The examination of unprocessed data to extract valuable insights is known as data analytics. The optimal course of action is then determined based on these observations. When would be the best time to launch this marketing campaign? Is the current structure of the team as effective as it could be? Which customer groups are the most likely to buy your new product?
Finally, data analytics is an essential component of any successful business strategy. However, how do data analysts precisely transform raw data into useful information? Data analysts use various methods and techniques depending on the type of data and the insights they want to uncover. You can learn about the application of data analytics in this brief, free course.
This post will look at some of the most valuable data analysis techniques. You’ll have a much better understanding of transforming irrelevant data into business intelligence by the end.
What Is Data Analysis, and Why Is It Important?
The process of analyzing data to discover relevant information is known as data analysis. To accomplish this, data must be analyzed, cleansed, transformed, discussed, and modeled using analytical and statistical tools, which are further in this article.
What is the significance of data analysis? Practical data analysis aids business decision-making in organizations. Businesses now collect data continuously through various channels, including surveys, online tracking, online marketing analytics, subscription and registration data (think newsletters), and social media monitoring.
These data will manifest in a variety of structures, including but not limited to:
Big data
The concept of big data gained traction in the early 2000s. Big data is defined as data that is so large, fast, or complex that it is difficult or impossible to process using traditional methods. According to industry analyst Doug Laney, the three Vs.—volume, velocity, and variety—have since become the standard definition of big data.
Volume
Businesses, as previously stated, constantly collect data. It would not have been easy to store items in the not-too-distant past, but today’s storage is inexpensive and takes up very little space.
Velocity
The processing of received data necessitates a quick response. This data may be arriving continuously and at an unprecedented rate as the Internet of Things expands.
Variety
Organizations collect and store a wide range of data, including unstructured data (emails, videos, and audio) and structured data (spreadsheets and databases) (more conventional, numerical data). Later, discuss both structured and unstructured data.
Metadata
This data type includes details about other data types, such as images. You can find out by right-clicking on a file in a folder and selecting “Get Info,” which will display information like the file’s size, type, creation date, and other details.
Live Data
This information is made available as soon as it is collected. A stock market ticket that provides real-time information on the most active stocks is a good example.
Automated Data
This data was generated entirely by computers, with no human intervention. As an example, consider the call logs that your smartphone automatically generates.
Quantitative and Qualitative Data
Structured data, also known as quantitative data, can take the form of a “traditional” database with rows and columns. Other types of data, also known as unstructured data, include text, images, videos, and other forms of qualitative data analytics services that do not fit into rows and columns. We will go into greater detail in the following section.
Quantitative vs. Qualitative Data
Certify Quantitative data is information that consists of precise quantities and numbers. Sales figures, email click-through rates, website visitor numbers, and percentage revenue growth are all examples of quantitative data.
The statistical, mathematical, or numerical analysis of (typically large) datasets is the focus of quantitative data analysis techniques. It includes using computational methods and algorithms to manipulate statistical data. Numerous phenomena can be predicted or explained using quantitative analysis methods.
Because measuring qualitative data objectively, they are subject to more subjective interpretation. Product reviews, comments in response to survey questions, statements made during interviews, tweets, and other social media posts are examples of qualitative data. The goal of qualitative data analysis is to interpret unstructured data (such as written text or transcripts of spoken conversations).
Qualitative analysis will frequently categorize data into themes; thankfully, it automates this process. Data analysts must be familiar with various analysis methods because they work with quantitative and qualitative data. Let us now look at the most effective strategies.
Data Analysis Techniques
Now that we’ve covered some of the different data types let’s get into the meat of the matter: data analysis techniques.
Regression Analysis
Regression analysis is used to discover the relationship between a set of variables. A regression analysis examines the relationship between a dependent variable and any independent variables (factors that may impact the dependent variable).
Regression analysis seeks to identify trends and patterns by estimating the possible effects of one or more variables on the dependent variable. It is beneficial for forecasting future events and trends.
Assume you work for an e-commerce company and want to look into the relationship between (a) the amount spent on social media marketing and (b) sales revenue. You are most concerned in this scenario with predicting and increasing sales revenue; it is your dependent variable.
You want to know if social media spending affects sales and if increasing, decreasing, or maintaining it is beneficial. You could use regression analysis to see if there is a relationship between the two variables.
Monte Carlo Simulation
When making decisions or taking specific actions, there are numerous possible outcomes. Taking the bus may result in traffic congestion. If you choose to walk, you risk getting caught in the rain or running into your chatty neighbor, both of which could cause your journey to be delayed. When the stakes are high, it’s essential to accurately and thoroughly estimate risks and benefits.
The Monte Carlo method, also known as Monte Carlo simulation, is a computer-based method for generating models of potential outcomes and their probability distributions. It essentially determines the likelihood of each specific outcome after considering a variety of potential outcomes. Data analysts use the Monte Carlo method to perform an advanced risk analysis to predict future events better and take appropriate action.
Factor Analysis
Reduce a large number of variables to a manageable number using the factor analysis technique. Predict It on the assumption that numerous distinct observable variables correlate with one another because they are all linked to a common construct. It aids in discovering hidden patterns and divides large datasets into smaller, more manageable samples. It enables you to investigate concepts challenging to quantify or observe, such as wealth, happiness, fitness, or customer loyalty and satisfaction, for a practical example in business.
Cohort Analysis
According to Wikipedia, cohort analysis is “a subset of behavioral analytics that divides data from a given dataset into related groups for analysis, rather than viewing all users as a single entity.” These related groups, or cohorts, frequently share similar characteristics or experiences over a given period.
What does this mean, and why is it important? Let’s take a closer look at the definition above. A cohort is a group of people who exhibit similar behavior over a specific period. Referred students who started college in 2020 to the 2020 cohort. Customers who purchased app-based items from considering your online store in December a cohort.
Cluster Analysis
Cluster analysis is an exploratory technique for detecting structures in a dataset. The goal of cluster analysis is to organize disparate data points into groups (or clusters) that are internally homogeneous and externally heterogeneous.
It means that data points in one cluster are distinct and comparable to another. Clustering can preprocess data for other algorithms or gain insight into data distribution within a given dataset.
Time Series Analysis
Time series analysis is a statistical technique that can identify long-term trends and cycles. A time series is a collection of data points that track the same variable over time (for example, weekly sales figures or monthly email sign-ups). Analysts can forecast future changes in the variable of interest by observing temporal tendencies.
Sentiment Analysis
When most people think of data, they think of numbers and spreadsheets. Many businesses undervalue qualitative data, but what people say and write about you, mainly clients can provide countless insights. As a result, what is the process for analyzing textual data?
Key Takeaways
You have access to a wide range of data analysis methods. It is critical to consider the type of data you have (qualitative or quantitative). To turn raw data into actionable insights, consider the types of insights that will be useful in the specific context. Although this post has covered seven of the most valuable data analysis techniques, there are many more to learn!
More so, ICT Distribution is the way to go if you’re looking to collaborate with industry professionals in the field of information technology. Get in touch with our group of expert consultants for objective guidance on the solutions your company needs.