According to you, how important are data and information? Today, we aim to answer this question and address the meaning and difference between data science, data analytics, business intelligence, big data, and data mining.
Peter Sondergaard, the Senior Vice President of Gartner Research, one of the world’s leading research and advisory company once said, “Information is the oil of the 21st century, and analytics is the combustion engine.” There are no second thoughts about it. Data is an asset, a powerful tool. Many industry verticals leverage the power of data to understand in-depth business behavior and patterns.
To begin with, we can explore the difference between data science and data analytics. This question might have crossed some of your brains, at one point or the other — What is the difference between a data scientist and a data analyst? Aren’t they similar? No. Just because they sound similar, their mode of work is not precisely the same. Let us see how –
Data Science vs Data Analytics –
One can say that both data science and data analytics are heads and tails of the same coin. But the head is different from the tale, right? So is the case with a data scientist and data analyst. While a data scientist works on new methods for procuring data, a data analyst works using techniques and tools in analyzing this procured data to draw meaningful insights.
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The difference in roles and duties of a data scientist and data analyst –
1. Acquiring data, processing, and cleaning it, followed by its integration and storage, comes under the responsibilities of a data scientist. The primary role of a data analyst is report submission based on drawing inferences from data.
2. A data scientist does exploratory analysis and data investigation. Sticking on to a chosen model or algorithm, a data scientist applies data science techniques (like AI or ML) as we advance. A data analyst examines patterns, collaborates with stakeholders, and consolidates data.
So by now, I hope you know the key difference between data analytics and data science — In simpler terms, data science preps up data, which is further interpreted under data analytics. There is another terminology that might confuse you when speaking about data analytics. It is data analysis.
· When data analytics covers the techniques and tools to deliver optimal business decisions, data analysis is a little bit more specific — in fact, it is a subset of data analytics!
· Data analytics is a broad field with a pipeline of activities dealing with data and its processing. Data analysis comes under data analytics and focuses on specific tasks in scrutinizing and examining data.
Now, let us get into the trio ‘D’ — Big Data, in addition to both Data Science and Data Analytics, a bit more detailed –
Data Science vs Big Data vs Data Analytics
You have read about the dissimilarities between data science and data analytics. So, where does big data fit in?
Big data refers to a bulk amount of data, with different formats from various sources. This can be structured or unstructured. Big data is utilized in several fields. For example, in financial services to perform functions like fraud analysis. Companies in the field of telecommunication use big data for customer retention and the identification of new users.
Roles and responsibilities of a big data analyst -
Comparing to a data scientist and data analyst, the following are the scope of action for a big data analyst –
· The duties of a big data analyst seemingly overlap with that of a data scientist. However, to state it specifically — big data analysts work on the inferences and conclusions drawn by data scientists, understanding them so that they can be put forward as new actionable strategies and business techniques.
· Big data sets the infrastructure that acts as a backbone for data analytics. Therefore, big data analysts also work in the scope of performing market analysis, understanding competitive business advantages, and pointing out relevant trends in the industry.
Now that you are aware of the key difference between data science, data analytics, and big data, let us explore the domain of the rest two important concepts — business intelligence and data mining.
Business intelligence vs Data mining
So, what is business intelligence in simple terms? Businesses aim for competitive merit and optimal strategies of growth. Business intelligence is an approach utilized by companies to achieve these goals by examining existing as well as previous data and applying new technologies, techniques, and advancements.
What is data mining and how different is it from business intelligence? Data mining helps get into the process of business intelligence, aiding in solving industrial problems. It is a process in which a large amount of data is analyzed, discovering trends and patterns, which are then interpreted to make intelligent business decisions.
How does business intelligence use data mining?
Data mining works with raw data so that meaningful insights can be drawn from it. Industries across different domains apply the process of data mining in their businesses.
Connecting data mining to business intelligence:
· In data mining, first, the total of business objectives is analyzed. This is followed by the collection of data relevant to it, its preparation, data modeling, evaluation, and deployment. After the deployment, the data mining process is completed. It is in this phase, business intelligence takes over.
· Business intelligence is the method by which companies analyze the data models, understanding data interpretations to make informed and optimal decisions. Based on the learning from data mining, new strategies are devised in business intelligence that can improve a company’s further performance.
By now you will have a fair insight into the meaning and difference between the concepts of — data science, data analytics, big data, business intelligence, and data mining. Big data is analyzed by data mining to derive intelligent business decisions. Data science and data analytics go hand in hand. When the former focuses on new ways of attaining data, the latter focuses on making sense out of it.
Having known the relevance of data science and related disciplines, you might have understood the significance of getting yourself trained in these areas. Data scientist roles are in high demand these days. This is similar to the demand for big data analysts and data engineers. So, getting skilled in data science is an important choice you can make. Skillslash, an e-learning platform offers a host of courses that includes our Flagship Program, along with certification, that you can refer to!