Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, while being used in different business, science, and social science domains.
The process of evaluating data using analytical and logical reasoning to examine each component of the data provided. This form of analysis is just one of the many steps that must be completed when conducting a research experiment. Data from various sources is gathered, reviewed, and then analyzed to form some sort of finding or conclusion. There are a variety of specific data analysis method, some of which include data mining, text analytics, business intelligence, and data visualizations.Data analysis is a primary component of data mining and Business Intelligence (BI) and is key to gaining the insight that drives business decisions. Organizations and enterprises analyze data from a multitude of sources using Big Data management solutions and customer experience management solutions that utilize data analysis to transform data into actionable insights.
Data Analytics Process
Now in Big Data Analytics Tutorial we are going to see the analytic process or how analyzing data can be done?
a. Business Understanding
The very first step consists of business understanding. Whenever any requirement occurs, firstly we need to determine business objective, assess the situation, determine data mining goals and then produce the project plan as per the requirement. Business objectives are defined in this phase.
b. Data Exploration
Second step consists of Data understanding. For further process, we need to gather initial data, describe and explore the data and verify data quality to ensure it contains the data we require. Data collected from the various sources is described in terms of its application and need for the project in this phase. This is also known as data exploration. This is necessary to verify the quality of data collected.
c. Data Preparation
Next come Data preparation. From the data collected in last step, we need to select data as per the need, clean it, construct it to get useful information and then integrate it all. Finally we need to format the data to get appropriate data. Data is selected, cleaned, and integrated in the format finalized for the analysis in this phase.
d. Data Modeling
Once data is gathered, we need to do data modeling. For this, we need to select modeling technique, generate test design, build model and assess the model built. Data model is build to analyze relationships between various selected objects in the data, test cases are built for assessing the model and model is tested and implemented on the data in this phase.
e. Data Evaluation
Next come data evaluation where we evaluate the results generated in last step, review the scope of error and determine next steps that need to be performed. Results of the test cases are evaluated and reviewed for the scope of error in this phase.
Final step in analytic process is deployment. Here we need to plan the deployment and monitoring and maintenance, we need to produce final report and review the project. Results of the analysis are deployed in this phase. This is also known as reviewing of the project.
The complete above process is known as business analytics process.
Characteristics of Big Data Analysis
Characteristics of Big Data Analytics which make it different from traditional kind of analysis.
Concurrently selecting data collection methods and appropriate analysis
While methods of analysis may differ by scientific discipline, the optimal stage for determining appropriate analytic procedures occurs early in the research process and should not be an afterthought. According to Smeeton and Goda (2003), “Statistical advice should be obtained at the stage of initial planning of an investigation so that, for example, the method of sampling and design of questionnaire are appropriate”.
Data Analysis Model
Gwen Shapira, a solutions architect at Cloudera and an Oracle ACE Director, outlines seven key steps of data analysis for Oracle’s Profit magazine. Shapira explains that while each company has its own data requirements and goals, there are seven steps that remain consistent across organizations and their data analysis processes:
- Decide on the objectives – Determine objectives for data science teams to develop a quantifiable way to determine whether the business is progressing toward its goals; identify metrics or performance indicators early
- Identify business levers – Identify goals, metrics, and levers early in data analysis projects to give scope and focus to data analysis; this means the business should be willing to make changes to improve its key metrics and reach its goals as well
- Data collection – Gather as much data from diverse sources as possible in order to build better models and gain more actionable insights
- Data cleaning – Improve data quality to generate the right results and avoid making incorrect conclusions; automate the process but involve employees to oversee the data cleaning and ensure accuracy
- Grow a data science team – Include on your science team individuals with advanced degrees in statistics who will focus on data modeling and predictions, as well as infrastructure engineers, software developers, and ETL experts; then, give the team the large-scale data analysis platforms they need to automate data collection and analysis
- Optimize and repeat – Perfect your data analysis model so you can repeat the process to generate accurate predictions, reach goals, and monitor and report consistently