Advanced analytics services entail various data analytics techniques, such as machine learning and predictive modeling. These enable businesses to gain deep insights into their operations and customers. Companies rely on advanced analytics to detect patterns and predict outcomes and are better positioned to make informed choices that drive growth and profitability. Whether it is fraud detection, market trends prediction, or resource optimization, advanced analytics services offer a critical edge for businesses looking to remain competitive. In this article, we explore advanced analytics, the techniques that define it, how it differs from other data analysis methods, and its real-world benefits.
Advanced analytics services—what it is
Advanced analytics services, broadly speaking, is the culmination of different data analysis techniques such as machine learning, predictive modeling, neural networks, and AI. Businesses employ advanced analytics to gain insights, forecast future outcomes and guide their decision-making. Companies use advanced analytics for various purposes—from anticipating complex market dynamics to identifying emerging market trends or reducing bias in decision-making.
Advanced analytics vs. business intelligence
Although advanced analytics and business intelligence share some similarities, they have key differences. Business intelligence uses standard data analytics methods such as data visualization and statistical analysis to generate actionable insights based on current and historical data. In that sense, BI is primarily used for descriptive analytics—to describe how a business is currently performing.
For example, when a business employs BI practices on a streaming platform, it can determine the number of subscribers and the most popular content. Equipped with this knowledge, the business can promote that content to retain current subscribers and obtain new ones.
On the other hand, advanced analytics employs more complex data analytics techniques, such as machine learning to make business predictions. This means that advanced analytics is used for both predictive and prescriptive analytics—to predict future outcomes and prescribe actions.
The business can use the same streaming platform and leverage advanced analytics to identify historical seasonal trends and forecast how those trends can impact the future. These predictions can direct business decisions, such as the type of content to produce to align with future trends.
Advanced analytics services—techniques
Advanced analytics are techniques for analyzing data. But some techniques, such as deep learning and predictive modeling, are generally only used by data science, engineering, and machine learning experts. However, some basic techniques, such as data visualization and mining, are easy for first-time data analysts to understand.
Here are some of the techniques to further explore advanced analytics:
- Predictive modeling pairs machine learning and statistical algorithms to forecast future outcomes using current and historical data.
- Data mining employs statistics, machine learning, and AI toidentify trends, patterns, and anomalies within data sets.
- Sentiment analysis mines text and leverages natural language processing (NLP) to identify whether the data is positive, negative, or neutral. This technique helps businesses understand their brand and how potential customers perceive it.
- Cluster analysis is typically used to classify data which analyzes data and identifies clusters of relevant data points. It is an unsupervised algorithm in machine learning.
- Cohort analysis breaks down large volumes of data into smaller, classifiable groups or cohorts. Businesses use this technique to fragment customer information into specific segments and gain insights to execute targeted marketing efforts.
- Data visualization is a common data analytics practice that conveys findings through easily understood visual representation (graphic or pictorial).
- Complex event processing (CEP) tracks and analyzes data in real-time to derive actionable conclusions.
- Machine learning (ML) is a technique to build analytic models capable of autonomous learning. These models can be trained with large data sets that incrementally alter the algorithm with each iteration.
Advanced analytics services: use cases
A recent report by McKinsey and Company states that a chemical company used advanced analytics to identify new possible patent applications. They scanned 100 million academic papers and 120 million patents for this purpose and identified almost 30 high-yield patent applications. It led to an additional $50 million in revenue.
Here are several common, real-world examples of advanced analytics:
1. Detect fraud transaction
One of the most critical concerns is fraudulent transactions in banking. Today many banks use advanced analytics for predictive detection and robotic process automation (RPA) to flag potentially fraudulent transactions when they occur.
2. Improve marketing strategy
To create a new campaign, marketers triangulate their efforts, costs, and revenue in order to attract new customers. Some marketers today employ an advanced analytics technique—marketing-mix modeling (MMM). This technique helps marketers analyze the sales drivers, marketing investments, and other external variables—such as seasonality—to design short and long-term strategies.
3. Identify growth opportunities
Successful businesses look for ways to identify potential growth opportunities and invest their resources there. Advanced analytics can be used to identify these patterns using big data.
Advanced analytics services—benefits
Advanced analytics services have a range of business benefits: make better decisions, assess risks, and predict future trends. Advanced analytics can also guide businesses through shifting market dynamics. Here are some benefits of advanced analytics services:
- Better forecasting: Businesses use advanced analytics to forecast future outcomes at a more granular level. For example, a business can employ advanced analytics to identify the probability that a customer will purchase a product or service.
- Provide strategic guidance: Advanced analytics can help make more strategic decisions by identifying potential courses of action for businesses.
- Reduce bias: Cognitive biases can negatively impact business decision-making. Fortunately, advanced analytics can reduce the impact of such biases by leveraging machine learning and AI to give fair conclusions.
- Decrease risk: Risk is an innate aspect of any business; it can be launching a new product or creating a marketing campaign. Advanced analytics can minimize these risks by providing stakeholders with predictive models and prescriptive guidance.
PreludeSys’s expertise in advanced analytics services
PreludeSys understands that the data-rich landscape of modern business is complex. Our Microsoft-based advanced analytics services and solutions allow companies to access deep insights buried in their data, empowering them to make better decisions and stay ahead of the competition. Our solutions leverage Azure Analytics which enables users to draw informed conclusions from complex data sets, positioning their companies to navigate shifting industry trends successfully. Whether you’re looking to improve efficiency, maximize profits, or identify new opportunities, our expert team guides you; take advantage of industry trends and scale your operations. PreludeSys helps businesses unlock the full potential of their data and drive success.