Advanced Analytics: Definition, Benefits, and Use Cases

Written by Coursera Staff • Updated on

Advanced analytics employs some of the most complex data analytics techniques to help businesses and organizations improve their decision-making. Learn about using advanced analytics can help your business or organization.

[Featured Image] A data analyst is studying data on two computer screens

Advanced analytics is a collection of data analytics techniques, such as machine learning and predictive modelling, used by businesses to improve their decision-making. Leveraging some of the most complex techniques in the field of data science, advanced analytics is used to do everything from detecting fraud by banking institutions to directing the development of marketing campaigns. 

In this article, you’ll learn even more about advanced analytics, how it differs from other similar data analysis methods, the techniques that define it, and its benefits in the real world. You’ll also find courses you can take to start developing your advanced analytics skills today. 

What is advanced analytics?

Advanced analytics is an umbrella term referring to various data analysis techniques used primarily for predictive purposes, such as machine learning, predictive modelling, neural networks, and artificial intelligence (AI). Businesses employ advanced analytics primarily to forecast future outcomes and guide their decision-making, not just to gain business insights.

Businesses use advanced analytics for many purposes, from identifying emerging market trends to reducing decision-making bias and anticipating complex market dynamics. 

Advanced analytics vs. business intelligence 

Although they share some similarities, business intelligence and advanced analytics have key differences and use cases. 

Traditional business intelligence (BI) uses common data analytics techniques, such as data visualization, data mining, statistical analysis, and reporting, to create actionable insights for businesses based on their current and historical data. Businesses use BI for descriptive analytics or the analysis of past and present data to describe how a business currently is. 

For example, a streaming platform might employ BI practices to identify the number of subscribers they have and the most popular content on the platform. The business will use this information to begin promoting that content to retain current subscribers and obtain new ones. 

Advanced analytics, however, employs more complex data analytics techniques, such as machine learning, to make predictions and improve business decision-making. As a result, advanced analytics is used for predictive and prescriptive analysis, meaning organizations use it to predict future outcomes and prescribe a course of action. 

The same streaming platform, for example, might use advanced analytics to identify historical seasonal trends and forecast how those trends will play out in the future. These predictions could direct their long-term business decisions, such as producing more content to align with future trends. 

Advanced analytics: Benefits, techniques, and use cases

Advanced analytics encompasses a variety of impactful techniques that can benefit companies as they make strategic decisions to improve their operations, development, and bottom line. In this section, you’ll learn more about advanced analytics's benefits, techniques, and use cases. 

At its core, advanced analytics is just a collection of data analytics techniques and methodologies. While entry-level data analysts understand some of these techniques, like data visualization, data scientists, data engineers, and machine learning engineers are more likely to use others, such as deep learning and predictive modelling. 

Here are some of the techniques you can expect to use as you explore advanced analytics: 

  • Predictive modelling pairs machine learning and data mining to forecast future outcomes using current and historical data. 

  • Data mining is identifying trends, patterns, and anomalies within data sets. Data mining often employs a combination of statistics, machine learning, and AI.  

  • Sentiment analysis mines text and uses natural language processing (NLP) to identify whether the data is negative, positive, or neutral. This technique can help businesses understand how potential customers perceive their brand. 

  • Cluster analysis is analyzing data to identify clusters of related data points. Organizations typically use it to classify data. In machine learning, it is an unsupervised algorithm.  

  • Cohort analysis breaks large volumes of data down into classifiable related groups, or “cohorts.” Often, businesses use this technique to break down customer information into specific segments, which can give them insight into targeted marketing efforts. 

  • Data visualization is the graphic or pictorial representation of data. This common data analytics practice conveys findings through easily understood visual representation.  

  • Complex event processing (CEP) is a method that tracks and analyzes data in real-time to derive actionable conclusions. 

  • Machine learning (ML) is a form of artificial intelligence (AI) concerned with building analytic models capable of autonomous learning. Organizations train algorithms using large data sets that incrementally alter the algorithm with each iteration to create machine-learning models. 

Advanced analytics benefits 

Advanced analytics provide businesses with a tool kit of data analysis techniques that can have a range of benefits when facing common business challenges. From helping them make better business decisions to predicting future trends and assessing risks, advanced analytics can guide businesses as they manoeuvre shifting market dynamics. 

Some of the benefits that advanced analytics can provide businesses include: 

  • Improved forecasting: One of the primary reasons businesses use advanced analytics is to forecast future outcomes at a more granular level than what’s possible with traditional methods. For example, a company might employ advanced analytics to identify the likelihood that a specific customer or target market will purchase a product or service. 

  • Providing strategic guidance: Advanced analytics can identify potential courses of action for businesses faced with an uncertain future. Using predictive modelling and analyzing complex market dynamics, advanced analytics can help companies make more strategic decisions. 

  • Reducing bias: Cognitive biases are a well-known psychological phenomenon that can negatively impact decision-making in business and life. Using advanced analytics, however, businesses can minimize the impact of such biases by leveraging AI and machine learning to give a more information-driven, equitable perspective. 

  • Decrease risk: Risk is an inherent aspect of running a business or starting a new endeavour, whether it be launching a new product or creating a marketing campaign. Advanced analytics can help businesses decrease the risk associated with these undertakings by providing stakeholders with predictive models and prescriptive guidance. 

Advanced analytics use cases 

Many real-world examples of advanced analytics are operating today. Here are some of them to help you get a better understanding of how organizations use them daily: 

1. Detect fraud 

Unsurprisingly, some of the most important concerns in banking are fraudulent transactions in which an individual or entity withdraws money from a fund that is not their own. While this problem is as old (if not older) as banks, many banks today use such advanced analytics techniques as predictive detection and robotic process automation (RPA) to flag potentially fraudulent transactions when they occur.  

2. Improve marketing strategy 

When designing a new campaign, marketers closely monitor how much their efforts will cost versus how much they will earn by attracting new customers. As a result, some marketers employ an advanced analytics technique known as marketing-mix modelling (MMM). This models the interactions between marketing investments, sales drivers, and other external variables, like seasonality, to help marketers design short and long-term strategies. 

3. Identify growth opportunities 

One of the most common concerns for a successful business is how to grow and where to invest resources. Organizations can use advanced analytics to identify patterns using big data and spot new growth opportunities. 

Advance your skills with Coursera.

To implement advanced analytics approaches like machine learning, you'll need a grasp of the myriad of tools and techniques that make it possible. 

One way to gain the skills you need is to take a flexible online course, such as AI visionary Andrew Ng’s three-course Machine Learning Specialization, designed for beginners with only basic coding knowledge. Meanwhile, Ng’s Deep Learning Specialization will walk those with intermediate Python skills through building and training deep neural networks, CNNs, and RNNs. 

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