Almost everything you do online produces data, from a quick Google search to liking a TikTok—a fact only exacerbated by the ubiquity of smartphones and smart home devices. Businesses that learn to analyze this data and use it to make strategic decisions hold distinct advantages over their competition. Data analysis can improve efficiency and productivity, long-term decision-making, customer retention and loyalty, measurement of campaign ROI, and more.
The first step in reaping these benefits is learning about the data analysis process. Here’s where to start.
What is data analysis?
In business, data analysis is the collection and interpretation of facts, quantifiable information, and feedback to help a company make strategic decisions. Organizations that apply data analysis systematically can make more informed decisions, uncover patterns and trends, identify areas for improvement, optimize processes, and, ultimately, gain a competitive edge.
By analyzing data, decision-makers can lean on solid numbers to back up, inform, flesh out, and keep a check on the human tendency to rely on intuition. Data analysis doesn’t automate decision-making or even tell business managers what decision to make. Instead, it augments and shapes the process of making a decision with credible, real-world information.
How data analysis can impact your business
Enterprises that invest in data analysis can use data to:
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Make strategic decisions. Data analysis can help predict income, determine where to invest capital, and detect growth opportunities. For example, organic search data might suggest high demand for a new product, increasing sales estimates.
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Reduce costs. Data analysis can help spot opportunities for improvement and streamlining throughout the organization. For instance, if a company’s video production initiative fails to achieve the desired metrics, analyzing the data can indicate the need to reallocate resources to more promising projects.
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Target customers. Data analysis can enable a deeper understanding of your customer’s behavior, channel preferences, demographics, and broader interests. You can leverage this information to refine marketing campaigns. For instance, if analysis reveals a younger customer base than expected, this knowledge can guide product design and inform the selection of appropriate marketing channels.
Understanding the general data analysis process can help manage expectations about what it can do. The standard five-phase process for data analysis is:
1. Identify
The first step is to determine what question(s) the data will help you answer. Say you run a growing online shoe store. Perhaps you want to answer questions related to customers—who are they, how often do they buy, how much do they spend, where do they live, and how old are they?
2. Collect
Gather relevant data over a given period. It can come from various sources, including databases, social media and web analytics, or survey results. For your shoe store, let’s say data collection focuses on purchase data plus a newly implemented survey on your website.
3. Clean
In an ideal world, everyone would answer questions the same way, and computers would immediately understand the intent of the answer. We do not, as of yet, live in this world. Therefore, it’s necessary to purge duplicate and anomalous entries, reconcile inconsistencies, and ensure collected data is structured and formatted uniformly. For your shoe store, data cleaning may mean changing survey entries in the “age” field so they’re all numerical.
4. Analyze
The cleaned data is then analyzed and manipulated using various techniques to uncover valuable insights. This ranges from simple descriptive analysis, such as revealing recent performance trends like increased sales over the past month, to advanced predictive analysis using machine learning algorithms to predict future sales.
For example, as you look to understand its customer base better, you might cluster data by age groups to reveal demographic patterns or process and transform raw data about location into a bar graph, allowing you to visualize the geographic distribution of your customers.
5. Interpret
Identify patterns, make inferences, and draw conclusions. You might use data visualizations and other presentation techniques to convey the findings. Then, make recommendations (“Appeal to millennials in the suburbs”), discuss limitations (“This was only one month of data”), and sort out the next steps (“Let’s dig into social analytics to learn more about this demo”).
Qualitative vs. quantitative data
All data falls into one of two categories: qualitative data and quantitative data. It’s worth getting to know both of these before digging into specific analysis methods.
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Quantitative data. Quantitative data is measurable, numerical data, often gathered through closed-ended survey questions and data analytics tools. It focuses on answering questions related to “how much” or “how many.” Examples of quantitative data in ecommerce include conversion rate, product price, and sales volume.
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Qualitative data. Qualitative data is descriptive and deals with qualities and attributes that cannot be easily measured numerically. It’s generally gathered via interviews and open-ended surveys and helps answer questions like why and how. In ecommerce, this may involve collecting feedback about a website. Qualitative data can be analyzed at scale through machine learning and turned into sentiment data.
Data analysis methods
You can analyze qualitative and quantitative data using any of several methods. These methods describe the types of questions the data is being used to answer:
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Descriptive data analysis. Descriptive data analysis involves summarizing and presenting data to provide a clear and concise summary of a dataset’s main characteristics. For example, collecting sales figures and presenting them in stacked graphs helps answer questions like, “How much did we sell last month?”
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Exploratory data analysis. Exploratory data analysisis often the first step in a broader analysis and focuses on uncovering patterns, relationships, and potential outliers within the data. For instance, examining sales data spanning multiple years may reveal unforeseen seasonal trends in product popularity, prompting further research. Using various data visualization and statistical analysis techniques, exploratory data analysis helps answer questions like, “Are there any notable seasonal trends in product popularity that can be observed from the sales data across multiple years?” or “Are there any outliers or unusual patterns in website traffic data that coincide with the seasonal trends observed in the sales data?”.
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Diagnostic data analysis. Diagnostic data analysis aims to understand the reasons behind specific outcomes or events. It helps answer questions like, "Why did our sales decline last quarter?"
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Predictive data analysis. Predictive data analysis uses historical data to build models and predict future outcomes. It answers questions like, "Will the next quarter’s sales rise or fall?" Predictive data analysis often leverages machine learning algorithms.
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Prescriptive data analysis. Prescriptive data analysis goes beyond predicting future outcomes by suggesting optimal courses of action. It helps answer questions like, "What marketing strategy should we employ to increase conversion rates?"
Data analysis tools
Data analysts make use of various tools. These include:
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Spreadsheet programs. Spreadsheet programs like Microsoft Excel and Google Sheets are widely used for recording, manipulating and understanding data in a tabular format.
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Programming languages. Programming languages like R and Python are commonly used for statistical modeling.
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Cloud-based analytics platforms. Cloud-based data analytics platforms like Google Analytics and Adobe Analytics can provide insights into website analytics and user pathways.
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Data visualization. Data visualization tools like Tableau and Power BI help create dashboards and visualizations that aid in interpreting and presenting analysis.
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Advanced statistical software. Advanced statistical software aids in some of the complex computational aspects of analysis. Applicable data analysis software includes SAS (Statistical Analysis System), SPSS (Statistical Package for the Social Sciences), KNIME (Konstanz Information Miner), and MATLAB (Matrix Laboratory).
Data analysis FAQ
What are some challenges of data analysis?
Some common challenges in data analysis include data quality issues, data privacy concerns, and the complexity of handling large and unstructured datasets.
What is data-driven decision-making (DDDM)?
Data-driven decision-making refers to using data and analytics to guide decision-making processes.
What is data mining?
Data mining is the process of extracting meaningful patterns, insights, and knowledge from large datasets, often using statistical and machine learning techniques to discover hidden relationships and trends.
What is predictive analytics?
Predictive analytics involves using historical data and statistical models to predict future outcomes or trends, helping businesses anticipate customer behavior, optimize processes, and make proactive decisions.
