Similar to any other scientific field, data analysis is a systematic, rigorous process. Every step calls for a new set of skills. To gain useful insights, however, it is necessary to comprehend the process as a whole. Producing outcomes that withstand examination requires a solid foundation.
This post delves into the primary steps involved in the data analysis process. It covers aspects such as goal definition, data collection, and the execution of analysis.
Today’s businesses require every advantage and edge possible. Businesses today are operating with smaller margins of error due to challenges including rapidly changing markets, economic uncertainties, shifting political landscapes, finicky customer attitudes, and even worldwide pandemics.
Making wise decisions while addressing the issue, “What is data analysis?” will increase a company’s chances of survival and growth. And how do people or organizations come to these decisions? They gather as much practical, relevant data as they can, then use it to guide their decisions!
Data analysis involves the systematic process of refining and assessing raw data to extract valuable information and insights. A data analyst plays a crucial role in this process, involving tasks such as discovery, collection, storage, cleaning, and analysis of data through the utilization of automated data analytics tools. The analyst then examines the outcomes derived from the data, generating insights that can be applied to enhance the operations of a business or any other institution.
The applications and advantages of data analysis for businesses are extensive. Notably, it has the potential to increase profits by up to 6% and reduce costs by as much as 10%. The benefits of data analysis are not limited to businesses alone; any organization or institution that generates data stands to gain from this practice. The undeniable advantages of data analysis are reflected in the anticipated 71% increase in global enterprises’ investments in data analytics.
Every time we make a decision in our daily lives, we may see a basic example of data analysis in action by looking at what has happened in the past or what will happen if we make that choice. To put it simply, this is the process of conducting an analysis of the past, or future, followed by a conclusion.
Data Analysis process
The data analysis process is a systematic series of steps aimed at gathering, processing, exploring, and deriving insights from information.
Gathering all the information, processing it, examining it, and applying it to identify patterns and other insights are all part of the data analysis process, also known as the data analysis steps.
The steps involved in data analysis are as follows:
1. Identifying the Problem Statement and Objectives
Begin by identifying the insights you aim to derive from data analysis. The process revolves around solving specific, pre-identified problems, and the first step involves setting a clear objective. This objective is framed as a question, known as a problem statement, tailored to address the business’s needs or goals.
2. Data Collection
Collect data from various sources, such as case studies, interviews, surveys, questionnaires, and focus groups and direct observation. Ensure that the collected data aligns with the identified requirements and organize it systematically for analysis.
Select relevant data aligned with the established problem statement.
This involves three categories of specific data:
- First-party data: Directly collected from customers by the company.
- Second-party data: Another company’s first-party data, expanding analysis options.
- Third-party data: Large-scale data collected from various sources, known as big data.
First-party data is directly collected from customers by the company. Various data collection techniques, including direct customer interviews or observations, contribute to gathering this information. However, a significant portion of first-party data is typically sourced from the company’s Customer Relationship Management (CRM) system and other digital tools employed to track transactional data.
Second-party data refers to another company’s first-party data. While not as directly relevant as the first-party data owned by a specific company, it broadens analysis options and insights. Obtaining second-party data can be done either directly from the company holding the data or through a vendor in a private marketplace.
Third-party data, often termed as big data, is sourced from diverse external origins. This data is collected from various sources, including vendors like Gartner or public repositories such as government databases. Third-party data can exist in structured or unstructured formats, providing a vast pool of information for analysis.
3. Data Cleaning
Prepare the data for analysis by cleaning and refining it. Eliminate unnecessary elements, such as white spaces, duplicate records, and basic errors. Data cleaning is a crucial step to ensure the accuracy and reliability of the information.
Data cleaning is a critical step that enhances data quality. This involves:
- Structuring unstructured data by addressing typos and layout issues.
- Filling gaps in crucial data points.
- Removing irrelevant data points, duplicates, outliers, and errors.
Data cleaning is a critical phase in the data analysis process, consuming a substantial portion of analysts’ time, often up to 90%. The rationale behind this extensive commitment to data cleaning lies in the fundamental principle that the outcomes of data analysis heavily hinge on the quality of the data under examination.
Key aspects of data cleaning include:
Converting unstructured data
This involves implementing solutions to address issues like typos and problems with the layout. Converting unstructured data into a well-organized format is vital for accurate analysis.
Filling in Major Data Gaps
Identifying and filling in significant gaps in the dataset where crucial information is missing is crucial. This step ensures a comprehensive dataset that aligns with the analysis objectives.
Eliminating data points
Eliminating data points that are irrelevant to the defined problem statement or objective is essential. This streamlines the dataset, focusing the analysis on pertinent information.
Eliminating Duplicates, Outliers, and Errors:
The process involves removing duplicate entries, outliers, and other major errors in the dataset. This ensures the integrity and accuracy of the data, preventing distortions in the analysis results.
4. Data Analysis
Use data analysis software and tools, such as Excel, Python, Looker, Chartio, R, Rapid Miner, Metabase, Redash, or Microsoft Power BI, to interpret and understand the data. Apply statistical methods and algorithms to uncover patterns, trends, and meaningful insights.
The primary phase of data analysis can now begin with the cleaned data. Depending on the goal and the type of data, you can employ a variety of data analytics techniques. There are numerous data analysis techniques, and they all fall into one of four categories:
Descriptive analysis provides insights into past occurrences. Taking the earlier example of a business grappling with customer retention, this analysis may reveal details such as the number of leads captured and the bounce rate. Its purpose is to offer a clearer understanding of the problem statement and the overall situation.
A diagnostic analysis is used to explain a phenomenon. Using the same example, it will attempt to explain why consumers visit and connect with the business but leave before making a purchase (or after a few transactions). Essential insights are provided by diagnostic analysis in order to resolve the problem and achieve the objective.
Predictive analysis uses previous data to provide insights about what is most likely to occur in the future. For instance, it can be employed to forecast the potential loss of customers if the business fails to enhance its marketing tactics or predict the growth the business could experience with improved marketing strategies.
Prescriptive analysis provides recommendations for future actions based on insights obtained from other analyses. Ideally, it should offer actionable solutions to achieve predetermined objectives. In the context of the original question, it addresses how the business can attain better results in its digital marketing campaigns.
5. Data Interpretation
The ultimate goal of data analysis is to assist the business in achieving its goals and provide solutions to its problems. The necessary solutions and insights have been obtained through data analysis, but the task is not yet finished. Even while the information seems clear to you, someone without experience with data analysis may find it confusing; an analyst should evaluate it.
A variety of tools are available for the interpretation and visualization of data. The best tools for presenting your conclusions are dashboards, reports, and interactive visualizations. It’s a good idea to prepare well in case you have to explain your findings and respond to inquiries from the audience during the presentation.
It is important to remember that data analysis is a key component of business decision-making. This implies that the business’s future plans will greatly benefit from the results and presentation. There is no place for mistakes at any stage of the process because false information will lead to poor decisions that might be extremely costly for the company.
6. Data Visualization
Data visualization is a sophisticated way of expressing the idea of presenting information graphically so that it is easily readable and understandable for people. It involves the use of charts, graphs, maps, bullet points, and various other methods to visually represent data. The purpose of visualization is to extract valuable insights by facilitating the comparison of datasets and the observation of relationships.
data analysis methods
While there are numerous data analysis methods, they can broadly be categorized into two primary types: qualitative analysis and quantitative analysis.
Qualitative Data Analysis
The qualitative data analysis method involves extracting insights from words, symbols, pictures, and observations without the use of statistical measures.
Common qualitative methods include:
- Content Analysis: Examining behavioral and verbal data to identify meaningful patterns
- Narrative Analysis: Analyzing data derived from interviews, diaries, and surveys to interpret and understand narratives
- Grounded Theory: Formulating causal explanations of events by studying and extrapolating from one or more past cases
Quantitative Data Analysis
Statistical data analysis methods are another name for techniques that gather raw data and turn it into numerical data.
Quantitative analysis methods include:
- Hypothesis Testing: Evaluating the validity of a hypothesis or theory for a given dataset or demographic through statistical analysis.
- Mean (Average): Determining an overall trend in a subject by dividing the sum of a list of numbers by the number of items on the list.
- Sample Size Determination: Analyzing a small sample from a larger group to obtain results considered representative of the entire population
Data Analysis Techniques: A Comprehensive Guide
To conduct effective data analysis, employing various techniques is crucial. Here are some techniques to consider:
- Identify your objectives: Clearly state the objectives for why you are doing this data analysis. Recognise the answers or insights you hope to gain from your questions. This provides the analysis process with a foundation for guidance.
- Data Cleaning: Initiate the process by cleaning the data to ensure quality and reliability. Eliminate duplicates, address missing values, and rectify errors or inconsistencies. Data cleaning is fundamental for accurate analysis.
- Machine Learning Algorithms: Harness machine learning algorithms for data analysis, predictions, or classifications. Select algorithms based on data nature and problem-solving objectives. Train models with historical data and evaluate their performance on new data.
- Descriptive Statistics: Compute descriptive statistics in order to understand the main characteristics of the data. Calculate percentiles, standard deviation, mode, mean, median, and other metrics. These statistics include information about distribution, spread, and central tendency.
- Exploratory Data Analysis (EDA): Employ EDA techniques for in-depth data exploration. Utilize summary statistics, data profiling and visual exploration to identify relationships, patterns, or notable features. EDA guides hypothesis generation and subsequent analysis.
- Text Mining and NLP: Use text mining and natural language processing techniques for textual data. To get insights from unstructured text data, do entity recognition, sentiment analysis, topic extraction, and text classification.
- Inferential Statistics: Use inferential statistics to draw conclusions from sample data to the wider population. To test for relationships, make predictions, or determine significance, apply methods like regression analysis, confidence intervals, and hypothesis testing.
- Segmentation and Clustering: To find clusters or segments in the data, apply techniques for clustering. Understanding patterns or similarities between data points is made easier with the help of clustering, which is useful for market analysis, customer segmentation, and anomaly detection.
- Data Visualization: Generate visual representations using graphs, charts, or plots. Visualization aids in identifying trends, patterns, or outliers not immediately apparent in raw data. Choose appropriate visualizations based on data type and intended insights.
- Time Series Analysis: Make use of time series analysis techniques if the data is time-dependent. Analyse patterns, seasonality, and trends to predict values in the future or spot underlying cycles.
What Makes Data Analysis Essential?
Data analysis holds paramount importance in business for several compelling reasons:
1. Better Customer Targeting: Efficient data analysis ensures that businesses do not waste valuable time and resources on advertising campaigns aimed at demographic groups with little to no interest in their products or services. It enables businesses to pinpoint where they should concentrate their advertising and marketing efforts.
2. Deeper Understanding of Target Customers: Data analysis tracks the performance of products and campaigns within the target demographic, offering businesses valuable insights into the spending habits, disposable income, and areas of interest of their audience. This data aids in setting prices, determining ad campaign lengths, and projecting the quantity of goods needed.
3. Reduction of Operational Costs: By revealing which areas in a business require more resources and investment and which areas are underperforming, data analysis guides informed decisions about resource allocation. This, in turn, allows businesses to streamline operations, potentially reducing operational costs.
4. Enhanced Problem-Solving Methods: Informed decisions are inherently more likely to be successful. Data analysis provides businesses with crucial information, enabling them to foresee trends and make sound choices. This process facilitates effective problem-solving and helps businesses avoid costly pitfalls.
5. Acquisition of More Accurate Data: For making informed decisions, accurate data is paramount. Data analysis plays a crucial role in helping businesses acquire relevant and precise information. This accurate data is crucial for developing future marketing strategies, crafting business plans, and realigning the company’s vision or mission.