Compass and crystal ball? In the world of data, data analytics and data science are exactly that - tools that show companies the way or give them a glimpse into the future. Companies use them to gain valuable insights, make well-founded decisions and develop innovative solutions. But what exactly is the difference between data analytics and data science? And why is it so important for companies to know these differences?
In this article, we highlight the key characteristics of data analytics and data science and compare the job profiles of data analysts and data scientists. We show how both approaches support companies in achieving their goals and driving innovation. We also explore the central question: How can these powerful approaches be used optimally to lead your company into the future?
What is data analytics?
Data analytics deals with the analysis of past data in order to gain patterns and insights that help companies make important operational decisions and optimize processes. Typically, structured data is analyzed - i.e. data that is available in clear, predefined formats such as tables.
There are two main methods of data analysis:
- Descriptive analysis: It answers questions such as "What happened?" and "What patterns are recognizable in the data?". For example, a company could use descriptive analysis to determine that sales have fallen in the last quarter.
- Diagnostic analysis: This method goes one step further and examines the causes of certain patterns. Questions such as "Why did this happen?" or, in the case of the example, "What caused this drop in sales?" are answered here.
The toolset of the modern data analyst: from Excel to SQL
Data analysts mainly work with structured data. This data is well organized and easily accessible - often stored in databases and data warehouses. Examples include transaction data, customer data and sales data. From this, data analysts can create complex calculations for comprehensive analyses or create models using "what-if scenario planning" to make intelligent decisions faster.
Data analysts use various tools to carry out their analyses:
- Excel is a widely used tool for data processing and visualization. With its extensive functions and user-friendly interface, Excel enables quick calculations, the creation of pivot tables and basic data visualizations.
- Tableau offers powerful visualization options to present complex data in an understandable way - and works simply by drag & drop.
- SQL ("Structured Query Language") is a standardized programming language for managing and querying relational databases. With SQL, analysts can retrieve, filter, sort and merge data from databases in order to extract relevant information. Sounds complex? Think of a database as a huge, well-organized library. SQL is like an intelligent librarian who knows exactly where each "book" (record) is and how to find it.
Data analytics: increasing efficiency and optimizing business processes
The main objective of data analytics is to support companies in making operational decisions and improving their processes. By analyzing past data, patterns and trends can be identified that help to increase efficiency and achieve business goals. Data analytics makes it possible to establish relationships between different metrics and gain deeper insights.
A practical example is a retail company that analyzes its sales figures to evaluate the effectiveness of a marketing campaign. By identifying sales and customer trends, the company can optimize the performance of its campaigns and make data-driven decisions. Such analyses not only help to identify the reasons for declining sales, but also to improve the customer experience and transfer successful strategies to other locations. Ultimately, this leads to measurable improvements in the company's performance.
Synergy of knowledge and ethics in data analysis
Data analytics combines various disciplines—from statistics to business administration to IT. This diversity makes the field all the more exciting. At the same time, ethical questions come to the fore: Privacy notice and fair use of information are crucial. The challenge here? Companies must ensure that their analyses are responsible and compliant with the law.
What is data science?
Data science goes far beyond analyzing past data. It is the craft that enables companies to develop innovative solutions and identify future opportunities. Data scientists use statistical models, machine learning and deep learning to gain valuable insights from large, often unstructured amounts of data - such as texts, images or videos.
Here are the most important methods:
- Predictive analytics: Enables predictions to be made about future events based on existing data patterns.
- Machine learning: Machines learn independently from data and improve their forecasts over time without being explicitly programmed.
- Deep learning: A sub-area of machine learning in which neural networks are used to recognize complex patterns in large data sets - similar to the human brain.
The most important tools include programming languages and frameworks such as Python, R and TensorFlow. Python and R offer numerous libraries for statistical analyses and machine learning, while TensorFlow was developed specifically for deep learning applications.
What is machine learning?
Machine learning enables computers to learn from data and perform tasks independently without having to be specially programmed. The algorithms recognize patterns and correlations in large amounts of data and become better and better over time the more information they process. This ability makes machine learning very useful: it can find correlations in large amounts of data, make predictions and complete tasks automatically - often faster and more accurately than humans.
From unstructured data to innovation: how data science is shaping the future
A key difference to data analytics is that data scientists often work with unstructured data that is not organized in traditional databases or tables. This includes texts, images, videos or even social media data, which require special analysis methods.
In the latter case, for example, millions of people share their thoughts, preferences and opinions on platforms such as X, Instagram or Facebook every day. Data scientists use social media analyses here to identify trends or understand customer behavior. By analyzing hashtags, likes or interactions, it is possible to find out which products, services or topics are trending - and could become particularly popular with the target group in the coming months.
While data analytics is primarily aimed at optimizing operational processes, the focus of data science is on innovation. Data science helps companies to develop new business models and make long-term strategic decisions that have the potential to transform entire industries.
Data scientist wanted
Incidentally, the shortage of specialists in the field of data science is currently getting worse and worse. According to a Bitkom study from 2022, 96% of German companies consider the recruitment of dataspecialists to be difficult. 38% see it as very difficult and 58% as rather difficult. The profession of data scientist therefore promises enormous opportunities for the future, as demand is significantly higher than supply on the labor market.
Data analyst vs data scientist: a comparison of job roles
The difference between data analysts and data scientists lies not only in the methods they use, but also in their roles, responsibilities and the added value they bring to the company. While data analysts focus on examining data from the past, data scientists dedicate themselves to the future by making predictions and developing innovative approaches.
Tasks and responsibilities
- Data analyst: Data analysts focus on analyzing and interpreting existing data. They create reports, identify trends and provide concrete recommendations for current business decisions. Their main task is to answer the "what" and "why" questions.
- Data Scientist: Data Scientists are responsible for developing models that enable predictions about future developments. They design machine learning algorithms and often work closely with product development teams to drive data-based innovation. Their focus is on the "what if" questions.
Skills and qualifications
- Data Analyst: Strong analytical skills, excellent knowledge of statistics and experience with data visualization tools. Good knowledge of SQL and Excel is essential, as well as the ability to translate business problems into data-based solutions.
- Data scientist: In addition to analytical skills, a data scientist also needs programming skills (Python, R) and a deep understanding of machine learning. Skills in working with large, often unstructured data sets are also required. Knowledge of mathematics and advanced statistics is also very important.
Methods and techniques
- Data Analyst: Descriptive and diagnostic analyses are the most important methods. This includes the identification of patterns, trends and correlations in existing data.
- Data Scientist: Predictive analytics, machine learning and deep learning are key techniques used to develop data models in order to predict future results or automate processes.
Tools and technologies
- Data Analyst: Tools such as Excel, SQL and Tableau are frequent companions in everyday working life. These tools help to manage databases, create reports and generate visualizations.
- Data Scientist: Python, R, TensorFlow, Hadoop and Spark are standard tools for performing complex analyses and machine learning models and processing big data.
Added value for the company
- Data analyst: The added value offered by a data analyst lies in the optimization of current business processes. By analyzing the past, trends can be identified and more efficient solutions implemented.
- Data Scientist: Data Scientists create long-term added value through innovation. They enable companies to develop new business models, make strategic decisions and drive forward data-driven innovations.
| Criterion | Data Analyst | Data Scientist |
|---|---|---|
| Tasks and responsibilities | - Analysis of past data - Creating reports |
- Development of models for predicting trends |
| Skills and qualifications | - Analytical skills - Data visualization |
- Machine Learning - Programming skills, - mathematics |
| Methods and techniques | - Descriptive and diagnostic analysis | - Predictive analytics, machine learning, deep learning |
| Tools and technologies | - Excel - SQL - Tableau |
- Python - R - TensorFlow - Hadoop - Spark |
| Added value for the company | - Optimization of processes and decisions | - Innovation - Development of new business models |
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The choice between data analytics and data science: which path is right for your company?
Every company is faced with the question of which data strategy best suits its goals: Data analytics or data science? Both disciplines offer valuable approaches, but the focus and results differ. The choice between data analytics and data science depends heavily on the specific needs and strategic direction of your company.
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When should you use data analytics?
Data analytics is the right choice if your company is looking for immediate solutions to operational challenges. By analyzing past data, you can quickly recognize patterns, identify sources of error and make informed decisions to increase efficiency. Companies in highly regulated or more traditional industries, such as retail or finance, often benefit from data analytics as existing processes are optimized.
Typical questions are:
- How did our last marketing campaign go?
- What caused the drop in sales this quarter?
- What trends can we identify in our customer behavior?
Data analysts support you in improving ongoing business operations by helping you to learn from the past and make operational decisions.
When is data science the better choice?
Data science is ideal if your company is looking for a long-term competitive advantage. It's less about what happened in the past and more about developing new business models, forecasting and innovative approaches. If your company operates in a dynamic market environment where rapid technological changes and innovations set the tone - such as in the tech sector, healthcare or e-commerce industry - data science can be of great benefit.
Typical questions are:
- What future market trends can be predicted and how can we respond to them?
- How can data-driven innovations be developed and implemented?
- How can we use machine learning to improve or automate our products?
With Data Science, you are focusing on a long-term, strategic perspective that enables you to actively shape the future and open up new business opportunities.
Combination of both approaches: Using synergies
In many cases, it makes sense to combine data analytics and data science. Data analytics provides important insights into current operations and helps to optimize processes. Data science, on the other hand, paves the way for future innovations by developing new business models and strategies. This synergy can give companies a clear competitive advantage.
A good example of this is Netflix. The streaming provider uses data analytics to analyze past user data. It examines which series and films are watched by which audience and at what time. This data helps Netflix to create personalized recommendations for its user . This keeps them on the platform for longer and strengthens customer loyalty. And who hasn't experienced it: choosing a series or movie in the evening is becoming increasingly difficult - as the selection is always tailored to suit.
But Netflix goes one step further: with the help of data science and machine learning, the company analyzes huge amounts of data to predict trends. The aim is to find out which genres and topics could become popular in the future. Whether action or urban romance: Netflix uses these insights to produce new films and series that appeal to viewers' tastes.
Conclusion - The right path for your company
Whether data analytics or data science - the right approach depends on your company's goals. If you want to optimize your processes in the short term, data analytics is the right tool. However, if you want to invest strategically in the future and create data-driven innovations, then data science is the better choice.
Whichever option you choose, it is crucial that your company has the right specialists with the right skills on board. With the right tools, methods and skills, you can get the most out of your data.
If you're still unsure which strategy best suits your company or are looking for further training, continuing education providers like Haufe Akademie offer targeted courses and programs on data analytics and data science . These will help you acquire the skills you need to make data-driven decisions and future-proof your company.
FAQ
What is the difference between data analytics and data science?
Data analytics focuses on analyzing past data to identify patterns and support operational decisions. It answers questions such as "What happened?" and "Why did it happen?". Data science, on the other hand, goes further and uses advanced techniques such as machine learning and predictive analytics to make predictions about future developments and promote innovation. It answers questions such as "What will happen?" and "How can we influence it?".
What types of data do data analysts vs. data scientists analyze?
Data analysts mainly work with structured data organized in databases and tables, such as sales figures or customer data.
Data scientists analyze both structured and unstructured data. Unstructured data includes texts, images, videos or social media data, which require more complex analysis methods.
What career prospects do the roles of Data Analyst and Data Scientist offer?
Data analysts can work in areas such as marketing, finance, logistics or corporate management. Their skills are in demand in many industries, especially when it comes to optimizing business processes.
Data scientists have a wide range of career opportunities, especially in innovative and technology-driven industries such as the automotive industry or IT. They often work on projects that incorporate machine learning and artificial intelligence and contribute to the strategic direction of the company.
How do data analytics and data science complement each other in a company?
Data analytics and data science can work synergistically. Data analytics provides quick insights and helps to make short-term business decisions. Data scientists, on the other hand, develop long-term models that drive innovation and enable new business models. Companies that combine both approaches use data for both process optimization and strategic future planning.
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