Data Science

Data Science is a discipline that focuses on the strategies and techniques used to gain meaningful insights from large volumes of data.

What is a Data Scientist?

The best way to define data science is to consider what data science professionals do. A Data Scientist’s role can encompass many of the following functions:

• Selecting data sources for analysis to answer questions such as what happened and why.
• Applying algorithms, machine learning, and AI techniques to data sets to extract meaning from them.
• Analyzing data and interpreting the consequent results.
• Working with data engineers to design and optimize data pipelines.
• Extracting insights from the analysis that can be applied to a business problem.

How Does the Data Analyst Role Differ From a Data Scientist?

The Data Scientist role is a superset of a Data Analyst. Many Data Scientists begin their careers as Analysts who perform more mundane tasks, including collecting and normalizing data for ready analysis. Data Analysts solve business problems using data. A data scientist will use the same data to make predictions to support the business strategy function or explore data to uncover new opportunities.

Enabling the Data-Driven Enterprise

Data analytics helps a business make more informed decisions than opinion-based decisions. A good data scientist will infer and test various hypotheses before sharing opinions. Businesses are forward-looking, so having a science-based approach makes a big difference when evaluating the risks and potential rewards associated with launching new business initiatives, especially when justifying actions that need to be taken to senior management. It is much easier to predict future customer behavior when you have studied what they have done in the past.

Data science can help businesses understand what metrics to collect to improve future decision-making. It can also test decisions by simulating scenarios and predicting potential outcomes.

Examples of Data Science

Below are some use cases that illustrate the application of data science:

• In the Logistics industry, data science is used to predict the best delivery route for a driver to take to save both fuel and time.
• Credit rating agencies use it to support loan decisions by scoring loan applications. This process is used to ensure a balanced risk loan portfolio.
• Insurance carriers use data science for fraud detection and deciding premium levels when bidding for business on online insurance comparison sites. This process can include driving history data from existing customers, which they can use to encourage or discourage renewal.
• Online shopping sites apply data science AI algorithms to make product recommendations based on past purchases and recent online browsing history.
• Marketing automation systems use intent-based data to suggest the next steps in the engagement process for prospective customers and sales agents.
• Credit card companies use data science to detect potentially fraudulent activities and warn consumers by holding transactions in real time.
• In automotive production, the resource planning system can adapt to changing conditions by controlling parts bin replenishment based on constraints such as the number of available dock doors and the proximity of the trailer to the needed parts to an available door.
• Weather forecasting uses many variables and models to drive accurate predictions, including satellite imagery, historical seasonal trends and real-time sensor data.
• In pharmaceutical research, Machine Learning (ML) models test many alternatives when analyzing clinical trial results before recommending the most promising path for study.
• Farming relies on data science to manage crops using information gathered by satellite and drone-based photogrammetry.
• Law enforcement also uses it to analyze forensic evidence, crime predictions, and law enforcement staffing.

Actian and the Data Intelligence Platform

Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.

Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.