With all the IT initiatives in place in all the organizations throughout the world, it’s no wonder why industry terms become muddled, confused, mixed-up, and interchanged. Data science, data analytics, data mining—it’s a mishmash of terms and concepts that overlap and interweave with one another, but that are still quite distinct. Ultimately, it becomes necessary to understand the purpose and value of each concept in order to give the terms real meaning, as all play a part in the world of big data.
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What is Data Science?
Let’s start with data science, largely seen as the umbrella discipline that incorporates a number of other disciplines. Data science, most often linked to the big data explosion, is the amalgamation of numerous parental disciplines, including software engineering, data engineering, business intelligence, computer science, and statistics, among others.
Data science incorporates a number of processes revolving around the retrieval, collection, ingestion, and transformation of large amounts of data – called big data.
Data science, which emerged in the wake of big data, is often said to include:
- The allure of big data
- The fascination of unstructured data
- The precision of advanced mathematics and statistics
- The innovation of social media
- The creativity of storytelling
- The investigation and inquiry of forensics
The Data Science Umbrella: Big Data, Machine Learning, Data Mining, and Data Analytics
Data science involves bringing structure to big data, finding compelling patterns in it, and advising decision makers on the possibilities and implications of putting it in motion. A number of tools and processes exist under the data science umbrella:
Big Data are huge volumes of unorganized data, often from a number of sources, not able to be processed using traditional applications. It is the foundation for data science.
Machine Learning encompasses artificial intelligence techniques used in data mining.
A combination of statistics, computer science, and mathematics, machine learning is a catch-all term to describe the process by which an algorithm learns from and makes predictions related to the data it encounters. Python programming language is a tool used extensively in machine learning development, for example.
Machine learning concepts include the interaction of existing systems like production databases, data cleansing, and data acquisition, with the ultimate goal of developing predictive models/machine learning algorithms that change as inferential statistics learn new data.
Data Mining involves building models capable of predicting values of target variables by applying machine learning algorithms to big data.
Data mining is the process of collecting data and searching for patterns in that data. Data mining involves designing algorithms used to extract insights from large, unstructured data sets by identifying and applying patterns. Some of the activities of data mining include:
- Supervised classification
- Pattern recognition
- Statistical techniques
Data science depends on data mining. In fact, it is usually the first step of data science, as it allows data scientists to differentiate between significant findings and random noise.
Data Analytics makes use of data mining techniques and tools to discover patterns in the analyzed data set.
Data analytics predicts the relationship between data sets or other known variables in an effort to learn how a particular event can occur in the future. Data science utilizes data analytics to provide strategic and actionable insights.
Data Science vs. Business Intelligence: How They’re Alike, How They’re Different
Many have come to view data science as the new business intelligence. However, data science and business intelligence are actually two very different disciplines, and one cannot replace the other. In fact, both data scientists and business analysts work together in different but related roles in big data, turning raw data into useful and actionable information.
Further, both data science and business intelligence allow organizations to uncover the information within raw data that may be commercially or socially useful. Many organizations require the expertise of both data scientists and business analysts to optimize their use of big data.
The business intelligence process includes providing retrospective reports to help businesses monitor the current state of their business and answer questions about historical business performance. In other words, business intelligence focuses on interpreting past data. Business analysts perform meticulous, plan-based work that includes assembling pieces of the big data puzzle to arrive at concrete answers.
Business intelligence tends to focus on reporting, dashboards, and alerts, all of which have the value of visualization. Easily digestible deliverables—pie charts, bar graphs, and the like—serve as the hallmark of business intelligence. The value of business intelligence lies in its accessibility. Although organizations use business intelligence in strategic decision making, it does have its limitations. Most importantly, business intelligence tools work with variables that already exist. In other words, we have to know what we are looking for to use business intelligence tools.
Data science differs from business intelligence in that it makes use of past data to make future predictions. Many times, data scientists help companies mitigate the uncertainty of the future by making predictions of future performance.
While business intelligence tends to be structured, data science leans more toward the unstructured. In other words, data science deals with incomplete, messy, unorganized data, not immediately usable without some degree of cleaning and prepping.
Data science and business intelligence exist on the same spectrum, albeit at opposite ends. Business intelligences focuses on managing and reporting existing business data in order to monitor areas of concern or interest, while data science generates predictive insights and new product innovations by applying advanced analytical tools and algorithms.
The data science toolkit is more technically sophisticated than the business intelligence toolkit, with data scientists utilizing tools such as advanced statistical packages, SQL, Hadoop, and open source tools like Python and Perl.
Data Scientists: The Evolution of the Quants
Quantitative analysts (called quants) are experts skilled in analyzing and managing quantitative data. When data science was still in its infancy, statisticians with advanced analytical experience—quants—dominated the field. They were responsible for finding that proverbial needle in the haystack, identifying it, and then turning it over to the hands of skilled programmers, who could then turn it into a repeatable, operational algorithm.
Quants faced a number of challenges during this time since the only data available was the data already known to be useful. In order to test a theory, quants would need to make use of a number of intricate statistical languages and find ways to operationalize the algorithms to repeat the findings. A full-scale support system, including databases and IT infrastructure, was necessary.
However, big data soon began to take center stage, particularly as the cost of storing and processing data plummeted, while at the same time data visualization tools allowed for the more efficient sifting and harvesting of data.
Today’s data scientists work without the complex support system the quants required, while at the same time the tools that data scientists use have a much steeper learning curve. But like the quants, data scientists must know what to look for even if they don’t know exactly what they may find. They must be experts in their industry and domain, and they must be able to possess the insight to know how an organization can optimize processes, reduce costs and increase customer value.