What is Data Analytics?
The process of examining data sets in order to draw conclusions about the information they contain is known as Data analytics (DA). Data Analytics is carried out increasingly with the aid of specialized systems and software by the boutique digital marketing agency all over the world. The qualitative and quantitative techniques of data analytics are widely used in commercial industries to enable organizations make success driven business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.
Data analytics refers to an array of applications, from basic business intelligence, reporting and online analytical processing (OLAP) to various forms of advanced analytics. Data analytics are primarily conducted in business-to-consumer (B2C) applications. Global organizations collect and analyze data associated with customers, business processes, market economics or practical experience. Data is categorized, stored and analyzed to study purchasing trends and patterns.
Data analytics can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, quick responses to emerging market trends and to always be on top. Depending on the particular application, the data that’s analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. Along with this, it can come from a mix of internal systems and external data sources.
Data Analytics Applications
Data analytics include confirmatory data analysis (CDA), which uses statistical techniques to determine whether hypotheses about a data set are true and exploratory data analysis (EDA), which works to find patterns and relationships in data.
Data analytics can also be segmented into quantitative data analysis and qualitative data analysis. The qualitative part involves understanding of the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view. The quantitative part includes analysis of numerical data with variables that can be compared or measured statistically.
When it comes to application, performing data analytics provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. Unlike in the past when data queries and reports were created for end users by business intelligence developers working in IT or for a centralized team; organizations now increasingly use self-service BI tools that let execs, business analysts and operational workers run their own ad hoc queries and build reports themselves.
Data analytics also includes:
- Data mining – which involves analyzing through large data sets to identify trends, patterns and relationships
- Predictive analytics – which seeks to predict customer behavior, equipment failures and other future events
- Machine learning – an artificial intelligence technique that uses automated algorithms to go through data sets more quickly than data scientists can do via conventional analytical modelling.
Big data analytics applies data mining, predictive analytics and machine learning tools to sets of big data that often contain unstructured and semi-structured data. Text mining provides a means of analyzing documents, emails and other text-based content.
The working mechanism
Data analytics applications involve more than just analyzing data. Typically, on advanced analytics projects, much of the required work takes place up front, in collecting, integrating and preparing data and then developing, testing and revising analytical models to ensure that they produce accurate results. In addition to data scientists and other data analysts, analytics teams often include data engineers, who help get data sets ready for analysis.
The analytics process starts with data collection, in which data, scientists identify the information they need for a particular analytics application and then work on their own or with data engineers and IT staffers to assemble it for use. Data from different source systems may need to be combined via data integration routines, transformed into a common format and loaded into an analytics system, such as a Hadoop cluster, NoSQL database or data warehouse. In other cases, the collection process may consist of pulling a relevant subset out of a stream of raw data that flows into, say, Hadoop and moving it to a separate partition in the system so it can be analyzed without affecting the overall data set.
Once the data needed is extracted, the next step is to find and fix data quality problems that could affect the accuracy of analytics applications. This includes running data profiling and data cleansing jobs to make sure that the information in a data set is consistent and that errors and duplicate entries are eliminated. Additional data preparation work is then done to manipulate and organize the data for the planned analytics use, and data governance policies are applied to ensure that the data hews to corporate standards and is being used properly.
A data scientist builds an analytical model, using predictive modelling tools or other analytics software and programming languages such as Python, Scala, R and SQL. The model is initially run against a partial data set to test its accuracy; typically, it’s then revised and tested again, a process known as “training” the model that continues until it functions as intended. Finally, the model is run in production mode against the full data set, something that can be done once to address a specific information need or on an ongoing basis as the data is updated.
The last step in the data analytics process followed by most of the digital marketing company in USA is communicating the results generated by analytical models for business executives and other end users to aid in their decision-making.
This is done with the help of data visualization techniques, which analytics teams use to create charts and other infographics designed to make their findings easier to understand. Data visualizations often are incorporated into BI dashboard applications that display data on a single screen and can be updated in real time as new information becomes available.