Statistical data is a crucial part of any business domain. When leveraged appropriately, this information can significantly improve the quality of business diligence. Data science that heavily relies on statistical data helps to draw the most valuable business insights hidden inside this information. In this article, SOLVVE will guide you through the most popular cases of data science applications in business and everyday life.
Practical applications of data science
Credit scoring in banking
Credit scoring systems are integral to all banking systems especially those providing loan services. Such scoring allows assessing the reliability of a potential client with high precision. This system significantly influences the decision-making process when it comes to granting or declining loans.
Client assessment, also known in the business domain as the client segmentation, is based on the hazard curves. They allow for flexible control of the interest rates for loan products leading to better returns on investment.
Business development forecasting
Like any other business both banking and other domains that we will discuss below depend on the understanding of the development dynamics. The key element of it is the so-called time series forecasting. It accurately shows the dynamics of business for both short- and long-term predictions. This equips businesses with the flexibility to adjust the strategy fast enough to stay one step ahead of the competitors.
Data modeling to track brand development
When you introduce a new brand to the market you would definitely like to see the estimates of its development for at least the nearest future. However, during the launch, you do not have any historical data about the brand to rely on when drawing the estimates. In such a case, it is beneficial to use data modeling. It can be based on the history of “parent” brands.
This historical data can be adjusted over time as the new data from the actual business flows in. One of the most recommended approaches for such data modeling is the Bayesian method. It allows for flexible probabilities navigation when there is a lack of data.
Recommender systems in social networks
We live in times of social media and it is obvious that not only the information about the client (a person, a business, etc.) but also its connections are important. It is a powerful source of information for client base growth and promotion.
The key approach is social network analysis. It builds flexible recommendations and offers a client highly relevant information based on the historical experience of the clients that showed similar behavior patterns.
Unlike in any other domain, it is hard to overestimate the cruciality of the correct decision making during the diagnostics in healthcare. In terms of data science, we can talk about the exclusion of the type 1 error also known as a “false positive”. Currently, in order to achieve this goal data science relies on the methods of statistical analysis.
For example, one can diagnose anomalies with high accuracy based on the blood sample analysis. Inspection of ultrasonography videos allows to detect anomalies and prevent abnormalities in fetal development. Analysis of the DNA chains helps to identify genetic diseases.
The key specificity of data science application in healthcare is the possibility to use the global experience in medical diagnostics. Data science uses experience accumulated by prominent institutions as a unified body of knowledge. Already today they are successfully put into practice.
In the eSports domain, graph analysis leads the way as it provides 360-degree coverage for tournament organisers. Graph representation of information is the basis for a powerful recommender system discussed above that increases players’ engagement through promotion of news content or content from event organisers, the search of a team, following popular players, and many more.
As we have seen in this article, data science has penetrated our lives through different applications. One does not have to be deeply involved in math, statistics, or any other scientific field to get in touch with and benefit from the achievements of data science.
If you have any questions or ideas related to data science applications in your business projects, do not hesitate to contact us. Let us make this happen!