Data Analysis as a career can help businesses transform by propelling them towards success in the information age.

What is data Analysis?

A data analyst examines data sets to reveal patterns, relationships and predict trends that help organisations make better decisions regarding their current and potential customer base. The role typically includes collecting, cleaning and making sense of data sets to answer questions provided by the business. Gathering insights from data helps businesses solve problems that are glaring at them or even hiding in the shadows. It also helps with informed decision-making and helps to bring improvements to the organisation overall.

The Data Analysis Process

First, one must need to understand the business needs to help with the definition a question that needs to be answered using data. Identifying the root cause of an issue is a good place to start.

Data Collection:

The next process involves collecting the data that you will eventually analyse for answers. This data can come from surveys, marketing data or even customer interviews. First party data is data collected by the organisation itself. Second party data comes from an organisation used by the main business and third party data can come from multiple other sources.

Data Cleaning:

Raw data is messy and requires a lot of transformation before it can be deemed useful. Data must also be restructured to make it make sense. Gaps need to be filled to ensure data accuracy. Accurate data provides better insights.

Data Validation:

This happens once the data is cleaned and made usable. This involves verifying whether it meets certain requirements before any analysis can be done.

Data Analysis:

This happens in in four major formats

• What happened? – Descriptive
• Why did it happen? – Diagnostic
• What will happen? – Predictive
• What actions can be taken? – Prescriptive

Techniques

Predictive models are developed using statistical techniques to help with optimising processes. Techniques such as regression analysis, time series analysis, cluster analysis, classification analysis, text analysis (natural language processing), data mining, among others come in handy at this stage.

Visualising insights allows stakeholders to easily understand them while also seeing patterns and trends from the data analysed. Tools such as Tableau, Power BI and python libraries like Matplotlib are useful for this task.

Skills

Data analysis involves proficiency in the use of tools such as Excel, Python, R and SQL to help query and analyse the data.