Remember how we talked about innovations in transportation in our recent article? One breakthrough that undeniably revolutionized power generation and vehicle performance was the combustion engine. Developed at the turn of the 18th and 19th centuries, it evolved through the decades to soon power more energy-efficient vehicles, liquid-fueled rockets, and jet aircraft, making trade and travel faster and less expensive.
But what does this invention have to do with data analytics or data science? Just ask Peter Sondergaard, the former executive vice president and member of Gartner’s operating committee. In his famous quote, he said, “Information is the oil of the 21st century, and analytics is the combustion engine.”
These simple words perfectly illustrate the significance of data and its processing in the contemporary world. In this article, we will investigate how it impacts the automotive industry and elucidate the difference between data science and data analytics to settle it once and for all.
Even though the idea of extracting valuable knowledge from gathered data emerged in the 1960s, in 2022, many of us still struggle to differentiate between data science and analytics. Both deal with data, use techniques and theories from mathematics, computer science, statistics, and other disciplines, and both can benefit your business in many ways.
However, these terms should not be used interchangeably, as they refer to distinct concepts. Business-wise, you need support from a data analyst in some cases, but seek assistance from a data scientist in others. But let’s start from the basics.
In simple terms, data science is a field of research that looks into gathering, integrating, modifying, and manipulating data. Just as a biologist dissects an animal to learn what’s inside or looks under the microscope to understand how amoeba function, a data scientist investigates mass volumes of information to assess what knowledge can be derived from it to inform various industries and disciplines.
As a multidisciplinary field, data science comprises statistics, mathematics, artificial intelligence (AI), software programming, and advanced analytics. It uses raw data to identify trends, patterns, and correlations and uncover actionable, industry-specific insights. By leveraging data science, companies can improve decision-making, eliminate errors, streamline operations, and optimize the customer experience, among other benefits.
For example, in car insurance, data scientists apply AI algorithms to help providers establish dynamic pricing aligned with the driver’s risks. Or in fleet management, data-driven analytics software allows managers to understand asset utilization, improve fuel efficiency, and optimize maintenance to save costs.
The scope of data scientists’ work embraces stages such as:
Even though the term ‘data science’ is relatively new (it was popularized in 2008 by Dr. DJ Patil and Jeff Hammerbacher, heads of analytics and data at LinkedIn and Facebook, respectively), conceptually, the discipline can be traced back to the 1960s. Then, statisticians and mathematicians began investigating data as a research subject, looking into how they could convert it into useful knowledge.
In the 1970s, the term ‘datalogy’ emerged, referring to the new field of study. By the early 2000s, the notions of data mining, processing, and knowledge extracting had been well-researched but still limited to the scientific community.
We regard data science as the ‘science of data’ — the evidence-based study of the socio-technical developments and transformations that affect science policy; the conduct and methods of research; and the data systems, standards, and infrastructure that are integral to research.
The data science definition by Data Science Journal
Despite its maturing as a scientific discipline, data science had yet to wait for widespread business applications. However, a giant technology leap in the second decade of the 21st century has turned everything on its head. A convergence of factors such as the Internet boom, increasing computing power, the emergence of the cloud, and virtualization contributed to the rising adoption of data solutions across industries, making the evolution of data-driven business use cases possible.
Data analytics describes using data manipulation techniques to analyze datasets and:
Typically, data analysts deal with the following tasks:
The domain falls into four subcategories, each of which has different applications:
Data analysts typically write scripts in Python, SQL, or R programming languages to acquire data from various databases and data warehouses, clean, align, and reorganize it, conduct analyses and present their findings in easy-to-read formats. Unlike data scientists, they usually work with structured data, and their main goal is to spot trends, answer questions, and isolate patterns from datasets.
Other terms that are often confused are data analytics and analysis. They are often used interchangeably but hold distinctive meanings. To understand the difference, let’s first review their dictionary definitions:
analysis – a detailed examination of the elements or structure of something;
analytics – the systematic computational analysis of data or statistics.
In layman’s terms, the analysis aims to understand the ‘Why?’ and ‘How?’ based on events that happened in the past. For example, ‘Why has the number of road events increased in my fleet in recent weeks?’ or ‘How do I optimize routes to use less fuel?’.
To answer that, you 1) take the data at your disposal (e.g., telematics data, weather reports, accident history, etc.), 2) filter out information that might provide the answer to your question, and 3) and integrate it into a dataset. Then, this dataset (or several) becomes the subject of your analysis. The goal is to look for patterns that ultimately may help identify the root cause of a given issue. This way, you may find that your fleet drivers are more likely to cause accidents on roads prone to emergency braking or that they drive more efficiently in the early mornings.
Ultimately, data analytics takes you from data to information, and its product is usually a detailed report or dataset. And that’s where analytics steps in.
Data analytics aims to translate datasets (or data analysis outcomes) into specific actions and decisions by applying statistical tools, programming languages, and data visualization methods. In other words, it takes information and processes it to power decision-making. So, once you know the pattern (issues with emergency braking, greater fuel efficiency in the morning), you may now analyze what best course of action to take—thanks to analytics.
One of the key ways data analytics stands out from data science is what they do with data and what tools both fields apply.
Structured vs. unstructured data. Data analysts tend to work on defined and structured data sets. They examine a finite amount of data to conduct a specific task. Meanwhile, data scientists gather large sets of structured and unstructured data and perform ad-hoc data mining (exploring), very often not knowing what they are going to divulge. Quite like Henri Becquerel or Alexander Fleming, they may unearth knowledge from data by chance (though, of course, they need to know what data to use and how to manipulate them to make a discovery).
Statistics vs. AI. While data analysts rely mostly on statistical and investigative skills, data scientists apply predictive modeling, machine learning, and AI techniques. The former manipulate large but mostly specified datasets to spot trends and derive conclusions that inform business decision-making. The latter extract meaning from massive volumes of data not only to solve existing issues, but also to make predictions, suggest ingenious solutions, and reveal new trends.
Problem-solving vs. problem-defining. Analyst roles usually work reactively – i.e., they get a business issue to solve or a goal to attain, receive datasets to work on, and scan them for patterns. Meanwhile, data scientists often deal with the unknown. They may not have a thesis to formulate or a hypothesis to verify. Instead, they explore all data at hand, apply sophisticated AI algorithms or predictive models, and develop insights that can proactively improve business performance.
Still, there is some overlap between data analytics and data science. Both involve the application of programming languages and statistical analyses, involve operations on large data sets, and lead to problem-solving.
For instance, by combining driving performance and contextual information such as weather conditions or road topology, data-driven mobility analytics can help reduce vehicle wear. In the same way, data analytics solutions can help auto insurers improve their products by offering risk scores based on several sophisticated factors, which are of little value in isolation.
But while data analysts must be familiar with systems such as Excel, SQL, or Power BI and have a good grasp of data visualization tools, they don’t necessarily need to be adept in machine learning, big data platforms, and database administration. Meanwhile, these skills are essential for data scientists.
Recently, we discussed the differences and similarities between data analytics and data science. Today, we’re going to investigate the first one in more detail, focusing on its use in the context of mobility. Who uses data analytics solutions, what are the core use cases, and what aspects to consider when applying analytics to mobility? Check out this introduction to our 3-part series on data analytics SaaS. … Read More
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“Information is the oil of the 21st century, and analytics is the combustion engine.”
These simple words, spoken by Peter Sondergaard, the former executive vice president and member of Gartner’s operating committee, perfectly illustrate the significance of data and its processing in the contemporary world. In this article, we will investigate how it impacts the automotive industry in particular and elucidate the difference between data science and data analytics to settle it once and for all.
… Read More
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