Advanced analytics is the process of using complex machine learning (ML) and visualization techniques to derive data insights beyond traditional business intelligence. Modern organizations collect vast volumes of data and analyze it to discover hidden patterns and trends. They use the information to improve business process efficiency and customer satisfaction. With advanced analytics, you can take this one step further and use data for future and real-time decision-making. Advanced analytics techniques also derive meaning from unstructured data like social media comments or images. They can help your organization solve complex problems more efficiently. Advancements in cloud computing and data storage have made advanced analytics more affordable and accessible to all organizations.
What are the use cases of advanced analytics?
Your organization can use advanced analytics to solve complex challenges beyond traditional business analysis and reporting. Here are some examples across industries.
Healthcare
Healthcare and life science companies analyze clinical and operational data to decrease care costs while boosting diagnosis accuracy. For example, advanced analysis of medical images supports precision diagnosis. Similarly, they use advanced analytics to turn patient, genomic, transcriptomic, and other omics data into actionable insights. It accelerates clinical trials, enhances research and innovation, and simplifies clinical multiomics.
Finance
Financial services can enhance operational processes and innovation using data-driven insights from transformative technologies. For example, they can use advanced analytics for these purposes:
- Optimize critical banking operations
- Drive transformation and reimagine business models in capital markets
- Modernize core systems and enhance risk modeling in insurance
The industry can perform data mining to transform experiences for stakeholders, employees, intermediaries, and customers. Advanced analytics helps companies make better decisions for profitability and customer satisfaction.
Manufacturing
The manufacturing industry uses advanced analytics to improve overall equipment effectiveness (OEE). Diagnostic and predictive analytics improve equipment maintenance and monitoring. Additionally, the manufacturing sector can do the following:
- Improve processes by identifying and remedying bottlenecks
- Detect real-time anomalies in equipment
- Automate inspection, verification, and other time-consuming manufacturing processes
Retail
The retail industry uses advanced analytics technologies to create smart stores, streamline digital commerce, and build toward an intelligent supply chain. They can derive insights from customer interaction and behavior for many purposes:
- Improve merchandising decisions and develop effective merchandising strategies
- Boost customer lifetime value by personalizing product recommendations
- Optimize internal business operations to lower costs and improve margins
- Democratize access to data to innovate and accelerate positive outcomes
What are the types of advanced analytics?
Advancements in data science have helped develop several distinct focus areas within the field of analytics.
Cluster analytics
Cluster analysis organizes data points into groups based on similarities. It doesn’t require initial assumptions about the relationship between data points, so you can find new patterns and associations in your data.
For instance, you can use cluster analysis to create demographic or psychographic categories within customer bases. You can then plot the relationship between one quality and another. You could trace whether there’s a relationship between certain demographics of customers and their buying habits.
Cohort analytics
Like cluster analysis, cohort analysis divides large data sets into small segments. However, it tracks a group’s behavior over time. On the other hand, cluster analysis focuses on finding similarities in the dataset without necessarily considering the temporal aspect.
Cohort analysis is often used in user behavior and retention studies. You can use it to trace how each cohort responds to different events. This advanced analytics method improves customer retention, user engagement, product adoption, and interaction.
Predictive analytics
Traditional descriptive analytics looks at historical data to identify trends and patterns. Predictive modeling uses past data to predict future outcomes. You mainly use predictive analysis in risk-related fields or when you want to find new opportunities. By seeing potential future scenarios, you can make better decisions with confidence. It contributes to risk reduction and increases operational efficiency.
Prescriptive analytics
Prescriptive analysis recommends actions you can take to affect a desired outcome. Beyond just showing future trends, prescriptive analytics suggests different courses of action to best take advantage of the predicted future scenario. For instance, imagine a business scenario where predictive analytics tells you which customers are most likely to churn in the next quarter. Prescriptive analytics suggests specific retention strategies tailored to each at-risk customer segment, such as special discount offers, loyalty programs, or personalized communication campaigns.
What are the technologies used in advanced analytics?
Advanced analytics solutions combine several technologies.
Machine learning
Machine learning models like time series analysis, decision trees, and regression support advanced analytics. In predictive and prescriptive advanced analytics techniques, you need to understand data and extrapolate from it to make observations. ML techniques can help in predictive suggestion and monitoring by rapidly processing millions of data points. Advanced analytics uses machine learning for future forecasting.
Artificial intelligence
Advanced analytics uses artificial intelligence (AI) to simulate a human level of intelligence. With AI, you can process more data, work on more complex modeling, and incorporate increasingly abstract algorithms.
AI tools include a range of technologies that work together to enable advanced analytics to perform various functions. Deep learning techniques utilize neural networks with many layers to analyze various forms of data, such as images, sound, and text. For example, natural language processing (NLP) extracts information from human-generated text, and computer vision interprets images and videos to perform tasks.
What are the essential infrastructure technologies required for advanced analytics?
The following elements are needed to run a successful advanced analytics program in your organization.
Internet of Things
You require Internet of Things (IoT) devices to capture real-world data for advanced analytical processing. Smart sensors provide real-time data streams that give you access to continuous information. Without IoT devices to feed your analytical systems, you may miss valuable insights and useful decision-making context.
Storage
Advanced analytics software requires big data sets to provide precise and useful insights. Big data requires storage systems with capacity, fault tolerance, availability, and automatic backup capabilities. You must be able to automatically integrate data from diverse sources into your storage system. It should also support built-in integrations with machine learning and other data analysis software.
Computing
Advanced analytics tools need a high data processing capacity to function effectively. You should be able to support distributed computing so multiple server nodes can process the data simultaneously. You also require computing capabilities for your predictive models and other machine learning and artificial intelligence tools.
Visualization
You require data visualization tools to create and present reports on the findings of your advanced analytics. Without clear visualization, it is challenging to act upon the insights generated. You may discover deeper insights with advanced analytics, but it can be hard to share them. With visualization technology, you can share those insights with your business users and nontechnical stakeholders.
Security
Access control, encryption, and protection against unauthorized access are vital throughout the analytics process. Data security is essential as most analytics deal with sensitive data, especially in fields like finance or healthcare. Equally, you may have data privacy regulation standards to comply with.
