Advanced Analytics: Definition, Benefits, and Use Cases

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Advanced analytics employs some of the most complex data analytics techniques to help businesses and other organizations improve their decision making.

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Advanced analytics is a collection of data analytics techniques, such as machine learning and predictive modeling, 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 own advanced analytics skills today. 

What is advanced analytics?

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

Businesses use advanced analytics, consequently, for a wide range of purposes, from using it to identify emerging market trends to reducing bias in decision making and anticipating complex market dynamics [1]. 

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 historic data. As a result, BI is used for descriptive analytics, or the analysis of both 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 currently have and the most popular content currently on the platform. Equipped with this knowledge, the business might begin promoting that content in order 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 decision-making for businesses. As a result, advanced analytics is used for both predictive and prescriptive analysis, meaning that it’s used to predict future outcomes and prescribe a course of action. 

The same streaming platform, for example, might use advanced analytics to not only identify historic seasonal trends but also forecast how those trends will play out in the future. These predictions, in turn, could direct their long-term business decisions, such as producing more content to align with future trends. 

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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 the benefits, techniques, and use cases for advanced analytics. 

At its core, advanced analytics is really just a collection of data analytics techniques and methodologies. While some of these techniques (like data visualization and mining) are well-understood by entry-level data analysts, others (like deep learning and predictive modeling) are more often used by data scientists, data engineers, and machine learning engineers

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

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

  • Data mining is the process of identifying trends, patterns, and anomalies within data sets. To accomplish this task, 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 their brand might be perceived by potential customers. 

  • Cluster analysis is the process of analyzing data to identify clusters of related data points. Typically, it is used 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 seeks to convey findings through easily understood visual representation.  

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

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

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Advanced analytics benefits 

Advanced analytics provide businesses with a tool kit of data analytic 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 provide guidance to businesses as they maneuver 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 business 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 modeling and analyzing complex market dynamics, advanced analytics can help businesses make more strategic decisions. 

  • Reducing bias: Cognitive biases are a well-known psychological phenomenon, which can negatively impact decision-making in both business and life. Using advanced analytics, however, businesses can reduce 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 endeavor, 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 

There are many real-world examples of advanced analytics operating today. Here are some of them to help you get a better understanding of how they’re used every day: 

1. Detect fraud 

Unsurprisingly, one of the most important concerns in the world of banking is 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) than banks themselves, today many banks 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 keep a close eye on how much their efforts will cost versus how much they will earn by attracting new customers. As a result, some marketers today employ an advanced analytics technique known as marketing-mix modeling (MMM), which can model the interactions between marketing investments, sales drivers, and other external variables like seasonality to help marketers design both 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. To help spot new growth opportunities, advanced analytics can be used to identify patterns using big data

According to a 2021 report by McKinsey and Company, for example, a chemical company used advanced analytics to scan 120 million patents and 100 million academic papers to identify new possible patent applications. Ultimately, the company identified 30 high-yield patent applications, which led to an additional $50 million of revenue [1]. 

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To implement advanced analytics in your career, you need a grasp of the myriad of tools and techniques that comprise it. 

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

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Article sources

1. McKinsey & Company. “The strategy-analytics revolution, https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/the-strategy-analytics-revolution.” Accessed August 24, 2022.

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