Question

Reading: Portfolio Analytics

The text identifies four broad areas in the context of investments, in which analytics is used:

1. Market analytics
2. Financial screening
3. Quantitative modeling, and
4. Financial Analytics.

Pick any of these four areas and research the internet to find companies that are using Advanced analytics and data science in that domain. Summarize how the firm is using data science and Analytics to achieve its objectives. How is it different than what they have done in the past. Is there any drawback to transitioning from the old to the new way?

Answer 1

Some of the companies that are using advanced analytics and data science under Quantitative modeling are

1. Capgemini
2. Siemens PLC
3. American Express
4. Gartner
5. Intuit etc.

Data science and data analytics have multiple impacts on a firm. These can be used for finding diverse things in a firm. It helps to achieve goals and objectives of firm by searching internet and digital advertising, getting artificial intelligence and helps in machine learning. While the firm uses both system that allows them to use computers to shift via large amount of data using algorithms to get connections and it mostly help them to reach their goals and play a huge role in business running for future.

Firms and companies can extract insights and information from data through data science and analytics which can lead to better decisions and strategic business moves as well as companies can authenticate and contradict existing theories or models.

Firms and companies had excessive data which aren’t in right lineage and didn’t had allowable purpose to serve their customers however after using Quantitative modeling companies can make good progress. They can centralize capabilities and democratize its uses. Firms had also faced poor data quality reducing analytics in organizations.

Firms or companies can face difficulties while transitioning to use quantitative modelling. Companies shouldn’t just dive in without a plan before using data science and analytics, resulting expensive ongoing maintenance. When the company implements Quantitative modeling for the first time they faces error messages and hence they need to involve in a steep learning curve. They also make common mistakes while implementing Quantitative modeling for the first time.