Two-thirds of the world population will be online by the end of 2023 and more than 2,500,000,000,000,000,000 bytes (or 2.5 quintillions) of data are created each day. There’s an insane amount of data we can use to support better decision-making. But, without data processing and data analysis, there’s only raw, unstructured data that doesn’t tell a cohesive story.
It’s like Mark Twain said, “Data is like garbage. You’d better know what you are going to do with it before you collect it.”
You need a plan for how you’re going to collect and analyze your data or else it’s useless.
Data analysis is the process of inspecting, cleaning, transforming, and interpreting data to discover meaningful insights, patterns, and trends.
Data analysis helps you make informed choices, solve problems, and identify opportunities for growth.
In 2017, The Economist published an article titled “The world’s most valuable resource is no longer oil, but data.”
It’s true there’s a treasure trove of information in the amount of data being produced. But, a diamond is just a piece of coal unless it’s polished. Data analysis is the process of polishing raw data to make it shine.
Your business collects massive amounts of data daily, ranging from customer feedback and sales figures to website traffic and social media interactions.
Through data analysis, you can glean valuable insights that can drive strategic decisions, enhance efficiency, and improve competitiveness.
These are 5 major benefits of using data analysis in your business:
Data analysis helps you make informed decisions by providing insights into customer behavior, market trends, and operational performance.
By identifying inefficiencies and areas for improvement, data analysis can save your business money through optimized processes.
Analyzing customer data can lead to personalized marketing campaigns, product recommendations, and enhanced customer service.
Data analysis can help you identify and mitigate risks by spotting anomalies or potential issues early on.
Companies that leverage data analysis gain a competitive edge by staying ahead of market shifts and customer preferences.
Data analysis techniques can be broadly categorized into two main types: quantitative analysis and qualitative analysis.
Quantitative analysis deals with numerical data and aims to uncover patterns and relationships within that data.
For example, you would use quantitative analysis to calculate the sales revenue from one of your products.
There are two main branches of quantitative analysis: descriptive and inferential analysis.
Descriptive analysis involves summarizing the visible characteristics of a dataset. This can include creating charts, graphs, and summary statistics to provide an overview of the data’s main characteristics.
Inferential analysis uses statistical techniques to draw conclusions about a population based on a sample of data. It helps in making predictions and testing hypotheses, often with the goal of generalizing findings beyond the sample.
Qualitative analysis, on the other hand, deals with non-numeric data and focuses on understanding the underlying meanings and context.
This type of analysis can take different forms including:
Text analysis involves examining written or spoken content to extract insights. This is widely used in sentiment analysis, content categorization, and natural language processing tasks.
Statistical analysis in qualitative research aims to find patterns and relationships within non-numeric data. Techniques like thematic analysis and content analysis are commonly used in this context.
Diagnostic analysis involves examining data to identify problems or issues. It’s often used to troubleshoot issues in various fields, such as healthcare, manufacturing, and quality control.
Analysts can use whatever methods they prefer to find the answers they’re looking for. But, the data analysis process typically consists of several stages.
Before diving into data analysis, it’s essential to define clear objectives and questions you want to answer. This stage involves setting goals, selecting appropriate data sources, and determining the analytical methods to be employed.
Collecting relevant data is the foundation of any analysis. This may involve gathering data from various sources, including databases, surveys, or sensor readings. Data quality is critical at this stage, as inaccuracies can lead to flawed results.
At this stage, you will clean and organize your data. This includes removing duplicates, handling missing values, and transforming data into a format suitable for analysis. Some companies pay hundreds of thousands of dollars for a data engineer to help prepare your data at this stage. Alternatively, you can use a tool like Pliable to prepare your data without needing to shell out that kind of money.
This is where your chosen analysis techniques are applied to the prepared data. It involves running statistical tests, generating visualizations, or training machine learning models to extract insights.
Interpreting the analysis results is a crucial step. It requires domain knowledge to make sense of the findings and draw actionable conclusions.
Effectively communicating the insights gained from data analysis is essential. Reports, dashboards, presentations, and data visualizations are common methods to convey findings to stakeholders.
A recent survey by NewVantage Partners found that just 23.9% of companies characterize themselves as data-driven, and only 20.6% say they have developed a data culture within their organizations.
In a nutshell, it’s not as easy to adopt a data-driven approach as some would think. That’s why the demand for data analysts is so high.
Companies need a way to turn data into actionable insights. And that’s why data analysis is so important.