Getting Started with Data Analysis

Lots of information is being gathered on a daily basis.

Doing the right thing with this information and data is very important, as it shapes future decisions.

That is what Data Analysis is about.

Processing and evaluating raw data to draw conclusions from the information that you have.

A lot of techniques and data analytics processes have been automated into algorithms that work on raw data for human use.

Data analytics can be used to reveal trends and that would normally be lost in the mass of information. This information can then be used to develop processes used to increase the overall efficiency of a business or a system.

Data analytics can use mathematical analysis, optimization, inductive statistics and concepts from nonlinear system identification to derive basic laws (regressions, causal effects, nonlinear relationships) from large sets of data with low information density to help with predictions of outcomes and behaviors.

Using statistical tools in understanding and using the insights gathered from large information sets can help businesses gain competitive advantage.

The Data Analysis Process.

Determine the data requirements or how the data is grouped. It may by age or gender or demographic income.

Collect the data. This can be done through different sources such as computers, online sources and so on.

Organize the data. This organization can be on a spreadsheet or any other software that can take statistical data.

Clean up the data. This simply means checking for duplication errors.

Types of Data Analysis.

Descriptive analytics: This describes past occurrences over a given period of time to find out if the number of views gone up or if sales are stronger this month than the last?

Diagnostic analytics: This focuses more on why an incident has occurred for example did the weather affect beer sales or did the last marketing campaign have an effect on our sales?

Predictive analytics: This focuses on what is likely going to happen in the near term for example what happened to sales the last time we had a hot summer?

Prescriptive analytics: This suggests the next action to be taken.

Applications of Data Analytics

Internet of Things (IoT): Data Analytics and the IoT work in conjunction with each other. Data gotten from IoT devices connects devices to one another. This information has been used by the media industry, companies and governments to target their audience more accurately and increase media efficiency. The IoT is also used to gather sensory data, and this sensory data has been used in medical, manufacturing and transportation contexts.

Information technology: Especially since 2015, Data analytics has been helping business operations and employees to work more efficiently and streamline the collection and distribution of information technology (IT). The use of data analytics/big data to resolve IT and data collection issues within an enterprise is called IT operations analytics (ITOA). If IT departments apply data analytics principles into the concepts of machine intelligence and deep computing, they can predict upcoming issues and get solutions ready before the problems even happen.

Agriculture: Big data helps farmers with granular data on rainfall patterns, water cycles, fertilizer requirements, and more. This enables them to make better decisions, such as what crops to plant for better profitability and when to harvest. The right decisions ultimately improve their farm yields.

Written by: Oyindamola Ezekiel.