A quick view for Data Analytics

Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things.

Types of Data Analytics

Data analytics is broken down into four basic types.

Descriptive analytics describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last?

Diagnostic analytics focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales?

Predictive analytics moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year?

Prescriptive analytics suggests a course of action. If the likelihood of a hot summer is measured as an average of these five weather models is above 58%, we should add an evening shift to the brewery and rent an additional tank to increase output.

Data Analytics Process

The process involved in data analysis involves several different steps:

The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or be divided by category.

The second step in data analytics is the process of collecting it. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources.

Once the data is collected, it must be organized so it can be analyzed. The organization may take place on a spreadsheet or other form of software that can take statistical data.

The data is then cleaned up before analysis. This means it is scrubbed and checked to ensure there is no duplication or error, and that it is not incomplete. This step helps correct any errors before it goes on to a data analyst to be analyzed.

Data Analytics Roles

Data Analyst

Needs SQL experience; needs to be able to summarize results in Excel using tables, charts, and graphs.

Data Engineer

Works with the Data Scientist to build pipelines of transformations required to run analytical jobs

Data Architect

Data architects define how the data will be stored, consumed, integrated and managed by different data entities and IT systems, as well as any applications using or processing that data in some way

Data Scientist

Works with both Data Analyst and Engineer to design workflows and analytical jobs, align work with specific use cases, plan goals, interpret and measure results and continually improve processes