{"id":3424,"date":"2026-04-03T08:21:34","date_gmt":"2026-04-03T06:21:34","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/what_is_advanced_analytics\/"},"modified":"2026-08-26T13:48:36","modified_gmt":"2026-08-26T11:48:36","slug":"what_is_advanced_analytics","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/what_is_advanced_analytics\/","title":{"rendered":"What is advanced analytics?"},"content":{"rendered":"<style>.ndb{width:100vw;margin-left:calc(50% - 50vw);background:#eeeeee;padding:22px 24px 14px;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif;color:#1b2635}.ndb *{box-sizing:border-box}.ndb a{text-decoration:none}.ndb-wrap{width:min(100%,1200px);margin:0 auto}.ndb-lead{font-size:18px;line-height:1.6;color:#3a4a5a;font-weight:300;margin:0 0 22px}.ndb-lead a{color:#2d799f;font-weight:600}.ndb-highlight{margin:0 0 36px;padding:28px 34px;border-radius:18px;background:linear-gradient(135deg,#56a1c8 0%,#245e80 100%);color:#fff;box-shadow:0 16px 34px rgba(30,83,116,.22)}.ndb-highlight p{margin:0;color:#fff;font-size:20px;line-height:1.5;font-weight:400}.ndb-highlight b{font-weight:800}.ndb-toc{list-style:none;display:flex;flex-wrap:wrap;gap:10px;justify-content:center;margin:0 0 42px;padding:0}.ndb-toc a{padding:10px 18px;background:#f2f2f2;border-radius:22px;color:#12354b!important;font-size:14px;font-weight:600;box-shadow:-6px -6px 12px rgba(255,255,255,.9),6px 6px 12px rgba(30,83,116,.10)}.ndb-toc a:hover{color:#2d799f!important}.ndb-card{margin:0 0 42px;padding:0;background:transparent;border-radius:0;box-shadow:none;scroll-margin-top:90px}.ndb-card h2{margin:0 0 20px;padding-bottom:12px;color:#001233;font-size:24px;font-weight:700;position:relative;border-bottom:1px solid #dbe5ec}.ndb-card h2:after{content:\"\";position:absolute;left:0;bottom:-1px;width:62px;height:3px;background:#56a1c8;border-radius:2px}.ndb-card p{margin:0 0 14px;font-size:16.5px;line-height:1.62;color:#33424f}.ndb-card 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tbody th{background:#e9f1f6!important;color:#12354b!important;font-weight:600}.ndb-card td{background:#fbfcfd!important;color:#33424f!important}.ndb-card table ul{margin:0;padding-left:18px}.ndb-card table li{font-size:14px}.ndb-card pre{margin:22px 0;padding:22px 24px;background:#0b1830!important;color:#e6eef5!important;border-radius:14px;overflow-x:auto;font-family:Consolas,Menlo,monospace;font-size:12.5px;line-height:1.5;white-space:pre}.ndb-card--accent{background:transparent}@media(max-width:820px){.ndb-card{padding:0}.ndb-card h2{font-size:21px}.ndb-toc{gap:8px}}@media(max-width:640px){.ndb{padding:12px 14px}}<\/style><div class=\"ndb\"><div class=\"ndb-wrap\"><div class=\"ndb-lead\"><p data-start=\"173\" data-end=\"227\">Advanced analytics refers to a set of methods that go beyond traditional reporting to extract deeper insights from data.<\/p>\n<p data-start=\"229\" data-end=\"299\">Organizations are generating and storing increasing amounts of data.<\/p>\n<p data-start=\"301\" data-end=\"394\">This large volume of information offers new opportunities for analysis and decision-making.<\/p>\n<p data-start=\"396\" data-end=\"513\">Most companies, however, still rely mainly on descriptive analytics, which only looks at what happened in the past.<\/p>\n<p data-start=\"515\" data-end=\"625\">Advanced analytics goes further and provides deeper insights that can add significant value to the business.<\/p>\n<\/div><ul class=\"ndb-toc\"><li><a href=\"#Objectives\">Objectives<\/a><\/li><li><a href=\"#DescriptiveAnalytics\">Descriptive analytics<\/a><\/li><li><a href=\"#DiagnosticAnalytics\">Diagnostic analytics<\/a><\/li><li><a href=\"#PredictiveAnalytics\">Predictive analytics<\/a><\/li><li><a href=\"#PrescriptiveAnalytics\">Prescriptive analytics<\/a><\/li><li><a href=\"#Conclusions\">Conclusions<\/a><\/li><\/ul><div class=\"ndb-card\" id=\"Objectives\"><h2>Objectives<\/h2><p>Advanced analytics is the set of techniques used to discover relationships, recognize patterns, predict trends, and find associations in your data.<\/p>\n<p>The objective is to use internal and external sources of information to obtain valuable insights that result in smarter decisions and better business outcomes.<\/p>\n<p>These methods enable us to understand what has happened in our company, why it occurred, what will happen next, and how we can make it happen.<\/p>\n<p>The following figure illustrates the whole advanced analytics process.<\/p>\n<p><img decoding=\"async\" style=\"width: 500px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/advanced_analytics.webp\"  alt=\"Advanced Analytics\"\/><\/p>\n<p>As we can see, the advanced analytics process comprises 4 phases: descriptive, diagnostic, predictive, and prescriptive analytics.<\/p>\n<p>Each of these phases is more complex than the previous one but provides more value.<\/p>\n<\/div><div class=\"ndb-card\" id=\"DescriptiveAnalytics\"><h2>Descriptive analytics<\/h2><p>Descriptive analytics is the first stage of advanced analytics. It answers the question: <em>What happened?<\/em>.<\/p>\n<p>In this phase, we obtain insights regarding the general aspects of the company.<\/p>\n<p>The output is a set of tables and charts with historical information about operations, sales, customers, and other relevant data.<\/p>\n<p>Some of the analyses carried out at this stage are:<\/p>\n<ul>\n<li><a href=\"#Statistics\">Statistics<\/a>.<\/li>\n<li><a href=\"#Distributions\">Distributions<\/a>.<\/li>\n<\/ul>\n<h3 id=\"Statistics\">Statistics<\/h3>\n<p>Statistics provide very valuable information. Indeed, they put the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/\">data set<\/a> in context.<\/p>\n<p>The most important statistical parameters are the minimum, the maximum, the mean, and the standard deviation.<\/p>\n<p>The table below illustrates these parameters for the total amount of money spent by a customer in an online store.<\/p>\n<table>\n<tbody>\n<tr>\n<td><\/td>\n<th>Minimum<\/th>\n<th>Maximum<\/th>\n<th>Mean<\/th>\n<th>Standard deviation<\/th>\n<\/tr>\n<tr>\n<th>Total amount (USD)<\/th>\n<th>12<\/th>\n<th>500<\/th>\n<th>51<\/th>\n<th>56<\/th>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>As we can see, each customer has spent an average of 51 USD, but some people have spent up to 500 USD.<\/p>\n<h3 id=\"Distributions\">Distributions<\/h3>\n<p><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\/#Distributions\">Distributions<\/a> show how the data is arranged over its entire range.<\/p>\n<p>Histograms show the distribution of continuous variables. A normal (Gaussian) or uniform distribution is, in general, desirable.<\/p>\n<p>For example, the following figure depicts a histogram for the age of our customers.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/age-distribution.webp\"  alt=\"Age Distribution\"\/><\/p>\n<p>As we can see, most of our customers are between 30 and 40 years old.<\/p>\n<p>Pie charts show the distribution of binary or nominal variables.<\/p>\n<p>The following figure shows the pie chart for the customers who purchased a product from an online store during the last marketing campaign. This is a binary variable.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/advanced-analytics-pie-chart.webp\"  alt=\"Advanced Analytics Pie Chart\"\/><\/p>\n<p>As we can see, only 1% of the people who visit our ad buy the product.<\/p>\n<\/div><div class=\"ndb-card\" id=\"DiagnosticAnalytics\"><h2>Diagnostic analytics<\/h2><p>Diagnostic analytics is the second phase of advanced analytics. It answers the question <em>Why did it happen?<\/em>.<\/p>\n<p>Diagnostic analytics allows for determining the factors that contributed to the outcome.<\/p>\n<p>Here we concentrate on the following techniques:<\/p>\n<ul>\n<li><a href=\"#ScatterCharts\">Scatter charts<\/a>.<\/li>\n<li><a href=\"#Correlations\">Correlations<\/a>.<\/li>\n<\/ul>\n<h3 id=\"ScatterCharts\">Scatter charts<\/h3>\n<p>Scatter charts might help discover dependencies between the output and input variables.<\/p>\n<p>This chart plots output values versus input values.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/Feature 1 vs Target scatter chart.webp\" alt=\"Scatter chart\" \/><\/p>\n<p>The chart above shows that as the value of Feature 1 increases, the value of Target decreases.<\/p>\n<h3 id=\"Correlations\">Correlations<\/h3>\n<p>Correlations are also helpful for discovering dependencies between input and output variables.<\/p>\n<p>A correlation is a numerical value between 0 and 1 that expresses the strength of the relationship between two variables.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/advanced-analytics-correlations-chart.webp\" alt=\"Correlations chart\" \/><\/p>\n<p>The maximum correlation obtained (-0.287) is between the recency and the conversion.<\/p>\n<p>Such a high correlation indicates that we have to study this variable more thoroughly.<\/p>\n<\/div><div class=\"ndb-card\" id=\"PredictiveAnalytics\"><h2>Predictive analytics<\/h2><p>Predictive analytics is the third stage of advanced analytics. It answers the question: <em>What will happen?<\/em><\/p>\n<p>Predictive analytics allows identifying the likelihood of future outcomes based on historical data.<\/p>\n<p>This is the most critical phase of the analysis; its output is a predictive model capable of predicting future outcomes.<\/p>\n<p>It encompasses various machine learning techniques such as k-nearest neighbors, decision trees, neural networks, etc.<\/p>\n<p>We explain these techniques below.<\/p>\n<ul>\n<li><a href=\"#KNearestNeighbors\">K-nearest neighbors<\/a>.<\/li>\n<li><a href=\"#DecisionTrees\">Decision trees<\/a>.<\/li>\n<li><a href=\"#NeuralNetworks\">Neural networks<\/a>.<\/li>\n<\/ul>\n<h3 id=\"KNearestNeighbors\">K \u2013 Nearest neighbors<\/h3>\n<p>K-nearest neighbors is a very simple method used for\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Classification\">classification<\/a> and\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Approximation\">approximation<\/a>.<\/p>\n<p>It stores all available cases and classifies new cases based on a similarity measure.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/knn-blog2.webp\" alt=\"k-nearest neighbors image\" \/><\/p>\n<p>The chart above shows that the algorithm classifies many data points into A (blue circles) and B (orange squares).<\/p>\n<p>A new data point is introduced (green triangle). The k-nearest neighbors&#8217; method determines whether a point belongs to category A or category B based on the similarity between them.<\/p>\n<h3 id=\"DecisionTrees\">Decision trees<\/h3>\n<p>Decision trees are also a simple method used for\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Classification\">classification<\/a> and\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Approximation\">approximation<\/a>.<\/p>\n<p>A decision tree is a mathematical model that helps you choose between several courses of action. It estimates probabilities to calculate likely outcomes.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/decision-tree-blog2.webp\" alt=\"Decision tree image\" \/><\/p>\n<p>In the diagram above, we can see an example of a decision tree. Feature 1, Feature 2, and Feature 3 are three numerical features of the dataset; A, B, and C are the three categories in which the instances are classified; and a, b, c, and d are numerical values.<\/p>\n<p>We start at the top to classify an instance and follow the tree&#8217;s branches with our sample values.<\/p>\n<p>First, we check the instance&#8217;s value for Feature 1; if it is greater than a, it would be classified in category B, and we would be done.<\/p>\n<p>If the value is equal to or less than A, we check Feature 2; if it is greater or less than B.<\/p>\n<p>We follow this process until we reach a leaf category (A, B, or C).<\/p>\n<h3 id=\"NeuralNetworks\">Neural networks<\/h3>\n<p><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network\/\">Neural Networks<\/a> are recognized as one of the most powerful machine learning methods.<\/p>\n<p>They are used in\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Classification\">classification<\/a> and\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications\/#Approximation\">approximation<\/a> tasks.<\/p>\n<p>Neural networks are computational models based on the neural structure of the brain.<\/p>\n<p>The outputs from the neural network depend on the inputs fed to it and the different parameters within the neural network.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/Neural network advanced analytics.webp\" alt=\"Neural network image\" \/><\/p>\n<p>The graph above shows a neural network with four inputs (feature 1, 2, 3, and 4).<\/p>\n<p>When we introduce the values of the four features in the neural network, we get an output.<\/p>\n<\/div><div class=\"ndb-card\" id=\"PrescriptiveAnalytics\"><h2>Prescriptive analytics<\/h2><p>Prescriptive analytics is the fourth and last step of advanced analytics. It answers the question <em>How can we make it happen?<\/em>.<\/p>\n<p>It consists of applying the predictive model to determine the best solution or outcome among various choices.<\/p>\n<p>In this phase, we use our predictive model to predict future outcomes and show the decision-maker the implications of each option.<\/p>\n<p>For instance, we propose two different scenarios to see how the target varies as a single input function, depending on Feature 3.<\/p>\n<p>The following table shows the reference variables for the first scenario.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/Table 1.webp\" alt=\"Directional output table 1\" \/><\/p>\n<p>The following plot shows the output Target as a function of the input Feature 3.<\/p>\n<p>The x and y axes are defined by the range of Feature 3 and Target, respectively.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/Target against Feature 3 directional line chart 1.webp\" alt=\"Directional output plot 1\" \/><\/p>\n<p>The chart above shows that when Feature 3 is 1010, Target reaches its maximum of 484.93.<\/p>\n<\/div><div class=\"ndb-card ndb-card--accent\" id=\"Conclusions\"><h2>Conclusions<\/h2><p>As seen in this post, simple analytics are not enough to get actionable insights and improve business operations.<\/p>\n<p>Thanks to Advanced Analytics, you will not leave anything to chance. It encompasses a global vision, spanning from the past to the possible futures envisioned by your company.<\/p>\n<p>The data science and machine learning platform <a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a>\u00a0contains many utilities to perform descriptive, diagnostic, predictive, and prescriptive analytics easily.<\/p>\n<p>You can <a href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">download<\/a> Neural Designer now and try it for free.<\/p>\n<\/div><\/div><\/div>","protected":false},"author":22,"featured_media":2688,"template":"","categories":[],"tags":[36],"class_list":["post-3424","blog","type-blog","status-publish","has-post-thumbnail","hentry","tag-tutorials"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What is advanced analytics?<\/title>\n<meta name=\"description\" content=\"Learn what advanced analytics is and how you can apply descriptive, 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