What is Data Science and why your company should invest in it

What is data science used for?

What is data science? Much more than a trend, this powerful combination of statistics, mathematics, programming, analytics, and artificial intelligence has revolutionized how organizations make strategic decisions.

In this article, you will understand what data science is, how this discipline evolved over time, how it differs from analytics and BI, and why it has become essential for companies that want to remain competitive in a data-driven market.

Explaining Data Science

What is data science and how did it originate? Formally, its origin dates back to 1962, when statistician John W. Tukey wrote articles defining the existence of a specific science whose object of study would be data analysis. A few decades later, American computer scientist Bill Cleveland named and defined this science, helping to modernize the field.

Today, understanding what data science is involves much more than statistics: it is a multidisciplinary field that combines programming, machine learning, artificial intelligence, and data engineering to transform large volumes of information into actionable insights, directly impacting business results.

Basically, what started as a branch of statistics ended up transforming over time, and with the growing need for data usage and the advancement of digital technologies, into something of even larger proportions. Currently, this science includes tools and processes based on programming, machine learning, artificial intelligence, data infrastructure, and modern analysis techniques to support the processing and storage of large volumes of information and processes.

Thinking in an organizational context, data science is a very broad term that encompasses various positions, tools, processes, and different technologies with the objective of raising and validating hypotheses to use levers capable of optimizing the business.

Data Science vs. Analytics vs. BI

These three terms often get mixed up, but each answers a different type of question:

Business Intelligence (BI) looks at the past and the present: dashboards, reports, and metrics that answer "what happened" and "what is happening right now." It is reactive by nature, and depends on structured and consolidated data.

Analytics goes a bit further, exploring data to understand patterns and answer more specific business questions, often combining different sources to reach an answer that a static dashboard cannot deliver.

Data Science is the broadest of the three, and the only one that builds predictive and prescriptive models: it not only describes what happened, but predicts what will happen and recommends what to do about it, using statistics, machine learning, and, increasingly, artificial intelligence.

In practice, the three complement each other within a mature data team. BI keeps the organization informed about the present, analytics investigates specific questions, and data science builds the models that anticipate the future. If you want to better understand how these roles split in practice within a team, it's worth reading What a data scientist does, which details the difference between a data scientist, data engineer, and data analyst.

Why Apply Data Science in Organizations?

According to the Harvard Business Review, every company is at some stage of a long journey to achieve greater data maturity. Although it is not a mission with a final destination, there are very stark differences in decision-making when comparing the starting point of this process to walking a few "steps" forward. Understanding what data science is and how to apply it correctly can be the differentiator between gut-feeling decisions and evidence-guided strategies.

Data within organizations can be used across several different fronts:

Products

Data science applied to a company's products can be used as the final product itself, to improve user interface of existing products, or even to support improvements in the development phase, based on the analysis of data generated throughout the process.

Operations

Applied to company operations, data science helps support the business by improving processes, assisting in the implementation of more accurate metrics, and guiding decision-making in a technological way focused on the real-world scenario of all departments.

Marketing

In marketing, data can be used to understand the best actions, guide campaigns, reduce communication errors, create email and social media automations, and gain a strategic understanding of who is engaging with your brand.

Sales

When we think about modern sales departments, most rely on data to guide strategies: whether to understand who the best persona is to pitch services and products, to target specific cities and points of sale, or even to measure the results of each team member. A sales department that knows how to leverage data is revolutionary for companies, especially when sales forecasting is applied to optimize operations.

Human Resources

There is still a misconception that HR departments run on "gut feeling" or purely administrative tasks, but there are exceptional opportunities to use data science here.

It is possible to map employee performance, gain more accurate insights into their experience, guide promotions, layoffs, predict turnover, scale bonuses, understand the impact of new benefits, and automate manual administrative tasks.

How to Apply Data Science in Organizations?

Applying Data Science within organizations helps bring more accurate information using data to support decision-making in all areas. When used properly, it drives impressive changes in the business and in how your brand relates to customers.

Examples of Data Science Applications:

Startups

This type of organization focuses on developing and launching one or more scalable products in the market, usually in a disruptive way, or bringing innovation to a segment through technology.

• Customer churn prediction
• Product optimization and customer journey flow
• Sales forecasting

Learn more about how to accelerate your startup's data-driven journey in Why your startup should start its data-driven journey now.

Retail

In retail, understanding the end customer, delivering increasingly personalized products and shopping experiences, and tracking sales metrics and logistics across the brand is critical to success. With data science, you can:

• Build faster and more efficient pipelines, enabling integrated tracking of sales, logistics, and management in a unified way
• Understand consumer preferences to optimize product mix, find the best ways to increase average order value (AOV), and gain better insights for product placement
• Understand the best ways to reach your target audience through more accurate, data-driven marketing and sales strategies based on purchasing behavior analysis

Entertainment

In this segment, user data can be collected and used to find the best combination of scenarios for writing more engaging scripts, offering personalized content recommendations, understanding trending topics, and making casting decisions based on audience popularity.

Additionally:

• Production of movies, series, and cultural projects: analyzing and mapping the best touchpoints to promote different types of content. A famous case study of using data science to create entertainment solutions is Netflix's development of the series House of Cards
• Understanding demand for products and solutions
• Understanding the best strategy for creating and offering products targeted to the audience, based on their popularity and preferences

Construction

Data in construction is being used more and more constantly, for all types of companies in this segment:

• Through data science, it is possible to understand construction timelines and anticipate project phases more efficiently
• Predict demand or budget for cheaper and more accurate construction materials
• Find the best land opportunities for development
• Understand design and architecture needs for different consumers to build projects that meet real demand, reducing real estate risk for builders

Healthcare

The use of data science in healthcare is vital and helps save lives. Some applications can:

• Scale the need for medical supplies and emergency equipment to support ERs and urgent care units
• Use AI solutions to track key health markers and reduce the risk of hospital infections and other critical complications
• Improve hospital management and logistics, reducing costs and inefficiencies in daily operations or crises
• Optimize triage and medical histories
• Improve image recognition for diagnostics

Agribusiness

In Agribusiness, Data Science can bring innovative ways to optimize agricultural infrastructure. Among some solutions:

• Sizing and planning for the use of agricultural inputs, pesticides, and seeds
• Risk analysis for crop insurance
• Investment modeling, risk, and return for growers of all sizes

Why Your Data Foundation Matters as Much as Your Model

No data science application, whether churn prediction, sales forecasting, or risk modeling, works well if the data feeding the model is scattered across different systems or outdated. A churn prediction model trained on CRM data that doesn't match financial data will learn the wrong pattern, with the exact same confidence as one that learned the right one.

That's why, before investing in data scientists or predictive models, it's crucial to ensure your data ingestion is solved: centralized, updated, and reliable sources. This is the role Erathos plays, connecting your company's data sources directly to the warehouse where data science models fetch information, without requiring custom pipelines to write or maintain.

Frequently Asked Questions about Data Science

What is the difference between data science and analytics? Analytics explores data to answer specific questions about what has already happened. Data science builds predictive and prescriptive models, forecasting what will happen and recommending actions, rather than just describing the past.

Do I need a data scientist from day one at my company? In most cases, no. Early-stage companies benefit more from reliable data ingestion and basic descriptive analytics. Dedicated data science usually makes more sense when there is already enough data volume to train models with confidence.

Are data science and artificial intelligence the same thing? No. Artificial intelligence is a broader concept that includes data science as one of its applications. Data science uses statistical and machine learning techniques, which are part of the broader AI landscape, but with a specific focus on extracting insights from business data.

Why is my data science model not performing well? In most cases, the problem is not the model; it's the data feeding it. Data scattered across different systems, outdated, or inconsistent across departments produces a model with high confidence but predicting the wrong pattern.

Conclusion

Understanding what data science is and applying it strategically can completely transform how your company makes decisions and generates value. Combining mathematics, statistics, and modern technologies, this field enables you to build a data-driven culture capable of delivering more efficient and relevant solutions to the market.

The possibilities are endless, and to apply them, you need a data strategy for your company, creating an action plan based on the improvements that need to happen. Using Data Science to guide business decisions is a journey involving multiple stages and different implementations, starting with the reliable data foundation that any model depends on.

Create your free Erathos account or book a chat with us to find out how we can help your company become data-driven in less time.