Question

Crime Prevention: Stop Human Trafficking

Human trafficking affects over 40 million people each year. It is a sad crime that reaches vulnerable populations, such as children. Many have a strong desire to see this crime stopped. One of those is Tom Sabo, who was very disturbed by this issue after attending a human trafficking symposium. He wanted to put his text mining skills into action.

Using text analytics and AI, he was able to create useful models that law enforcement could use. One model pulled together several text-based data sources: police reports, newspaper articles, recent prosecutions, and a shady classified advertising website. The goal was to find relevant patterns in the text that Sabo could then incorporate into a predictive analytics model.

Sabo used the police statements from a specific New York jurisdiction. He then linked this data to other events outside of the state and even outside of the country.

He saw these trends related not only to who was involved, but also, where the events were happening.

This model was subsequently used to identify these situations quicker and allowed law enforcement to act.

Questions:

1. Please identify three most typical BI related technologies used by Tom Sabo in this case study, and briefly discuss how they help Tom in data analysis. [6 marks]

2. As discussed in our lecture, the financial departments of many companies also do predictive analytics. Compare the analytics done by such financial departments and the one by Tom Sabo, what differences can you identify in terms of the technologies applied by them? [4 marks]

Answer 1

1) The three BI related technologies are following : -

i) Data Discovery

Data analysis for Tom, data discovery is among the leading business intelligence trends. For Tom Sabo data discovery was the process aimed to detect patterns and deriving insights in data through data analytics.

As Tom performed, data discovery process in the following three stages:

1. Data preparation stage where Tom Sabo connected to multiple data sources like newspaper,police report,prosecution and a shady classified advertising website.
2. Data visualization stage where Tom Sabo easily performs visual data analysis using data visualization dashboards containing informative charts and graphs.
3. Data analytics stage where Tom Sabo uses analytical skills to identify advanced patterns in the available data.

ii) AI and Machine Learning in Business Intelligence

Data-driven personalized services, 97% of industry experts see a major role for technologies like artificial intelligence and machine learning in marketing. At the same time, AI and machine learning can also be deployed in business strategies related to business intelligence and analytics.

AI and machine learning technologies where useful for Tom Sabo in detecting any anomalies or unexpected patterns in data analytics. For example, through advanced neural systems, AI algorithms can analyze historical data and accurately detect anomalies or unexpected events.

iii) Predictive Analytics & Reporting

Tom used AI algorithims in predictive analytics and reporting to understand the correct and linked meaning in his data.Bi possess the capabilities of determining future business trends from the current data patterns.

Their are lot of industries are combining predictive analytics and BI for a number of use cases. Here, Tom Sabo used the police statements from a specific New York jurisdiction.Where He saw these trends related not only to who was involved, but also, where the events were happening.This model was subsequently used to identify these situations quicker and allowed law enforcement to act.

2) The analysis done by the financial department are in following ways : -

i) Corporate Financial Analysis

In corporate finance, the analysis is conducted internally by the accounting department and shared with management in order to improve business decision making. This type of internal analysis may include ratios such as net present value (NPV) and internal rate of return (IRR) to find projects worth executing.

Many companies extend credit to their customers. As a result, the cash receipt from sales may be delayed for a period of time. For companies with large receivable balances, it is useful to track days sales outstanding (DSO), which helps the company identify the length of time it takes to turn a credit sale into cash. The average collection period is an important aspect in a company's overall cash conversion cycle.

A key area of corporate financial analysis involves extrapolating a company's past performance, such as net earnings or profit margin, into an estimate of the company's future performance. This type of historical trend analysis is beneficial to identify seasonal trends.

For example, retailers may see a drastic upswing in sales in the few months leading up to Christmas. This allows the business to forecast budgets and make decisions, such as necessary minimum inventory levels, based on past trends.

ii) Investment Financial Analysis

In investment finance, an analyst external to the company conducts an analysis for investment purposes. Analysts can either conduct a top-down or bottom-up investment approach. A top-down approach first looks for macroeconomic opportunities, such as high-performing sectors, and then drills down to find the best companies within that sector. From this point, they further analyze the stocks of specific companies to choose potentially successful ones as investments by looking last at a particular company's fundamentals.

A bottom-up approach, on the other hand, looks at a specific company and conducts similar ratio analysis to the ones used in corporate financial analysis, looking at past performance and expected future performance as investment indicators. Bottom-up investing forces investors to consider microeconomic factors first and foremost. These factors include a company's overall financial health, analysis of financial statements, the products and services offered, supply and demand, and other individual indicators of corporate performance over time.