A Brief History of Business Analytics (Part 7 of 8)
2014— Business Analytics (BA)
Previous: A Brief History of Business Analytics (Part 6 of 8)
Contents
- Part 1: Introduction
- Part 2: 1749 — In the Beginning, there was Statistics
- Part 3: 1937 — Operations Research (OR) / Management Science (MS)
- Part 4: 1968 — Business Intelligence
- Part 5: Late 1960s — Decision Support Systems (DSS)
- Part 6: 2014 — Information Systems (IS)
- Part 7: 2014 — Business Analytics (BA)
- Part 8: Summary
Modern Business Analytics (BA) can be viewed as an integration of Statistics, BI/IS, and OR/MS as illustrated in Figure 3. While the core topics are traditional and have been used for decades, the uniqueness lies in their intersections (Evans, 2014).
Data Mining is focused on understanding characteristics and patterns among variables in large databases using a variety of statistical tools. Many standard statistical tools as well as more advanced ones are used extensively in data mining. Data mining involves six common classes of tasks (Wikipedia, 2018d):
- Anomaly detection (outlier/change/deviation detection) — The identification of unusual data records, that might be interesting or data errors that require further investigation.
- Association rule learning (dependency modelling) — Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
- Clustering — Task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data.
- Classification — The task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as “legitimate” or as “spam”.
- Regression — Attempts to find a function which models the data, with the least error, for estimating the relationships among data or datasets.
- Summarization — providing a more compact representation of the data set, including visualization and report generation.
Simulation and Risk analysis relies on spreadsheet models and statistical analysis to examine the impacts of uncertainty in the estimates and their potential interaction with one another on the output variable of interest. Spreadsheets and formal models allow one to manipulate data to perform what-if analysis — how specific combinations of inputs that reflect key assumptions will affect model outputs.
What-if analysis is assesses the sensitivity of optimization models to changes in data inputs and provide better insight for making good decisions.
Perhaps the most useful component of business analytics, which makes it truly unique, is the center of Figure 3 — Visualization. Visualizing data and results of analyses provide a way of easily communicating data at all levels of a business and can reveal surprising patterns and relationships (Evans, 2014).
About Dr. Alvin Ang
Dr. Alvin Ang earned his Ph.D., Masters and Bachelor degrees from NTU, Singapore.
Previously he was a Principal Consultant (Data Science) as well as an Assistant Professor. He was also 8 years SUSS adjunct lecturer. His focus and interest is in the area of real world data science. Though an operational researcher by study, his passion for practical applications outweigh his academic background
He is a scientist, entrepreneur, as well as a personal/business advisor. More about him at www.AlvinAng.sg.
