Understanding Advanced Analytics: Benefits, Techniques, and Examples

It’s not possible to jump in a time machine and travel to the future (at least not yet), but advanced analytics can provide the next best thing.

By combining the latest technology in data analytics, like artificial intelligence (AI) and machine learning (ML), with concrete data, businesses can more accurately forecast trends, events, customer behavior, and more. Advanced analytics are the best combination of tools businesses currently have to predict the future and proactively make the adjustments necessary to future-proof operations.

Let’s take a closer look at advanced analytics, including its concept, benefits, techniques, and use cases.

What Is Advanced Analytics?

Predictive modeling, ML and AI algorithms, business process automation, and other methods are used to glean maximum insights from data, all of which can be placed under the umbrella of advanced analytics.

Going beyond traditional business intelligence (BI) methods, advanced analytics gives more robust insight into future strategic decision-making by analyzing patterns and trends to predict the likelihood of certain events occurring.

The typical analytical process businesses follow when deriving insights from data comprises descriptive, predictive, and prescriptive analytics. 

Source

These are described in more detail in the table below:

These analytical processes should be viewed more like steps in gleaning insights rather than standalone. Advanced analytics is more of a blanket term that refers to applying the latest technologies in analytics to predictive and prescriptive analytics so that the insights consider more variables and provide deeper reasoning so that more effective business decisions can be made.

Why is Advanced Analytics Important?

Companies simply can’t afford to ignore the opportunities that big data bring. The world is moving faster than ever before in terms of innovation, and data is responsible for that. Through data sharing, exchange, and monetization, vaccines can be developed faster, help process temporary resident visa applications more efficiently, and much more.

Organizations are able to access a wide range of data through data sharing, exchange, and monetization efforts. Revelate is an example of a data marketplace and data exchange platform that allows organizations to increase access to their data, whether it be for external monetization or internal use, without compromising security. Users can access on-demand data via a customizable, white-labeled marketplace interface and have their dataset delivered to them immediately.

Taking advantage of the sheer amount of data that are generated every day is in the best interest of organizations. Advanced analytics is able to parse through data, garnering predictions, recommendations, models, and more that help organizations make quick, effective decisions in a fast-paced world.

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What Are the Benefits of Advanced Analytics?

1. Accurate Forecasting

One of the biggest shortcomings of traditional BI tools is that they are often unable to drill down and provide insight on more granular details, increasing the potential for missed opportunities and not providing as insightful forecasting than what would be possible with advanced analytics. By utilizing algorithms powered by AI and ML, advanced analytics can make more detailed connections between different types of data sources to determine patterns and trends better.

Organizations can use this information to make better predictions and proactive decisions about future events.

2. Faster Decision-Making

Because advanced analytics algorithms and processes significantly increase speed to insight compared to traditional processes, faster and more accurate business decisions can be made. Pre-emptive strategies can also be developed through the use of advanced business analytics, giving businesses more agility in dealing with potential future events.

3. Deeper Insight

Because advanced analytics are able to draw more information from data, businesses can make more impactful business decisions with the confidence that the information they’re working with will help them achieve their desired goals.

Advanced analytics tools are also able to recognize complex patterns in datasets and do so at a much faster rate than a person would be able to. These tools are also unbiased, ensuring that the insights provided are entirely based on data and not gut feelings or outliers.

4. Improved Risk Management

Because advanced analytics algorithms can pick up subtle patterns and trends, they are often used for risk management.

In the movie Office Space, a group of software engineers used a computer virus to steal a huge sum of money from their employer by skimming fractions of pennies from company transactions. If something like this happened today and the company was using advanced analytics algorithms, it would surely be detected, and the appropriate action would be taken.

5. Anticipate Problems and Opportunities

Often, businesses don’t realize there’s a problem until something happens, meaning they are taking a reactive approach to problem-solving rather than a proactive one. With advanced analytics tools and the ability to visualize insights into statistical models, businesses can identify potential problems before they occur, allowing them to change course or adjust their strategies.

Opportunities may also be gleaned during this process, such as a process bottleneck that can be fixed with a simple adjustment.

What Are Some Advanced Analytics Techniques?

There are a variety of advanced analytics techniques that organizations can use to gather insights from their data. The best advanced analytics technique depends on the type of data and what the organization hopes to use the insights to inform.

Here are several examples of the most common advanced analytics techniques:

Data Mining

In data mining, mathematical analysis is used to discover patterns, trends, and relationships from large data sets. Businesses can use data mining to derive actionable insights that can be used to influence business decisions and strategies. The information gleaned from data mining can be used in various data science tools, such as business intelligence, advanced analytics applications that involve historical data analysis, and even real-time analytics applications.

The four stages of data mining include:

• Data acquisition: The relevant data to use in an advanced analytics application is gathered. Data may be in various source systems, such as a data lake, warehouse, or database. The data acquisition step may also include gathering data from external sources.

• Data cleaning, preparation, and transformation: Once data has been gathered, it needs to be cleaned (removing incorrect, corrupted, duplicate, or incomplete information), prepared (manipulated into a format that can be analyzed), and transformed (conversion from one format or structure to another).

• Data analysis, classification, modeling, and forecasting: In this step, data is analyzed so that it can be used to create analytical models that can be used in making business decisions. It’s also organized via the business’s data naming and classification procedures and conventions, often with the assistance of data catalog tools.

• Reporting: Findings gleaned from the data are communicated through data visualization tools and data storytelling techniques, which involves presenting the findings in such a way that it goes beyond the technical side and is instead presented in an easy-to-understand visual and text format.

Sentiment Analysis

Just like the name suggests, sentiment analysis uses various techniques like natural language processing, computational linguistics, text analysis, and biometrics to determine the emotional tone behind the text. This determines factors like the writer’s attitude towards the topic, product, or service.

Common use cases of sentiment analysis include interpreting customers’ overall attitude towards a company by analyzing social media posts, public reviews, and comments on a forum website such as Reddit. Through sentiment analysis, businesses can gain the insight they need to improve customer service and their organization’s overall customer experience.

Cluster Analysis

In cluster analysis, data is organized into categories, groups, or clusters, depending on their similarities. In other words, data objects end up in the same cluster because they are more similar than data objects in another cluster. There can be a variety of criteria used for organizing data into various clusters depending on the data that are being collected for the cluster analysis.

Potential use cases of cluster analytics include:

• Network traffic classification: Gaining a better understanding of the different types of traffic that’s entering the network, including what is legitimate traffic and what portion of traffic is from spam or bots.
• Document analysis: Cluster analytics helps organizations deal with large volumes of documentation by enabling real-time organization. Themes can be identified in documentation through clustering algorithms that analyze the text in the documents and then organize them based on their contents.
• Marketing and sales: Clustering algorithms can group customers together that share similar traits, allowing the organization to create more personalized marketing campaigns that target specific groups of customers based on a variety of criteria, such as their likelihood of purchasing.

Complex Event Processing

With complex event processing, technology predicts the likelihood of certain high-level events occurring due to specific low-level factors. In other words, complex event processing looks for patterns in specific events.

For example, complex event processing could power an alarm at a data center that lets operators know that a certain external network link has failed, allowing them to act quickly and switch to a backup link.

A proximity sensor in a vehicle uses complex event processing to warn the driver that an object is near the vehicle and that they need to react accordingly, helping to prevent accidents.

Big Data Analytics

Many organizations have started analyzing big data sources to glean a higher level of insights than they would otherwise be able to if they relied on smaller data sources. Big data analytics consists of structured and unstructured data analysis to uncover patterns, correlations, trends, and more insights.

For example, financial institutions can use big data to capture new markets, improve fraud detection efforts, and reduce investment risk.

Machine Learning

With the development of machine learning, organizations can significantly increase the speed at which data is processed and analyzed. The entire data analysis process can be automated through machine learning, providing faster and more comprehensive insights than manual data analysis. Machine learning is a vital component of certain types of data analysis, such as predictive and prescriptive analytics.

Data Visualization

Once data is analyzed, it needs to be presented so that it can be understood by all stakeholders across an organization. Data visualization tools are essential because they help with data storytelling, allowing even non-technical individuals to understand what the data is trying to say. Visualizations include graphs, infographics, comparison images, charts, tables, etc.

What Are Some Advanced Analytics Examples?

Advanced analytics can improve business performance in various ways, from optimizing experiences to providing insight for new products or service development.

Here are several examples where using advanced data analytics results in positive outcomes:

1. Supply Chain Optimization

Supply chain analytics are essential for companies to make the appropriate strategic changes and decisions across hundreds of possible entities within their supply chain, all in the name of increasing efficiency.

Demand planning is an important use case for advanced business analytics. Demand planning uses historical data, like customer purchases, order frequency, and more, to determine needed inventory levels for businesses like manufacturers, suppliers, and distributors. Excess inventory increases costs, but not enough inventory causes lost sales. With advanced analytics, better predictions can be made to ensure companies can find that sweet spot with just enough inventory to meet demand.

2. Risk Management

With its ability to make accurate future predictions, advanced analytics is essential to risk management. Patterns and trends can be analyzed using AI-powered algorithms, allowing businesses like banks and manufacturers to reduce risk.

For instance, in March 2023, 41.9 million records were compromised by cybersecurity breaches, with big businesses like AT&T, Latitude Financial, and Go Anywhere being the most affected. When advanced data analytics are embedded into cybersecurity processes, businesses are better equipped to build models that help them identify potential risks and their impacts, allowing strategic decisions to be made on the financial and management level to respond to these risks.

3. Business Operations

Overall, business operations in various areas can be improved using advanced analytics. Here are some examples:

• Optimize workflows: The first and most obvious use case of data analytics to improve business operations is to improve efficiency by analyzing patterns in workflows and identifying bottlenecks and barriers that slow down the speed of work. This can be done by department or across the entire organization.
• Improve employee engagement and retention: Historical challenges like the need for better work-life balance, better wages, and more have given way to The Great Resignation, where people are leaving their workplaces for greener pastures in droves. This has caused employers to put increased effort into initiatives that help retain employees and find better employee matches. To power these initiatives, businesses use advanced analytics to identify ways to allow more flexible working schedules, provide more opportunities for growth, and offer more perks to employees by fixing process inefficiencies, reworking budgets, and strengthening hiring practices.
• Inform business strategies: Using historical data is paramount in predicting future possibilities and developing a plan to move forward. For instance, Mcdonald’s utilizes advanced data analytics in various ways. The McDonald’s app allows them to gain better customer intelligence insight and use that information to create personalized offers. Digital menus can be optimized to change menus depending on the time of day, weather, and product availability. Some Mcdonald’s locations even use licence plate readers, alongside geofencing technology, to alert a store when a customer is near so they can start preparing their order. The possibilities of using advanced data analytics to inform business strategies are really endless.

4. Negotiation

For manufacturers and retailers, developing and maintaining positive relationships with distributors, suppliers, and delivery companies is essential for business success. Advanced analytics helps by providing performance visualizations in key areas, such as cost fluctuations, time discrepancies (e.g., with delivery or fulfillment times), and more, allowing businesses to use these insights in future negotiations.

For example, a company can use advanced data analytics to determine customer cost of service, which is an important metric to determine whether some customers (typically the bigger accounts) cost more to serve than others, how the cost of service in one region differs from another, or how prices should be set for different customers.

To determine the cost of service, companies should utilize advanced analytics to draw insights from a variety of different categories, including:

• Order management overhead
• Customer service overhead
• Material and carrying costs
• Manufacturing costs
• Product returns and repair

5. Vendor Management

Sometimes, advanced business analytics can bring to light issues with vendor workflows, such as process bottlenecks causing delays, business agreements not being properly adhered to, and more. It’s important that conclusions about vendor performance are based on data rather than gut feelings or intuition to significantly reduce bias when vendors are evaluated.

For example, a computer GPU manufacturer could use a price scoring system to determine the performance of their vendors that source silicon:

• Vendor 1 offers a 300mm silicon wafer for $500, which has an average cost of $600. Since Vendor 1 sells the product for less than the average, they receive a transaction point of 1.
• Vendor 2 sells the same amount of silicon as Vendor 1 for $650, so they receive 0.5 transaction points.
• After totalling the point scores across all vendors, the manufacturer can determine which vendors outperform others with regard to pricing.

Of course, other metrics and considerations would be used as well, but this is just a simple example.

6. Identifying Growth Opportunities

Patterns and trends within big data can be analyzed using AI and ML to provide businesses with the information they need to determine the best places to invest resources that would otherwise be difficult or impossible to find without using big data. This could be for things like ideas for new products or services, new applications for existing offerings, or a new market to target.

McKinsey suggests that the most effective algorithm for growth discovery is network analysis and natural language processing (NLP) to parse through disparate text data sources and find the insights within them. For instance, a healthcare organization could use network analysis and NLP algorithms to analyze patient and diagnostic information, potentially making connections that determine more ailments that certain medications or treatments can be prescribed.

Advanced Analytics Tools

There are a variety of advanced analytics tools available. Each has its own benefits depending on the use case. These tools are outlined in the table below.

Conclusion

By taking advantage of advanced analytics, organizations can increase their speed to insight, as well as the comprehensiveness of those insights to make effective business decisions. As the reliance on data grows, organizations will need to prioritize high-tech tools to help them understand the entire objective view of their business.

Part of the conversation surrounding data is data democratization. This means making data available to those who need it internally within an organization and externally. Revelate is a data marketplace solution that allows ease of access to data through a customizable storefront while still enforcing security, access, and data governance measures. The best part? Revelate does it all through automation, taking the pressure away from IT teams to fulfill data orders.

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Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started