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Descriptive Analytics: Steps, Techniques, Use Case, Examples

10th May, 2024
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    Descriptive Analytics: Steps, Techniques, Use Case, Examples

    Descriptive Analytics is a field of business intelligence with expertise in statistical analysis, waiting for history, and other data. Descriptive Analytics professionals find the data to question and study; they pose the questions that need answers; they translate these queries into mathematical models and apply them to their chosen data. Using descriptive analytics in data visualization is a practice that can greatly aid and improve people’s decision-making. Their decisions are taken from over-reliance, wishful thinking, and in isolation. Descriptive analytics is a rapidly growing field with a promising future. It offers the ability to make better business decisions and understand how customers interact with companies and products. General techniques used in descriptive analytics include; Data collection, Data preparation, exploratory data analysis, data visualization, statistical analysis, and predictive modeling. In this article, we will go through  Descriptive Analytics: Steps, Techniques, Use Cases, and Examples. in detail. 

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    What is Descriptive Analytics in Data Science?

    The purpose of descriptive analytics is to turn data into insights. It is used to understand what happened in the past and why it happened. Descriptive analytics uses various techniques to answer questions such as “what is the average order value?”, “how many orders were placed last month?” or “what was the most popular product last year?”.  

    There are four main steps in descriptive analytics:  

    1. Data collection: Collecting data from various sources such as sales reports, customer surveys, social media, etc. 
    2. Data preparation: Cleaning and organizing the data so it can be analyzed. 
    3. Exploratory data analysis: Analyzing the data to find trends, patterns, and relationships. 
    4. Data visualization: Creating graphs and charts to visualize the data and make it easy to understand. 

    The most common techniques used in descriptive analytics are statistical analysis, data visualization, and predictive modeling. 

    Why is Descriptive Analytics Important in Data Science?

    Descriptive analytics is a branch of data science that deals with data collection, organization, and analysis. This type of analytics is important in data science because it allows researchers to understand trends and patterns in data. It also helps researchers to develop hypotheses about how certain factors may influence the results of their research. Additionally, descriptive analytics can create visualizations of data that can help researchers/organization communicate their findings to others. 

    Most businesses gather enormous amounts of data, yet it's frequently impossible to interpret it without doing some analysis. For instance, when looking at thousands of individual sales transactions for the most recent quarter, it is impossible to determine the average customer spending level or whether overall sales were higher or lower than in earlier quarters. The first step in making sense of unstructured data is descriptive analytics. To better understand the current health of your business, it frequently uses elementary mathematical processes to provide summary statistics, including average revenue per customer. Companies can employ different sorts of analysis to look deeper into the causes and effects of trends once they have been identified. 

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    How Does Descriptive Analytics Work in Data Science?

    Descriptive Analytics is a powerful tool that can summarize data and communicate information in an understandable way. Statistics associated with descriptive analytics can describe the distribution, the data's central tendency, and the data's dispersion. Additionally, descriptive statistics in data science can be used to identify relationships between variables and examine the differences between data groups. Descriptive statistics are an important part of any data analysis and can be used to help make decisions about how to best analyze a dataset. Reports, pivot tables, and visualizations like histograms, line graphs, pie charts, and box and whisker plots are frequently used to illustrate the results of descriptive analytics. 

    Data analysis requires businesses to first gather and consolidate raw data from multiple sources, then transform it into a standard format for analysis. They can now begin analyzing the data. Many businesses employ data intelligence, a collection of techniques and instruments for gathering and analyzing data, then drawing conclusions and formulating plans of action based on the results. Others add simple descriptive analytics to the combined data using spreadsheet formulas, producing KPIs and other statistics that are then included in reports. Integrated ERP systems can store the organization's business data in a single database, simplifying descriptive analytics. Leading suites also come with integrated analysis tools to aid with data storytelling, creating a narrative around data using visualizations to communicate the significance of the data in an engaging manner. Common KPIs can be provided with real-time data combined into dashboards, charts, and reports using ERP-embedded business intelligence tools

    How is Descriptive Analytics Used?

    Businesses utilize descriptive analytics in various areas of their operations to assess how well they are performing and if they are on track to meet their objectives. Common financial measurements generated by descriptive analytics, such as quarterly increases in sales and expenses, are monitored by business executives and financial experts. By tracking stats like conversion rates and the number of social media followers, marketing teams may assess the effectiveness of their campaigns. Production line throughput and downtime are among the variables that manufacturing companies keep an eye on. 

    There are several uses for the metrics generated by descriptive analytics, including: 

    1. Reports: Descriptive analytics is used to provide the primary financial indicators found in a company's financial statements. Descriptive analytics are often used in other typical reports to emphasize specific areas of business performance.
    2. Visualizations: Metrics can be more effectively communicated to a larger audience by being displayed in charts and other graphic forms.
    3. Dashboards: Dashboards are a tool that executives, managers, and other staff members can use to monitor progress and organize their daily workload. Dashboards offer a selection of KPIs and other crucial data that are catered to the needs of each individual. To help people quickly digest the information, it may be presented as charts or other visualizations.

    The Five Steps Descriptive Data Science Involves

    Determining the metrics you want to output is typically the first step in applying descriptive analytics, and presenting them in the proper format is the final step. The procedures to generate your own descriptive analytics are listed below. 

    Step 1: Define business metrics

    Defining the metrics you wish to measure is the first step. These should represent the main organization’s objectives of each segment or the organization as a whole. For instance, a company that prioritizes expansion may track quarterly revenue growth, and its accounts receivable department may monitor metrics like days sales outstanding and other measures of how long does it take to get payment from a customer?  

    Step 2: Identify data required

    Find the data you require to generate the desired stats. The data may be dispersed over numerous programmes and files at some businesses. Businesses that use ERP systems can already have the majority or all of the data they require in the databases of their systems. Some indicators might also need information from outside sources, like social media platforms, e-commerce websites, and databases used for industry benchmarking. 

    Step 3: Extract and preprocess data

    When data is gathered from several sources, extracting, integrating, and preprocessing it before analysis is a time-consuming but necessary step to ensure accuracy. This procedure could include data cleansing to eliminate conflicts and inaccuracies in data from diverse sources and convert the data into a format compatible with descriptive-analytical tools. Advanced data analytics employ the method of data modeling to help prepare, shape, and organize corporate data. Data modeling is a framework for describing and formatting data inside information systems. 

    Step 4: Data Analysis

    Businesses can utilize a variety of technologies, such as spreadsheets and business intelligence (BI) tools, to do descriptive analytics. In descriptive analytics, applying simple mathematical operations to one or more variables is a common step. For example, sales managers could monitor the average profit per transaction or the monthly revenue from new clients. Executives and financial professionals may keep an eye on financial indicators like the gross profit margin, or the ratio of gross profit to sales. 

    Step 5: Present data

    Data that is presented in visually appealing forms, such as pie charts, bar charts, and line graphs, is typically easier for stakeholders to understand. But some people, like financial professionals, could like information that is provided in the form of figures and tables. 

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    Descriptive Analysis Techniques

    The techniques for descriptive analysis are the most common descriptive methods of data analysis for qualitative data. Descriptive data analysis techniques are used to describe the subjects of a study in detail, identifying patterns and trends, and providing insights into how subjects behave. 

    Some of the most common descriptive analysis methods for descriptive analysis statistics are: 

    • The frequency distribution is a method that provides an overview of all the responses to a question. 
    • The bar chart is a visual representation that displays how responses vary on different dimensions. 
    • The pie chart displays how responses vary on different dimensions. 
    • A scatterplot displays how two variables relate to each other. 
    • A histogram provides an overview of all the responses to a question, with each response grouped into bins according to some criterion such as age or income level. 

    Types of Descriptive Analysis

    There are four different types of descriptive analysis: measures of frequency, central tendency, dispersion or variation, position, . These techniques work best when only one variable is present. 

    1. Measures of Frequency

    Understanding how frequently a specific event or response is likely to occur is crucial for descriptive analysis. The main goal of frequency measurements is to create something akin to a count or a percentage. For example: Think about a survey where 500 people are questioned about their favorite football team. A list of 500 responses would be challenging to read and organize, but by counting the number of times a specific football team was chosen, the data can be made much more understandable. 

    Measures:  Count, Percent, Frequency 

    2. Measures of Central Tendency

    A single value that seeks to characterize a set of data by pinpointing the central position within that set of data is referred to as a measure of central tendency. As a result, measures of central location are occasionally used to refer to measures of central tendency. They also fit into the category of summary statistics. You are probably most familiar with the mean (also known as the average), but there are other central tendency measures, including the median and the mode. 

    The mean, median, and mode are all reliable indicators of central tendency, but depending on the situation, some indicators are more useful than others. 

    Think about a survey where 1,000 people's body weight are recorded as an example. The mean average would be a great descriptive metric to use in this situation to measure mid-values. 

    Measures: Mean, Median, and Mode 

    3. Measures of Dispersion

    At times, understanding how data is distributed across a range is crucial. Consider the average weight of a sample of two people to further explain this. The average weight will be 60 kilograms if both people weigh 60 kilograms. The average weight is still 60 kg even if one person weighs 40 kg and the other 80 kg. This type of distribution can be measured using dispersion metrics like range or standard deviation

    Measures: Range, Variance, Standard Deviation

    4. Measures of Position

    Identifying the position of a single value or its response in relation to others is another aspect of descriptive analysis. In this field of expertise, metrics like percentiles and quartiles are extremely helpful. 

    Measures: Percentile Ranks, Quartile Ranks 

    Similar to bivariate analysis, the multivariate analysis examines more than two variables. The two methods for bivariate analysis are listed below:

    5. Contingency table

    In statistics, a contingency table, also known as a two-way frequency table—is a tabular representation with at least two rows and two columns that are used to present categorical data as frequency counts. 

    For instance, the contingency table below, which has two rows and five columns, displays the findings of a random sample of 2200 adults categorized by gender and preferred method of eating Icy dessert.


    2. Scatter plots

    You can visualize the relationship between two or three different variables using a scatter plot. It represents a relationship's strength in a visual way. One variable should be plotted along the x-axis, and another along the y-axis in a scatter plot. A point in the chart represents each data point. 

    The Advantages and Disadvantages of Descriptive Analytics in Data Science

    Even though it is one of the more straightforward analytical strategies, descriptive analysis in data science has many benefits: 

    • gives access to information that would otherwise be difficult to understand. 
    • gives a precise estimation of how frequently important data points occur. 
    • is cheap, and it only calls for rudimentary mathematical knowledge. 
    • is easier to complete, particularly with the aid of programmes like Python or Microsoft Excel. 
    • relies on information that businesses already have, so getting new information is not necessary. 
    • compared to inferential statistics, it considers the entire population (rather than a data sampling). 

    We've examined the benefits of descriptive analytics, but what are its drawbacks? The following are some drawbacks of descriptive analytics: 

    • Although you can summarize the data sets you have access to, they might not provide the full picture. 
    • Descriptive analytics can't be used to test a theory or figure out why data is presented in a certain way. 
    • Descriptive analytics cannot be used to make future predictions. 
    • Your results cannot be applied to a larger population as a whole. 
    • Descriptive analytics provide no information regarding the method of data collection, so the data set may contain errors.

    Descriptive Analytics Use Cases

    How can descriptive analytics be applied in practice now that we've covered its theory? Even though descriptive analytics only considers what occurred rather than why it is still an important first step in the larger data analytics process. Let's look at it. 

    1. Monitoring social media activity

    A significant touchpoint in the sales process is social media. Therefore, it is essential to have the ability to measure and present engagement metrics across a complex constellation of campaigns and social networks in order to identify the most effective strategies for digital marketing. Fortunately, descriptive analytics will be included by default in marketing reports on social media engagement. Measures of social media engagement include clicks, likes, shares, detail expands, bounce rates, and more. These metrics are all easily summed up using descriptive techniques. 

    For instance, a business might be curious to know which social media account is bringing in the most visitors to its website. They can quickly compare data about various channels using dashboards, visualizations, and descriptive statistics. Similarly to this, marketing teams can examine particularly shareable content and compare, for example, blog posts and videos, to determine which generates the most clicks. 

    Although none of this data draws clear conclusions (since it doesn't assess cause and effect), it is still useful. Teams can use it to create hypotheses or make educated assumptions about where to spend their time and money. 

    2. Streaming and Online Shopping

    Descriptive analytics are used to spot trends by subscription streaming services like Spotify and Netflix as well as e-commerce websites like Amazon and eBay. Descriptive metrics are useful for identifying what users and consumers are currently most interested in. For instance, Spotify uses descriptive analytics to find out which albums or artists its subscribers are enjoying. Amazon compares customer purchases using descriptive analytics. These insights influence the recommendation engines in both cases to work with are influenced by these insights. 

    While Netflix goes even further in its application of descriptive analytics. Netflix, a business that places a high value on data, uses descriptive analytics to determine which genres and TV shows are most popular with its audience. Decisions about new content creation, marketing strategies, and even which production companies they work with. 

    3. Learning management systems

    Many institutions now use online/offline hybrid learning, from traditional education to corporate training. A common component of this is learning management systems or LMSs for those in the know. LMS platforms keep track of everything, including user participation, attendance, test results, and in the case of e-learning programs even the length of time it takes students to finish a course. Descriptive-analytical reports provide a high-level overview of what is working and what is not by summarizing this data. 

    Teachers and training specialists can monitor goals at the individual and organizational levels using these data. They can examine grade distributions or discover the most well-liked teaching aids. And even though they won't always understand why, it might be possible to deduce from the data that, for instance, videos are more popular than written documents. The first step in improving course design and improving learner outcomes is to present this information.

    5 Examples of Descriptive Analytics

    Descriptive analysis examples in real life are shown below and learn the application of descriptive analysis in real life: 

    1. Traffic and Engagement Reports

    Reporting is a type of descriptive analytics. You already use descriptive analytics if your organization monitors engagement through social media analytics or website traffic. These reports are made by comparing current metrics to historical metrics and visualizing trends using raw data that is generated when users interact with your website, advertisements, or social media content. 

    You might be in charge of reporting on which media outlets bring in the most visitors to the product page on your company's website, for instance. You can count the number of visitors from each source by examining the page's traffic statistics using descriptive analytics. You might choose to go a step further and contrast current traffic source data with earlier traffic source data. This will allow you to inform your team of any changes, such as highlighting a 20 percent year-over-year increase in traffic from paid advertisements. 

    2. Financial Statement Analysis

    Another well-known descriptive data analysis example is balance sheet analysis. Financial statements are periodic reports detailing financial information about a company, which as a whole, provide an overall picture of the company's financial position. 

    There are several types of financial statements, such as Balance Sheets, Income Statements, Cash Flow Statement and Shareholders' Equity Statements. Each is aimed at a specific audience and conveys different information about the company's finances. Balance sheet analysis can be performed in three ways: vertical, horizontal and ratio. 

    Vertical analysis reads the statement from top to bottom, comparing each element to the elements above and below it. This helps determine relationships between variables. For example, if each item is a percentage of a total, comparisons can provide insight into which percentages of the total are large and which are small. 

    Horizontal analysis reads the statement from left to right and compares each item to itself in the previous period. This type of analysis determines changes over time. 

    Finally, relationship analysis compares one section of the report to another based on their relationship to the whole. This allows you to directly compare articles and company metrics over multiple time periods to industry metrics to determine if a company is over- or underperforming.  

    Each of these balance sheet analysis methods is an example of descriptive analysis because it provides information about trends and relationships between variables based on current and historical data.  

    3. Demand Trends

    Additionally, descriptive analytics can be used to spot patterns in consumer preferences and behavior and predict demand for particular goods or services. 

    A great use case for descriptive analytics is trend identification by the streaming service Netflix. The Netflix team, which has a history of being highly data-driven, collects information on users' platform usage. They use this data to analyze what TV shows and movies are popular right now, and they display those titles in a section on the platform's home screen. This information not only enables Netflix subscribers to see what's popular and, consequently, what they might enjoy watching, but it also enables the Netflix team to understand which media genres, themes, and actors are particularly favored at a given time. This may influence choices regarding the creation of new original content, agreements with current production companies, marketing, and retargeting campaigns. 

    4. Aggregated Survey Results

    Market research can also benefit from descriptive analytics. Descriptive analysis in research can be used to find connections between variables and trends when extracting insights from survey and focus group data. 

    You might, for instance, carry out a survey and find that, as respondents' ages rise, so does their propensity to buy your product. Descriptive analytics can determine whether this age-purchase correlation has always existed or whether it was something that only happened this year if you've conducted this survey repeatedly over a number of years. 

    This kind of information can open the door for diagnostic analytics, which can explain why certain variables are correlated. Using predictive and prescriptive analytics, you can then use the trends to plan future product improvements or marketing campaigns. 

    5. Progress to Goals

    Descriptive analytics can also be used to monitor goal progress. Your team can determine whether efforts are on track or if changes need to be made by reporting on progress toward key performance indicators (KPIs). 

    If your company wants to reach 500,000 unique page views per month, for instance, you can use traffic data to show your progress. You have 200,000 unique page views, so you're probably halfway through the month. You should be halfway toward your objective at that point—at 250,000 unique page views—so this would be underperforming. With the help of this descriptive analysis, your team can determine what needs to be changed in order to increase traffic and get back on track to meet your KPI.

    Descriptive vs Predictive vs Prescriptive Analytics

    The distinctions between descriptive, predictive, and prescriptive analytics are outlined in the following table.

    CriteriaDescriptive AnalysisPredictive AnalysisPrescriptive Analysis
    SummaryWhat happened?What’s going to happen?What should happen?
    FunctionIt uses data mining and data aggregation to discover historical data.It looks at historical data and analyzes past data trends to predict what could happen.It takes the conclusions gleaned from descriptive and predictive analysis and recommends the best future course of action.
    ProsIt’s easy to employ in daily operations. Little experience is needed.It’s a valuable forecasting tool.It offers critical insights into making the best, most informed decisions.
    ConsIt offers a limited view, and doesn't go beyond the data’s surface.It needs lots of historical data to work. It will never be 100% accurate.It requires a lot of past data and often cannot account for all possible variables.

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    For businesses to understand the vast amounts of historical data, descriptive analytics is a crucial tool. By tracking KPIs and other metrics, it enables you to keep an eye on performance and trends. Companies can learn more about the reasons behind events, their likely future outcomes, and potential courses of action by combining descriptive analytics with diagnostic, predictive, and prescriptive analysis. This will help them perform their businesses more effectively.

    Descriptive analytics is a powerful tool for data science, and I hope this article has shown you why. By using descriptive analytics, we can gain a better understanding of our data and make more informed decisions about how to best use it. If you're interested in learning more about data science, I highly recommend checking out some of the other articles on knowledgeHut site.

    Frequently Asked Questions (FAQs)

    1What is the Difference Between Descriptive And Predictive Analytics?

    Using descriptive analytics, a business can better assess its performance thus far by looking at what has already occurred in the industry. In contrast, predictive analytics makes use of historical data to forecast potential future events and the effects of various scenarios on the business. 

    2How Can I Become A Data Scientist?

    BTech, BSc, and MTech degrees in data science are frequently pursued after high school. Additional specializations are specializations available for these programs, including machine learning, artificial intelligence, big data, etc.

    3What are Descriptive Statistics For Data Science?

    In a summary that describes the data sample and its measurements, descriptive statistics describe, illustrate, and summarize the fundamental characteristics of a dataset found in a specific study. It aids in better data comprehension for analysts. The importance of descriptive statistics is immense in the descriptive analysis as it is the building block of any descriptive analysis 

    4What are the Four Different Types Of Analytical Methods?

    Descriptive, diagnostic, predictive, and prescriptive analytics are the four main categories. Prescriptive analytics uses the forecast as a foundation to suggest a course of action. Descriptive analytics highlights what has occurred; diagnostic analytics clarifies why it occurred, predictive analytics projects what may occur. These four methodologies, when combined, give businesses crucial information about past, present, and potential future performance as well as potential solutions for improving operations. 

    5What Tools Are Used In Descriptive Statistics?

    Data are summarized using descriptive statistics in the form of mean, median, and mode. A random sample of data from a population is used by inferential statistics to describe and draw conclusions about the entire population. When it is impossible to examine the entire population, it is useful. 


    Ashish Gulati

    Data Science Expert

    Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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