The use of data analytics is increasingly pushing the adoption of big data for business decision-making. Data engineers also keep leveraging data science methods to solve real-world problems. But in applying these two terms, many times, the lines blur. This poses a very significant challenge if you plan to go into any of these fields. Check out this article for some differences between data analytics and data science.
Raw data has very little meaning until it becomes analytics. Before any information, it needs to go through several processes of cleaning and data preparation. The work of a data analyst begins after that. Data analytics is the process of leveraging data for business performance, market trends, competitive advantage, etc.
It involves a wide range of advanced analytics tools, techniques, and statistical models for the application of data. Combining these analytics tools, methods, and models for better business decisions constitutes business intelligence today. Depending on the data analysis project at hand, data analytics approaches may vary. However, there are four broad data analytics types established in the data management domain. They include:
- Predictive Analytics: Predictive analytics is the basis of how machine learning technologies function. It combines historical data and assumptions to predict future events.
- Descriptive Analytics: Some events occur with no records to show how they happened. Descriptive analytics employs data to draw connections between several data points to support meaningful descriptions of such events.
- Prescriptive Analytics: Prescriptive analytics profer solutions to help organizations scale specific data problems or attain business targets and goals. When there’s a problem, prescriptive analytics help analysts answer the question of what can be done? It uses neural networks, recommendation engines to generate suggestions that can help solve the problem.
- Diagnostic Analytics: When an event occurs, descriptive analytics use data in trying to understand why. According to Gartner, some attributes of diagnostic analytics include data discovery, data mining, and correlations.
Data science is the scientific study of data for efficient application. A data scientist can generate a hypothesis using scientific methods. And with computer science and statistical models, they can draw further analyses. There are many angles to the definition of data science. But the role data science plays in the entire data system can be a great place to start. Data science is the knowledge base experimenting and publishing new methodologies fuelling the data verse’s rapid innovation.
Data scientists first capture raw data with the several data mining tools available today. The type of data considered at this stage varies from one data warehouse to the other. After mining large amounts of data, the next step is to house the volumes in an efficient data warehouse.
Further processing takes place repetitively until the captured data is ready for data analysis. So it’s easy to say data science does all the dirty data work before a dataset can be deemed useful for a data analyst. The data science process is more cyclical than linear. A data scientist’s job never ends. After using data visualization to communicate data to analysts, they harness feedback inputting into the data cycle for another spin.
Many people use data science and data analytics interchangeably. But the definition scope can help differentiate between the two. Data science is an umbrella term comprising various disciplines to mine and prepares data. Data analytics, a sect of the larger process, helps to extract actionable insights to solve problems.
Data science involves efforts that don’t explore data but only lead to it. It provides insights and sometimes context for data professionals to ask the right questions. This direction serves as the basis for the actual exploration to take place using data analytics software. Data analytics helps to answer the questions presented.