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Examples of Business Analytics in Action | HBS Online
Source: online.hbs.edu
Article summary:
1. Business analytics is the use of math and statistics to collect, analyze, and interpret data to make better business decisions. It includes descriptive, predictive, diagnostic, and prescriptive analytics.
2. Microsoft used workplace analytics to improve productivity and collaboration by relocating employees closer together, resulting in a decrease in meeting travel time and increased operational efficiency.
3. Uber implemented a tool called Customer Obsession Ticket Assistant (COTA) using machine learning and natural language processing to improve customer support. A/B testing showed that the tool led to faster service and more accurate resolution recommendations.
Article analysis:
The article titled "Examples of Business Analytics in Action" provides examples of how companies are using data analytics to make informed business decisions. While the article highlights the benefits and successes of these companies, it lacks a critical analysis of potential biases, unsupported claims, missing evidence, and unexplored counterarguments.
One potential bias in the article is its focus on the positive outcomes of using business analytics. The examples provided all showcase successful implementations of data analytics, without mentioning any potential challenges or failures. This one-sided reporting may give readers a skewed perspective on the effectiveness and limitations of business analytics.
Additionally, the article lacks evidence to support some of its claims. For example, it states that "an increasing share of organizations report using analytics to generate growth," referencing a survey by McKinsey. However, no specific data or statistics from the survey are provided to back up this claim. Without this evidence, readers are left to take this statement at face value without knowing the extent or validity of the survey results.
Furthermore, the article does not explore potential risks or drawbacks associated with business analytics. While it highlights the benefits and successes of companies like Microsoft and Uber, it fails to mention any potential privacy concerns or ethical considerations that may arise when collecting and analyzing large amounts of customer data. This omission leaves out an important aspect of the discussion surrounding business analytics.
The article also contains promotional content for Harvard Business School's online course on Business Analytics. While it is understandable that HBS would want to promote its own courses, this promotional content should be clearly labeled as such to avoid misleading readers into thinking they are reading an unbiased analysis.
Overall, while the article provides interesting examples of how companies are using business analytics, it falls short in providing a critical analysis of potential biases, unsupported claims, missing evidence, unexplored counterarguments, and promotional content. Readers should approach this article with caution and seek additional sources for a more comprehensive understanding of the topic.