business Research 70.
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