Advanced Analytics: Definition, Benefits, and Use Cases

Key takeaways 

Advanced analytics is a collection of data analytics techniques, such as machine learning and predictive modeling, used to improve decision-making. 

• Advanced analytics uses complex techniques, including machine learning, to predict future possibilities and inform strategic planning. 

• Businesses can leverage advanced analytics to navigate complex markets, predict market trends, improve decision-making by reducing bias, and decrease risk. 

• You can perform advanced analytics using popular data science and analytics tools, such as SAS Viya and Altair RapidMiner.

Learn 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. Afterward, if you're interested in learning some of these advanced techniques yourself, consider enrolling in the Google Advanced Data Analytics Professional Certificate for expert instruction. Upon completion of this advanced-level, eight-course series, you'll have had opportunities to build machine learning models for data analysis, create visualizations, and learn how to communicate relevant insights. You'll also earn a career credential that highlights your newly gained expertise.

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 artificial intelligence (AI). Typically, businesses employ advanced analytics to forecast future outcomes and guide their decision-making, not just to gain business insights.

Organizations use advanced analytics for a wide range of purposes, including to identify emerging market trends, reduce bias in decision making, and anticipating complex market dynamics [1]. 

Advanced data analytics vs. business intelligence 

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

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. As a result, BI is used for descriptive analytics, or the analysis of both past and present data to describe how a business is currently performing. 

For example, a streaming platform might employ BI practices to identify the number of subscribers it currently has and its most popular content. Equipped with this knowledge, the business might then begin promoting its most popular content in order to retain current subscribers and obtain new ones. 

Advanced analytics, meanwhile, 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, meaning that it’s used to predict future outcomes and prescribe a course of action. 

The same streaming platform, for instance, might use advanced analytics to both identify past seasonal trends and also forecast how those trends will play out again in the future. These predictions, in turn, could direct their long-term business decisions, such as producing new content that better aligns with these trends. 

Advanced analytics techniques

Advanced analytics encompasses a variety of impactful techniques that can help companies make more strategic decisions.

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's used to classify data. In machine learning, it's an example of an unsupervised algorithm.

• Cohort analysis breaks large volumes of data down into classifiable, related groups, or “cohorts.” Often, businesses use cohort analysis for customer segmentation, which helps direct 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 representations like charts, bar graphs, and heat maps.

• Complex event processing (CEP) is a method for tracking and analyzing data in real time to derive actionable conclusions. It equips organizations with deeper insights into what is currently happening within their business and operations.

• 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. 

Learn more about machine learning in the following lecture from Deeplearning. AI and Stanford's Machine Learning Specialization. 

Advanced analytics benefits 

Advanced analytics provide businesses with a toolkit of data analytics 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 segment 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 that 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. 

Generative AI and data analytics

Generative AI tools like ChatGPT, Google Gemini, and Microsoft Copilot are already augmenting how data professionals do their work, including how they use advanced analytics. Some ways data professionals are leveraging generative AI include writing code, automating certain tasks, and quickly analyzing large amounts of data for trends and patterns.

Learn more: 5 Benefits of AI to Know

Advanced analytics use cases 

There are many real-world examples of advanced analytics operating today. Here are just some of the ways organizations use it to perform a wide range of tasks:

1. Detect fraud 

Unsurprisingly, financial institutions spend a significant amount of their time focusing on ways to prevent fraudulent transactions, which involve an individual or entity withdrawing 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 advanced analytics techniques such 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. 

Read more: Marketing Strategy: What It Is and How to Create One

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 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 in revenue [1]. 

Advanced analytics tools

There are countless tools you can use for advanced analytics. Some of the most popular include Google Cloud, IBM Watson Studios, MATLAB, Altair RapidMiner, Microsoft Azure Machine Learning, and SAS Viya.

Explore free resources to build your analytics skills

Stay updated on emerging trends in the analytics world and get in-depth career advice with our free weekly newsletter, Career Chat. Other resources that can help include:

• Explore career options: Data Analysis Career Progression: Job Levels and Skills to Advance

• Watch a video: 7 Essential Data Analytics Skills for Beginners

• Hear from a fellow learner: Meet the 18-Year-Old Getting His Master’s Degree in Data Science

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