What is Data Science? Definition, Application, and Examples.
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| What is Data Science? Definition, Application, and Examples. |
What is Data Science? Definition, Application, and Examples.
Data science is a discipline that encompasses several specializations. The function of data science is to extract knowledge from data. The so-called data science generates information from large datasets to derive recommendations for business management. These recommendations aim to improve the quality of business decisions and the efficiency of workflows.
What is Data Science?
Data Science, by definition, is the intersection of the scientific fields of mathematics, computer science, and industry-specific expertise. The field of data science involves:
- Analyzing (large) datasets,
- Identifying anomalies in the data,
- Predicting future events.
Individuals working in the field of data science are referred to as data scientists or data scientists.
Now that the definition is clear, let's look at what Data Science is meant to achieve.
What is the goal of Data Science?
The goal of Data Science is to enhance decision-making within a company, such as optimizing costs or revenue, using large datasets. Often, decisions are intended to be made automatically by machine learning algorithms (process automation). The following table provides an overview of the objectives of data science.
The main goals of data science are as follows:
- Filtering information: Analyzing the data set to reveal valuable information.
- Providing recommendations: The analysis of the data set should lead to providing recommendations.
- Improved decision-making: Data analysis and recommendations should serve as a basis for improved decision-making.
- Automation and optimization: Processes in the company are automated and optimized through data analysis.
To achieve the best results for your company, it is recommended to establish a data science team.
Which companies or areas benefit from the application of data science?
In which areas is data science applied?
Data science can be applied in all areas of a company, so there are almost no limits. Therefore, data science plays a crucial role wherever:
- There is a large volume of data.
- Different objectives for the company, such as making forecasts, need to be achieved based on these data sets.
However, there are typical areas where data science is more prevalent. These include online commerce or e-commerce, logistics, as well as healthcare, finance, industry, and production.
In addition to industries, applications can also be determined based on the nature of the activity. The following activities are worth mentioning:
- Exploratory data analysis: Analyzing and interpreting existing data to support hypotheses.
- Prediction of probabilities: Using data science and related techniques to calculate and predict probabilities, such as purchase or churn probabilities.
- Prediction of numerical values: Through historical calculations, it is possible to make future predictions, such as future power consumption or revenues.
- Anomaly detection: Data science provides the capability to identify irregularities and anomalies within the data.
- Text and language analysis: Natural Language Processing (NLP) is a way to analyze existing texts and spoken language.
- Image and video analysis: Image recognition, classification, etc.
- Identifying relationships and groups: Within vast amounts of data (Big Data), one of the tasks of data science is to identify relationships and groups within the respective datasets.
The number of activities that utilize data science is also very complex. To make them easier to understand, let's look at various industry examples in the next section. Different examples are described for each industry.
What are some practical examples of data science?
Today, large amounts of data are being analyzed using the tools and methods of data science in companies across all industries.
Marketing:
Data science methods are primarily used for personalization in marketing, analyzing large datasets to improve decision-making for marketing strategies. Historical data on customer transactions, behavior, and demographics are analyzed to derive marketing recommendations.
Practical example in marketing:
An e-commerce company offers various products in its online shop. Due to the diversity of products and the number of customers, the marketing team finds it difficult to decide which customers should be presented with which products.
To increase sales, the company utilizes techniques based on data science. The evaluation and analysis of historical customer data and their purchasing behavior are used with the help of machine learning algorithms to better predict future product preferences of customers. These targeted recommendations lead to significant increases in average order value (AOV) and conversion rate.
information technology Security:
Data science is increasingly used for monitoring information technology systems. Critical information technology systems are protected using Security Information and Event Management (SIEM).
Mobility:
In the field of mobility, autonomous driving is being further advanced thanks to data science and machine learning. Sensor data is primarily analyzed to generate precise information about the vehicle and its surroundings.
Retail and trade companies benefit from data science through analyses of customer purchasing behavior. Examining potential causes for returns helps in reducing product returns.
A practical example in e-commerce:
An e-commerce company experiences significant losses each year due to processing returns. By analyzing customer purchasing behavior through data science, several causes for product returns were identified, and corresponding measures were implemented.
In the healthcare sector (medicine and pharmacy), data science enables the creation of similarity analyses as a basis for personalized patient treatment and medication optimization.
Logistics companies improve their work processes and the quality of their transportation services using data science.
Practical example in logistics:
Planning and organizing routes and personnel pose a particular challenge for logistics companies. Not only do personnel costs need to be considered in the planning process, but also travel time, potential weather conditions, request volumes, and more.
By examining data from past trips and orders, as well as other internal and external company data, and analyzing it using data science techniques, future transportation processes can be more thoughtfully planned.
Industrial companies control and optimize manufacturing processes through the use of data science.
Insurance and banking industries leverage the potential of their available external and internal data using data science to improve their products and increase sales success.
Where is data science applied in practice?
Data science and machine learning are already present everywhere and are shaping our daily lives, often without us even realizing it. Artificial intelligence is encountered in various areas of our daily lives.
Virtual assistants:
The use of mobile devices is made easier every day thanks to voice-controlled assistants like Alexa (Amazon), Siri (Apple), and Google Assistant. These systems largely operate based on natural language processing, which enables them to understand and process human language.
Chatbots:
Routine tasks within customer service departments can be significantly reduced with the help of chatbots. This saved time can be utilized by employees for other tasks, such as providing individual customer support.
Large Language Models:
Large language models like ChatGPT facilitate human communication.
Recommendation systems:
Recommendation systems offer customers a personalized selection based on their individual preferences.
Customer Relationship Management (CRM):
Artificial intelligence is well-suited for CRM, enabling the efficient allocation of marketing budgets based on predicted customer values and product affinities.
Facial recognition:
In the field of image recognition, artificial intelligence enables the identification and tagging of faces within social networks and images.
Fraud detection:
Artificial intelligence is currently used by financial service providers, banks, and other industries for fraud detection as part of crime prevention efforts.
E-commerce:
AI-powered prediction of customer lifetime value (CLV) provides individual CLV forecasts for each customer. This helps, for example, in distributing discount coupons or high-quality flyers.
Autonomous driving:
Maintaining safe distances, parking, and staying in the lane are already possible thanks to intelligent systems. However, this is just the beginning. Pioneering global company Tesla has made it a goal to bring fully autonomous vehicles to the market shortly.
Supermarket:
Amazon Go is one of the most well-known examples in the field of grocery retail. Amazon Go aims to drastically improve efficiency and the customer experience during shopping.
Conclusion:
Data science is an interdisciplinary approach and the intersection of mathematics, computer science, and industry-specific domain knowledge. The potential of data science is enormous because it can be utilized in almost every business area and industry to perform process optimizations. The prerequisites for this are the right data, resources, IT systems, and employees.
If you have any questions on this topic or want to use data science profitably in your company, book a non-binding appointment.
Without data science consulting, errors can creep into the implementation of data science projects. Learn more here.
FAQ - Frequently Asked Questions:
What is Data Science?
Data science is the combination of several areas of expertise (mathematics, computer science, and industry-specific domain knowledge). The goal of data science is to acquire as much knowledge as possible from data to create advantages for the company.
Where can data science be applied?
Data science is mainly applied where large amounts of data are available. For example, in e-commerce, data science can be used for both B2B and B2C to personalize customer communication.
How is a data science project implemented?
Projects involving data science technologies are always implemented in consultation with the respective company. This is important because each company has individual processes and needs, requiring a customized approach. Next, suitable tools are determined, and the method is chosen.