The buzzword “Data Analytics” has been all around for decades now. It is the science of examining raw data or data sets in order to draw conclusions. Each business enterprise generates and gather enormous amount of data that is needed to extract meaningful insights and correlations. It is a broad term that involves using specialized software tools and applications for data warehousing, predictive analytics, data mining, text mining, forecasting and data optimization.
Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of data. This information can then be used to optimize processes to increase the overall efficiency of a business or system.
Using big data, Netflix saves $1 billion per year on customer retention.
So why is data so important in day-to-day life?
- It can gather hidden insights and analyze and improve business requirements which also helps in enhancing customer relations.
- It can perform market analysis which enables understanding the competitors weak and strong points.
- With the help of generated trends and reports that forecasts the need of important actions to be taken by a business to grow and hence improves decision making.
In 2020, every person will generate 1.7 megabytes of data in just a second.
The 5 Steps of Data Analysis Process
Data analytics has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. The process involves several different steps:
- Define data requirements: The first step is to create a questionnaire based on the data requirement. Data may be separated by age, demographic, income, or gender. It may have numerical values or can be divided by category. Some relevant questions may be, “Are we overpricing our goods?” or “How is the competitor’s product different from ours?”. To answer these, information needs to be gathered from customers which will vary depending on the questions.
- Data collection: The next step in involves the process of collecting it. This can be done through a variety of sources such as computers, online sources, social media, recording devices or cameras, environmental sources, or even personally.
- Data processing: Now that we know what data is to be collected and how it will be collected, it must be organized for analysis. It can be done in tabular forms using spreadsheets or other forms of statistical software.
- Data cleaning: Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. It is brushed up to ensure no discrepancy remains before it goes on to an analyst for further processing.
- Data analysis and interpretation: Once analysts gets clean data, they begin to understand the messages contained in the data. The process of exploration may result in additional data cleaning or requests for additional data, which makes these activities iterative.
Types of Data Analytics
Data analytics can be divided into four basic types:
Descriptive analytics indicates what has happened over a given period of time say, if the number of views has increased or is the sales number higher than previous month?
Diagnostic analytics questions on why something happened. This focuses on diverse data inputs and a bit of hypothesis like, is the umbrella sales affected by weather?
Predictive analytics asks if something is likely to happen in the near future. For instance, how was umbrella sales impacted the last time we had a cold winter?
Prescriptive analytics suggests a course of action. Taking the above example, if likelihood of rain when measured as an average of all-weather models is above some number, then the umbrella production should increase by some number.
Know more how Data Analytics help in cost optimization.