Data Analytics deals with leveraging data to derive meaningful information. The process of Data Analytics primarily involves collecting and organizing Big Data to extract valuable insights, thereby increasing the overall efficiency of business processes.
Data Analysts work with various tools and frameworks to draw lucrative insights.An analyst will focus on how you collect, process, and organize data in order to create actionable results.A data analyst will also find the most appropriate way to present the data in a clear and understandable way. With Data Analysis, organizations are able to take initiatives to respond quickly to emerging market trends; as a result, increase revenue.
Why Data Analytics is Important?
Implementing Data Analytics in various industries can optimize efficiency and workflow. The financial sector is one of the earliest sectors to adopt Data Analytics in banking and finance. For example, Data Analytics is used in calculating the credit score of a person because it takes many factors into consideration for determining the lending risks.Moreover, it helps to predict the market trends and assess risks.
Data Analytics is not limited to focusing on more profits and ROI. It can also be used in the healthcare industry, crime prevention, etc. It uses statistics and advanced analytical techniques to generate valuable insights from the data and help businesses in making better data-driven decisions. Data analytics looks more at statistics and the kinds of data analysis used to connect diverse data sources and trying to find connections between the results.
Data Analysts will focus on concrete problem solving and question answering which can then lead to immediately improving the situation. Initiatives off the back of data analysis can help you to increase revenues, improve operational efficiency, and optimize efforts and campaigns. They allow you to get insight into market trends and help you to get a competitive edge over rivals.
Data Analytics involves more than just analyzing data. Most of the required work takes place upfront while preparing datasets. To perform Data Analysis, the following steps are involved in the process:
1) Data Collection
Data Collection forms the foundation of all other processes, thus it must strictly adhere to the business goals. Data can be collected by Web Scraping, Querying Databases, or using Big Data tools.Although a huge volume of data is available today, only relevant data can provide the right insights to achieve business goals. In current scenarios, data is not restricted to Structured but also Unstructured formats such as text, images, and videos that are later partitioned for further Data Analysis.
2) Data Processing
Data collected is usually unstructured, inherited with few missing values, making it difficult to analyze. Data Processing maintains data quality by cleaning and transforming data into desired forms. This process involves imputation techniques to tackle null values and outliers along with various transformation methods to distribute data uniformly.
3) Data Analysis
Data Analysis is carried out using various software and tools to understand data and arrive at conclusions. Data Analysis requires prerequisite skills to create statistical data models such as Correlation and Regression Analysis to examine relationships. This process may involve Data Visualization tools to identify Linear, continuous characteristics inhibited within features.
4) Data Interpretation
Data Interpretation is the process of reviewing data through predefined processes to arrive at an informed conclusion. Data Interpretation helps Data Analysts make the audience understand the significance of numerical data by creating interactive Dashboards and Reports. This process uncovers facts and trends to find out meaningful information.