Advanced Analytics: What It Is, Benefits, and Examples

Businesses today produce and collect more data than ever before. Almost every platform, tool, business operation, and customer interaction creates data; but being able to access it all and gain value from your business information remains a challenge—especially if your data is complex or unstructured.

Fortunately, the growth and adoption of AI technology into analytics is making it easier to see a holistic view of your data. With advanced analytics, you can discover new patterns and insights within your data to improve your decision-making and performance. It even has the power to predict future outcomes, helping you find a competitive edge and stay ahead of changing trends. 

What is advanced analytics?

Advanced analytics is a specific sub-type of data analytics that uses advanced technologies and tools to analyze large volumes of complex data. Also called advanced data analytics, this methodology goes beyond the traditional statistical analysis of data, incorporating AI and machine learning (ML) algorithms to move from simpler descriptive analytics to more complex predictive analytics. 

With advanced analytics, you can analyze business data at a deeper level, which lets you uncover hidden patterns and develop real-time insights that traditional tools can’t offer. Instead of looking at what happened in the past to guide your future decisions, you’re examining current information and events for a more accurate understanding of what’s happening now and what to expect in the future. 

How advanced analytics differs from traditional analytics

There are three main ways that advanced analytics varies from traditional analytics.

Data sources

Traditional analytics methods can only process structured data in limited volumes. Advanced analytics, on the other hand, can handle more diverse data sets, including unstructured, semi-structured, and structured data. It’s also powerful enough to process big data from just about any source, from email or social media to Internet of Things (IoT) devices and even cloud-based tools and platforms. 

Techniques and technology

Advanced analytics uses more sophisticated techniques, including AI, ML, deep learning, natural language processing (NLP), and data mining. It also goes beyond simpler SQL queries, using programming languages like R or Python. Plus, you need specialized software to run advanced analytics, while traditional analytics can be done using Excel or basic BI tools. 

Focus

Traditional analytics looks at past data and events to help you make sense of what happened, while advanced analytics looks forward, answering what will happen. Advanced analytics uses AI and ML models to analyze current data, helping improve current performance and predict future trends and outcomes. It delivers real-time analysis, so you can make informed decisions based on events happening now. Plus, it can even prescribe actions to take to set your business up for the future.

Why is advanced analytics important?

Advanced analytics enables your organization to access insights from all types of data. Previously, businesses could only analyze structured data—predefined and formatted data that lives in databases. 

With advanced analytics, you can access all your data assets, no matter what format it’s in or where it’s stored. You can now learn from semi-structured data, such as JavaScript Object Notation (JSON), tagged social media posts, extensible markup language (EXL), or comma-separated values (CSV) files, to name a few. Advanced analytics can also process unstructured data like text-based documents, images, and audio files. 

Analyzing larger volumes of data—and more complex data sets—helps improve the accuracy of your data and insights. It also allows for greater context and a deeper understanding of the problem so you can develop more effective tailored solutions. 

Advanced analytics also gives you access to real-time data, so you can base business decisions on current information rather than relying solely on historical data. This element is crucial for all companies, especially those in fast-paced industries, where consumer sentiment and market trends can change rapidly. 

What makes it advanced?

Advanced analytics’ ability to handle large volumes of complex data and use more sophisticated methods and tools makes this type of data analytics more advanced than traditional analytics. It features technology like AI-powered analytics platforms that can collect, clean, and process all types of data at a faster rate than people, giving you access to more information sooner. 

AI and ML algorithms also power advanced capabilities like predictive analytics to forecast future trends and outcomes and allow you to customize insights or predictions based on different factors. Plus, advanced analytics offers a dynamic scope, updating models and results as data changes for the most accurate insights and predictions. These factors give you more effective results, allowing you to develop stronger strategies and optimize business performance and operations.

Advanced analytics techniques

Common techniques used in advanced analytics include:

Predictive analytics

This advanced analytics technique uses current and historical data sets, trained ML models, and statistical analysis to determine the likelihood of a specific event or action occurring. Using predictive analytics, you can forecast future trends or actions to help identify new business opportunities, reduce risk, and stay a step ahead of your competitors.

Data mining

This process sorts, or mines through, large data sets using ML and statistical analysis to find useful information and connect disparate data points into identifiable patterns or relationships. You can use data mining to find and describe a targeted data set or predict outcomes.

Prescriptive analytics

You can use this advanced analytics technique to identify the best course of action your business can take for any particular problem. Prescriptive analytics is similar to predictive analytics, as it relies on historical data, ML, and other mathematical models to predict future outcomes. However, it goes a step further by also providing specific actions to take based on its forecast. You will not only receive the answer to “What will happen?” but also know the answer to “What should we do next?” with prescriptive analytics. 

Big data analytics

Large, high-velocity data sets with structured and unstructured data, also called big data, can’t be processed without this advanced analytical technique. Big data analytics relies on AI and ML technology to identify trends, correlations, and other meaningful insights from data that would otherwise be difficult to interpret. It’s essential for assessing risk, detecting fraud, and analyzing trends.

Sentiment analysis

Use this technique to understand how customers or clients feel about your business. Sentiment analysis is a text-based advanced analytics process that calculates the feelings or emotions of others through reviews, social media posts or comments, and other forms of feedback. NLP tools can quickly determine if sentiment towards your company is positive, neutral, or negative and track how it changes.

Complex event processing

Complex event processing can recognize patterns or suspicious activity across numerous events and on different systems as they occur. This advanced analytics technique is used to detect and flag fraudulent activity or potential cyber threats, but it can also be used to find new opportunities to grow your business.

No matter which advanced analytics technique you use, you’ll need to consider the following factors for a successful practice:

• Data quality: While advanced analytics tools can automate data processing, you still need to ensure good data quality for accurate insights and predictions. Regularly cleaning and refining your data can help avoid problems.
• Required infrastructure and integrations: Your infrastructure must be able to handle large volumes of complex data, both structured and unstructured. Scalable solutions are ideal, as they can adjust to your data needs. You also need to consider your existing systems and how they will integrate with an advanced analytics practice, especially in terms of syncing data in real time. 
• Skilled employees: While many advanced analytics processes are automated, you still need trained personnel to operate and manage the tools and perform any necessary integrations to get your practice up and running. Many advanced analytics platforms offer low-code and no-code options, which lower the technological skill barrier for entry.
• Clear business objectives: To make the most out of your advanced analytics practice, you first need to identify the business goals you want to achieve and determine what you need to do to meet them. This step involves identifying your key performance indicators and planning how you will collect, analyze, and implement insights or predictions generated from your data. 
• Data privacy and governance: Before starting an advanced analytics practice, ensure your business has strong data governance policies in place to limit access, protect sensitive information, and stay compliant with industry regulations.

Technologies used in advanced analytics

In addition to the above techniques, specific technologies make advanced analytics possible. 

Artificial intelligence and machine learning

AI technology is the foundation of advanced analytics, automating data processing and analyzing data for patterns with minimal input from people. The growth of machine learning is equally important, as it enhances the role and speed of AI and powers predictive analytics. Both AI and ML continuously learn from data, using historical information to predict future outcomes with increasing accuracy. Other AI technologies, such as NLP and deep learning, enable advanced analytics techniques like sentiment analysis and finding hidden patterns in your data.

Cloud computing

Cloud computing allows for the delivery of computing services, including databases and analytics, over the internet. Cloud-based platforms and services are ideal for advanced analytics because they offer flexibility and can quickly scale as your data needs change. They’re also faster and more responsive, so you can access real-time data from across different systems.

Big data platforms

These platforms are able to store, process, and analyze diverse, high-speed big data sets efficiently and in real time. Big data platforms allow you to run more complex data queries and are essential for predictive analytics, prescriptive analytics, fraud detection, and more. 

Analytics and visualization tools

BI and advanced analytics tools and platforms help you make the most out of your data and analytics practice. These tools can integrate with your various data sources and update in real time. They also allow you to create and train ML models for predictive analytics. Analytics and BI platforms typically include visualization tools, so you can easily turn complex data into easy-to-understand interactive charts, graphs, and tables. 

Benefits of advanced analytics

Advanced analytics offers businesses numerous benefits to help predict future trends, anticipate risk, maximize performance, and make fully informed decisions across operations. Some of the benefits you can expect include:

• Develop deeper insights: With access to unstructured and semi-structured data, you gain a deeper level of understanding about who your customers or clients are, their behaviors and preferences, and industry or economic trends impacting your business. Developing deeper insights allows you to create more strategic and effective plans.
• Forecast future outcomes: Predictive analytics models give businesses an accurate picture of what is likely to happen in the future. For example, a retailer can use it to determine which segment of their target audience is most likely to purchase a specific product. You can also use “what-if” scenarios to prepare for different possible outcomes.
• Anticipate problems and reduce risk: AI and ML models can identify patterns in your data that humans can’t and send automated alerts, exposing potential problems or risks before an issue develops so you can quickly take corrective action.
• Identify new opportunities: ML models and predictive analytics look for trends in customer behaviors and changes in the market to identify new areas for growth or improvement.
• Make faster business decisions: Advanced analytics examines more information at a faster rate than traditional analytics, and its ML models become more accurate with use, all of which allow you to draw conclusions faster while feeling confident the actions you take will yield your desired outcomes.
• Increase ROI: With access to real-time analysis of your business data and market trends, you can make decisions or adjust operations on the fly, leading to more sales, improved operational efficiency, and a better return on your investments.

Advanced analytics examples and use cases

Businesses across industries can use advanced analytics to drive growth and make smarter decisions. Here are a few examples:

Banking and financial services

Banks and other financial institutions use deep learning neural networks and ML to identify patterns in transaction data that may indicate fraud. They also use predictive analytics to forecast and mitigate risk in a number of areas, including investments and loan, mortgage, and credit card applications.

Retailers

Online and brick-and-mortar retailers use advanced analytics to streamline different business operations like supply chain management and logistics to improve delivery efficiency and customer satisfaction while reducing costs. Predictive analytics also helps retailers forecast consumer demand and market trends, which can prevent out-of-stock or overstocked items. Retailers are also using it for dynamic pricing since it can analyze real-time data and adjust for changing demand, supply, and competitor pricing. 

Healthcare

Advanced analytics is changing how patients receive care and how medical facilities operate. Analyzing historical and current patient data lets practitioners develop personalized care plans and even predict potential future health problems that can be addressed now. Hospitals and other care facilities can use predictive analytics to forecast admissions and schedule staff accordingly, further improving operations.

Manufacturing and supply chain management

Companies can forecast demand and market trends with advanced analytics to ensure their supply chain stays agile as conditions change. Manufacturers also use this technology to conduct predictive maintenance, which reduces downtime and ensures greater efficiency throughout the process.

Advanced analytics allows you to transform data into strategic business decisions through AI, ML, predictive analytics, and other sophisticated techniques. Businesses will need to embrace advanced analytics to stay competitive in a fast-paced environment, meet consumer demands, and generate more value from their data. See how you can improve your business performance and generate valuable insights with Domo’s analytics tools.