Improvements in data collection and storage have allowed companies to prevent fraud, streamline operations, and gain new insights into consumers. However, implementing new measurement systems requires a team of professionals who can create software, run programs, and interpret results correctly.
Data science and data analytics are two closely interconnected fields that rely on statistics, programming, and other sciences to allow individuals and companies to interpret data. The skills and resources required for data analytics and data science vary somewhat depending on the industry, but the concepts behind the two are always the same.
In short, data science is the back-end theory behind modern data analytics but understanding the full differences between data analytics vs data science is essential to making the appropriate investments in both. Each has different boundaries and potential applications that can be revolutionary for businesses in the 21st century.
Key Skills of Data Analytics
Data analytics focuses on solving problems and gaining new insights from a given dataset. This data can be internal databases, data belonging to outside organizations, or a mix of both. Data can include numbers, words, descriptors, text, and more, and can be organized or in raw form.
Data analytics requires detailed knowledge of how to identify trends and differentiate correlation from causation. The first step in data analytics is collecting and grouping relevant data, which can include big data and other sources that have already been compiled into data lakes for easy processing.
From there, analysts must decide which data is relevant to the questions they want to answer. Sometimes the data analytics are limited by incomplete or unreliable data. However, with solid data collection, clean-up, and storage practices, data analysts can determine trends based on historical data from a wide variety of sources.
Questions Data Analytics Can Answer
Thanks to improvements in technology, data analytics can now answer a wide range of questions about:
- Who is utilizing services and products
- Where customers live and work
- What different groups of customers have bought
- Why customers need or want certain things
Some of these questions are much more difficult to answer than others. For example, “why” questions can be tricky to answer, as the correlations present in the data do not always translate to causations. A data analyst may want to know why a particular product had a spike in sales in one month but may be unsure of whether it was due to a change in customer demographics or outside world events.
With the right computer programs and human minds at work, data analytics can reveal complex answers to questions. Data analytics can reveal marketing successes, identify reasons for major and minor changes in sales, and allow companies to understand and improve customer satisfaction. Analytics allows companies to learn from their mistakes and try to replicate their successes in the future.
All of this is possible, thanks to the systems that data science provides.
Why Data Science?
Data analytics is a type of data science, which encompasses broader questions of how to analyze things in different ways using data mining, predictive analytics, and more. Data science essentially determines how the processes of data analytics work and refines and improves systems.
Data science is an umbrella term for the broader field that encompasses data analytics. Without data science, data analytics cannot be performed. However, another way to think about the difference between data science and data analytics is the relationship between the human nervous system and the hands and feet.
Data science is the brain and nervous system, which take and improve one’s practices through analysis and inventing new approaches to solving problems. Data analytics, on the other hand, is like the hands and feet that make the work happen.
Data Science Skills
One of the key skills of data science is programming. Most modern data analytics platforms run on Python, R, SAS, SPSS, or another programming language that can run extremely complex calculations. Programming languages are continually improving and becoming more powerful, so data scientists must continuously improve their knowledge and the systems that rely on those languages.
Another essential area is the development of tools that can process unstructured data, like audio and video. Automatically pulling dialogue, keywords, and other information from videos has been challenging, even as speech recognition technology has improved.
Big data is an up-and-coming area of data science that has been made possible by the enormous amounts of data the technology industry collects on its consumers. It’s critical for companies ranging from big box retailers to healthcare organizations to be able to understand their consumers’ needs based on their demographic factors and personal preferences.
Past vs Future
Another way to think about the difference between data analytics and data science is the difference between analyzing past data and predicting future events. Data science techniques can use past trends to draw conclusions and project future outcomes instead of just comparing variables from past data.
The biggest example of this is machine learning, which uses artificial intelligence (AI) to learn from past events and predict future ones using large amounts of data. There are various forms of machine learning, including machine learning, called unsupervised learning that tries to learn trends without seeking a particular objective.
Although unsupervised learning has its place, the most common type of learning for marketing applications is supervised learning. This type of machine learning teaches a program to look for conditions that lead to a given result, like customers deciding to buy a product.
Deep learning analytics use neural networks to run programs and learn processes similarly to humans, but faster. They have fallen in and out of fashion for several decades but have recently reemerged as serious contenders in data science. One key reason for the change is that computer chips can now handle the amount of processing required for deep learning.
Data Analysts vs Data Scientists
The roles of data analysts and data scientists are different, too. Data analysts examine data sets, run calculations, and draw conclusions. They may make charts and other tools to communicate those conclusions to colleagues and clients. They have a data analytics background that includes mathematics and technical knowledge about statistical operations.
Data scientists dedicate their careers to developing new programs and methods of data analysis. Data analysts do the day-to-day work of crunching data, while data scientists dive into deeper theory and innovation.
Both roles are crucial to businesses that need accurate and cutting-edge data analytics and science. Data scientists may develop groundbreaking new methods that revolutionize a company’s processes, but data analysts are usually the ones reading and communicating the results of those data science methods.
Applicability to Industries
Data analysis vs data science is an important distinction for healthcare, financial services, and other industries that must understand and improve their customer service and marketing. Data analytics can reveal which advertising campaigns are working, which services customers are using, and which past actions have affected a company’s bottom line.
Data science has paved the way for new fraud prevention methods, better healthcare data storage systems, and much more. As data science has evolved, it has learned to predict problems in financial sectors and even model the trajectory of the COVID-19 pandemic.
Current Progress and Future Development
Data analysis and data science are still being refined, and improvements are made every day. For example, machine learning analytics sometimes flag false positives or put something in an incorrect category and must be taught not to make the same mistake again.
However, the current and potential abilities of data science are clear due to huge improvements in computer processing power. Companies can now even use cloud computing to run data analytic and use machine learning programs, avoiding hefty costs associated with buying and maintaining servers on-site.
Whether you’re streamlining a healthcare facility or predicting wear and tear on factory equipment, Syntelli Solutions has the data analytics, big data, and machine learning know-how to help you reach new heights. We have a long track record of success in industries ranging from manufacturing to financial services. Contact our team today so we can make our data science and analytics expertise work for you.
Due to its fast, easy-to-use capabilities, Apache Spark helps to Enterprises process data faster, solving complex data problems quickly. We all know that during the development of any program, taking care of the performance is equally important. A Spark job can be...read more
As the U.S. economy faces unprecedented challenges, predictive analytics in financial services is necessary to accommodate customers’ immediate needs while preparing for future changes. These future changes may amount to enterprise transformation, a fundamental...read more
Healthcare organizations face an array of challenges regarding customer communication and retention. Customer intelligence can be a game-changer for small and large organizations due to its ability to understand customer needs and preferences. When it comes to data,...read more