Today, data is the core driver of the majority of business processes in all industries. By 2025, the total amount of data in existence globally is estimated to reach a figure in the range of 175 zettabytes (according to Deloitte) to 181 zettabytes (as forecasted by Statista). Moreover, Deloitte states that today, people are no longer the main producers of data — it’s technology, such as Internet of Things networks, smart devices, and other hardware, that generates the biggest chunk of modern data.
With the dependency of all routine flows and processes on data, it’s only logical that data deserves dedicated jobs and even its own field of science. The market for data-related jobs is growing faster than others, with data projected to create as many as 300,000 jobs by 2031 in the US alone as estimated by the U.S. Department of Labor.
In the context of data processing, we often come across references to data science and data analytics, and these terms are frequently used interchangeably. But while these two fields are closely related, there are key differences. This post dives deep into the specifics of data science and data analytics, attempting to answer the main question: “What is the difference between data analytics and data science?”
What is data science?
In God we trust; all others must bring data.
Data science applies scientific methods to extract meaningful information from various structured and unstructured data sources to isolate patterns and generate actionable insights. In the process, data science uses techniques from multiple scientific and science-related disciplines, such as mathematics, statistics, programming, machine learning, artificial intelligence, and data engineering and analysis. In building insights, data science applies visualization tools and techniques to communicate its findings.
Data science process
The data science process aimed at producing insights to perform a certain task typically passes through a sequence of steps:
- Data collection. To provide a basis for the research, data scientists tap into various resources to collect relevant data. Depending on the task, sources may vary from special internal or external databases to public portals and social media.
- Data cleaning. In most cases, gathered raw data is not suitable for immediate processing and requires conversion into structured formats that can be consumed by further algorithms.
- Exploratory analysis. Structured data is studied to find patterns and dependencies that can be useful in generating insights. At this stage, data scientists apply statistical techniques to discover trends and outline the direction of further processing.
- Modeling. Using machine learning algorithms, data scientists build models based on patterns identified in the available data. Models are tested for accuracy and can be adjusted and optimized if necessary.
- Interpretation. Based on the ML modeling results and using visualization techniques, data scientists present their findings to stakeholders in a human-comprehensible format.
Core elements of data management
In processing and managing data to achieve meaningful results, data science relies on multiple tools and methodologies to ensure the validity, integrity, and security of data:
- Data analysis. To define the basic nature of data and identify simple trends and patterns, data scientists use data analysis methods. The results of data analysis lay the foundation for further data modeling.
- Statistics. This discipline is needed to isolate patterns in data and derive insights from them. The majority of datasets scientists use follow certain normal distribution patterns characterized by similar properties that can be analyzed using statistical techniques.
- Data engineering. Enabling scientific manipulations of data requires infrastructure that acquires, stores, transmits, and protects datasets. All these resources are set up by data engineering services, that often use database management to enable secure and efficient data storage and retrieval.
- Machine learning. Machine learning is based on algorithms and models that enable automation of predictions, pattern analysis, and generation of insights.
- Advanced computing. Data science involves a lot of programming, including designing, debugging, testing, and maintaining source code used in data science processes. For data scientists, proficiency in a programming language — most often, Python — is a must.
- Domain expertise. Data does not exist in a vacuum, and both data science tasks and source data belong to a certain industry domain, describing its specifics. To build an efficient ML model for a particular task, data scientists need to be familiar with the domain from which data has been obtained.
What is data analytics?
Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.
Data analytics collects, organizes, and analyzes raw data to draw conclusions from it. In the process, it converts data into a human-understandable format to present it as information that can serve as a basis for decision-making.
As you may have guessed, data analytics represents a subfield of data science, processing incoming data and organizing it for further manipulation. Alternatively, it may be used as a standalone process, converting and visualizing data for use by stakeholders.
Data analytics process
From collecting raw data to representing it in a comprehensible format, the data analytics process involves several activities:
- Problem definition. The data analytics process begins with outlining the problem or task and understanding stakeholders’ expectations. This step usually involves direct communication with stakeholders to get an idea of what they wish to obtain.
- Data collection. Proceeding from the task or problem definition, data analysts collect and prepare data from various sources, both internal and external. Collected data is usually stored in a database or a simple spreadsheet.
- Data cleanup. At this stage, data is checked for errors and stripped of duplicates and irrelevant entries. Simultaneously, gathered data is checked for biases that may compromise the results.
- Data analysis. Using database management tools, data analysts find trends and patterns in the dataset, combining similar data and performing necessary calculations.
- Data visualization. The results of data analysis are presented in a visual format, such as a graph or a chart, making them understandable even to a non-technical audience. Such visual artifacts highlight findings that may help in decision-making.
- Communication. The final step in the data analytics process is passing visualized results to stakeholders, accompanied by explanations and interpretations that highlight the key findings. At this stage, information derived from the collected data is ready for use in decision-making.
Core components of the data analytics process
To set up an effective data analytics process, the following components are required. Unified in a cohesive ecosystem with a DataOps methodology, they create a seamless flow of data from acquisition to presentation:
- Data acquisition. To provide material for analytics, the process must begin with gathering relevant data. Depending on the task, there may be various sources and generators of data, both hardware and software: sensors, audio and video equipment, meters and gauges, computer applications and drivers.
- Data governance. To ensure regulatory compliance, data analysts need to implement data governance standards with regards to data transmission and ownership. In addition, should any regulations apply to the measurements and storage involved in the analytical process, those need to be accounted for too.
- Data security. While handling data in an analytical process, experts must ensure its protection from external threats and attacks. In some extremely sensitive areas, such as healthcare or finance, additional measures must be taken to guarantee data security and establish compliance with applicable regulations.
- Data storage. Collected data needs to be placed in secure and effective storage to ensure its safety and availability.
- Analytical tools. To calculate and analyze data, analysts use a variety of tools, from Microsoft Excel and Microsoft Power BI to the Python and R programming languages. These tools offer data processing features that help to build analytical algorithms.
- Visualization tools. To present analysis results to stakeholders, data analysts need tools for converting data into clear and intuitive graphs highlighting their findings.
Data science vs data analytics: main differences
If we wanted to summarize the differences between data science and data analytics in just a few words, we could say that data science uses data to build predictions for the future, while data analytics processes past data to enable today’s decisions.
In simple terms, the data science vs data analytics difference is best expressed in the following questions:
Data analytics: What happened and why did it happen?
Data science: What is going to happen and what can we do to make it happen?
In short, while data science and data analytics have some overlapping features, they have certain core differences that need to be taken into consideration while planning a successful data strategy:
Data science vs data analytics comparison
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Example applications and use cases
Real-life situations often call for leveraging features of both data science and analytics, as their practical applications might overlap. Still, let’s look at some typical cases when businesses need to take the approach of either data science or data analytics and seek the corresponding expertise. Such examples are the best illustration of the “data science vs data analytics” comparison.
Data science use cases
The majority of data science use cases are based on its ability to build future predictions using historical and current data.
Predictive maintenance in manufacturing
Enterprises looking for ways to optimize equipment maintenance costs, reduce downtime, and extend machinery lifespans implement data science solutions to switch from reactive to proactive maintenance. In such data-powered software products, which combine hardware and software, equipment is monitored through multiple sensors and cameras with ML models using data to build predictions.
As a result, any changes in machinery behavior are detected in sufficient time to project their possible consequences and take necessary measures.
We used this approach in building a predictive maintenance solution targeting the manufacturing, energy, utility, and chemical industries and allowing enterprises to adopt a proactive maintenance concept. The platform collects data from strategically placed sensors and leverages advanced IoT and ML technologies to detect anomalies and predict failures.
Fraud prevention and score evaluation in finance
Financial institutions apply data science methodologies for early identification of behavioral patterns that suggest fraud. By identifying patterns that may indicate fraudulent activities, data science algorithms create predictive models that can forecast such actions and enable financial organizations to take appropriate measures.
At the same time, banks and lending organizations can leverage data science to support and validate credit score evaluations for their customers. By studying borrower profiles, intelligent algorithms calculate credit scores to facilitate customer evaluation. A similar solution was implemented for a lending business offering a full cycle of financial services. As a result, the lending process became smoother for both the company and its clients.
Predictive diagnostics in healthcare
By applying data science algorithms to medical data contained in records, images, and wearable device data, healthcare providers can detect deviations early enough to offer effective treatments. Conditions such as diabetes, cardiovascular diseases, and some types of cancer can be diagnosed by patterns in patients’ data and lifestyle.
We pursued the same goal when our team participated in developing a digital mammography solution, designing an ML algorithm for automatic anomaly detection. Combined with digital reporting and image interpretation, this ML feature allowed the healthcare organization to improve diagnostic precision and streamline patient service.
Data analytics use cases
By applying various algorithms and calculations to historical data, analysts determine root causes of certain events and behaviors and discover optimization opportunities. Data analytics services find use in multiple real-life applications.
Performance evaluation
Data analytics can be useful for evaluating performance on multiple levels, from individual employee performance to the effectiveness of marketing campaigns. By analyzing various performance metrics, businesses can get insights about outcomes and identify possible areas for improvement.
Enhancing analytical algorithms with AI and ML, we helped a business consulting company to develop a brand performance evaluation solution that provides data for marketing strategy recommendations. Analyzing large volumes of data received from multiple online sources, the application determines how well the brand is doing and if strategy improvements are necessary.
Logistics optimization
Data analytics can play an important role in optimizing supply chain and transportation flows by analyzing warehouse, fleet, and inventory data and suggesting ways to improve efficiency. Processing data obtained from both sensor devices and software, analytics solutions isolate important trends that allow enterprises to better plan their activities.
In one of our projects, we built a retailer network analytics platform using advanced technologies to gather and process data from multiple locations of an automotive manufacturer. This platform gave the client improved visibility into their operations on a company-wide scale, providing them with unique marketing insights.
Energy management
Enterprises use analytical tools to monitor energy production and consumption. Insights generated by energy analytics help to optimize production costs, balance distribution, and identify consumption patterns to match production to demand.
This approach is at the core of our digital energy management platform with advanced visualization and reporting, which allows our client to optimize production, control billing, and practice preventive maintenance.
How data analytics and data science work together
In our “data analytics vs data science” comparison, we mainly focused on the differences between the two fields. However, they have a lot in common and their applications often overlap.
From what we have discussed so far, you can see that in most cases, data analytics operates under the umbrella of data science, performing manipulations required to apply scientific algorithms and build ML models. In such processes, the role of data scientist is more senior than that of a data analyst and calls for a broader range of skills.
Data scientists research and create new approaches and algorithms that analysts use in their routine work. At the same time, analysts collect and prepare large sets of data, generating reports reflecting the current state of things and providing material for data scientists.
In real-life projects, collaboration stretches along the entire data pipeline, involving data analysts, data scientists, data architects, and data engineers, all of whom contribute their skills toward the common goal of collecting quality data and converting it into valuable insights. These professionals share skills, such as knowledge of Python, R, or SQL, and perform their individual tasks adding to the common mission of data management.
Bottom line
To paraphrase the famous statement, data makes the world go round. Businesses today must treat their data as a strategic asset. It is imperative to have skilled data professionals to properly collect, store, process, and analyze a firm’s information. Enterprises that lack the expertise to refine their raw data into actionable insights risk stalling in the competitive race. Those who are adept at using advanced analytics to extract maximum value from their data reserves, turbocharging decision-making, increasing operational efficiency, and identifying new profit pools will be winners.
Whether you’re just starting your journey into data or need to take a more comprehensive approach to your data assets, Intellias would be happy to support you by providing our data services. We’ll help you set up a robust data infrastructure that meets your business needs, integrate all your data sources, build advanced analytics models, and provide actionable insights you can use to drive growth, optimize operations, and gain a competitive edge.
