What is Data Science?

Data scientists specialize in uncovering valuable, industry focused insights from data. Their expertise goes beyond that of traditional business analysts or data analysts, combining advanced knowledge of computer science and quantitative methods with a strong grasp of the specific domain they operate in whether it’s automotive manufacturing, eCommerce, healthcare, or another sector.

A data scientist must be able to demonstrate a diverse set of skills that combine business knowledge, technical expertise, and communication. The key responsibilities include:

These skills are in high demand, prompting many aspiring professionals to pursue data science through various pathways such as certification programs, specialized courses, or full academic degrees offered by universities and institutions.

It is important to note that data scientists are not solely responsible for every stage of the data science lifecycle. For instance, building and managing data pipelines is usually the role of data engineers, although data scientists often provide input on which types of data are most valuable or necessary. Similarly, while data scientists can design and train machine learning models, scaling these solutions efficiently often requires collaboration with machine learning engineers, who bring advanced software engineering expertise to optimize performance at scale.

The responsibilities of a data scientist also frequently intersect with those of a data analyst, especially in tasks such as exploratory data analysis and visualization. However, data scientists generally possess a broader skill set, leveraging programming languages like Python and R to perform advanced statistical inference, predictive modeling, and more complex data visualizations beyond the scope of traditional analysis.

It is common to confuse the terms data science and Business intelligence (BI) since both deal with analyzing organizational data, but they differ in their primary focus.

Business intelligence (BI) serves as a broad term for technologies and processes that enable data preparation, mining, management, and visualization. BI tools help end users uncover actionable insights from raw data, supporting data-driven decision-making across industries. While data science tools share similarities, BI is more focused on analyzing historical data, producing descriptive insights that explain what has already happened. BI typically works with structured, static (unchanging) data to inform organizational actions.

In contrast, data science goes beyond descriptive analysis by leveraging historical data to identify predictive variables, enabling tasks such as data categorization and forecasting.

Importantly, data science and BI are not mutually exclusive. Modern, digitally driven organizations often integrate both approaches to gain a comprehensive understanding and maximize the value of their data.

Data scientists rely on widely used programming languages to perform exploratory data analysis and statistical regression. These open-source tools provide built-in support for statistical modeling, machine learning , and visualization. Commonly used languages include the following (read more at "Python vs. R: What's the Difference?"):

To collaborate and share code effectively, many data scientists make use of platforms such as GitHub and Jupyter Notebooks.

For those who prefer working within a graphical interface, several enterprise-grade tools are available for statistical analysis. Two widely used options include:

Data scientists develop expertise in leveraging big data processing platforms such as Apache Spark, the open-source framework Apache Hadoop, and various NoSQL databases. They also work with a wide range of data visualization tools—from basic graphics features in presentation and spreadsheet software like Microsoft Excel, to specialized commercial platforms such as Tableau and IBM Cognos, as well as open-source solutions like D3.js (a JavaScript library for creating interactive visualizations) and RAW Graphs. For building machine learning models, they frequently rely on frameworks such as PyTorch, TensorFlow, MXNet, and Spark MLlib.

Because of the steep learning curve in data science, many organizations face challenges in hiring the right talent to maximize the value of their AI projects. To accelerate return on investment, companies are increasingly adopting multipersona data science and machine learning (DSML) platforms, which has also led to the rise of the citizen data scientist.

These multipersona DSML platforms incorporate automation, self-service portals, and low-code/no-code interfaces, enabling individuals with limited technical or data science expertise to generate business value using data science and machine learning . At the same time, they provide advanced technical options for professional data scientists. By supporting both groups, multipersona platforms foster collaboration across the enterprise and expand the impact of data-driven initiatives.

Cloud computing enhances data science by offering scalable access to processing power, storage, and specialized tools essential for complex projects.

Because data science often involves working with massive datasets, scalability is critical—especially in time-sensitive scenarios. Cloud-based storage solutions, such as data lakes, simplify the ingestion and processing of large data volumes. These systems provide flexibility, enabling users to deploy large clusters when required and add incremental compute nodes to accelerate workloads. This scalability allows organizations to balance short-term resource demands with long-term business objectives. Additionally, cloud platforms typically offer diverse pricing models—such as pay-per-use or subscription plans—making them suitable for both large enterprises and small startups.

Open-source technologies also play a central role in data science workflows. When hosted on the cloud, they eliminate the need for local installation, configuration, maintenance, and updates. Many cloud providers, including IBM Cloud®, further support this ecosystem by delivering prebuilt toolkits. These solutions allow data scientists to develop models with minimal or no coding, thereby broadening access to advanced technologies and actionable insights.

Enterprises can gain significant advantages from data science, with applications ranging from process optimization through intelligent automation to advanced targeting and personalization that elevate the customer experience (CX). Beyond these broad benefits, there are several concrete use cases where data science and artificial intelligence are delivering measurable impact:

Banking and Financial Services: An international bank accelerated loan processing through a mobile application powered by machine learning based credit risk models. The solution leverages a secure and robust hybrid cloud architecture to deliver faster, more reliable services.

Automotive and Electronics: A leading electronics company is designing high-performance 3D-printed sensors to support the next generation of autonomous vehicles. By applying data science and advanced analytics, the firm enhances real-time object detection capabilities critical for safe navigation.

Robotic Process Automation (RPA): An RPA provider developed a cognitive business process mining solution that reduces incident handling times by 15% to 95% for client organizations. Trained on customer email content and sentiment, the system enables service teams to prioritize the most urgent and relevant cases.

Media and Entertainment: A digital media technology company launched an audience analytics platform that helps clients understand viewer engagement across an expanding portfolio of digital channels. The solution applies deep analytics and machine learning to capture real-time insights into audience behavior.

Public Safety and Law Enforcement: An urban police department implemented statistical incident analysis tools to identify patterns of criminal activity. The data-driven platform generates dashboards and reports that guide resource deployment and strengthen situational awareness for field officers.

Healthcare and Life Sciences: Shanghai Changjiang Science and Technology Development partnered with IBM® Watson® to create an AI-based medical assessment platform. The solution analyzes patient records to categorize individuals by stroke risk and predict the likely success of different treatment strategies.