Comprehensive Guide to Advanced Analytics Techniques and Benefits
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Most organizational decisions today are insight-driven, with data being the king. Considered to be perhaps the most important asset of a modern business, it is, however, equally critical to fully and correctly capture the potential of a company’s data. Traditional methods are no match for the complexity of data in a modern-day enterprise, and businesses need to turn to advanced analytics techniques.
As the name suggests, advanced analytics, or data analytics, covers a wide range of cutting-edge methods for collecting, analyzing, and applying data. You’d normally find that machine learning algorithms, natural language processing (NLP), predictive modeling, artificial intelligence (AI), and computer vision are among business’ most commonly used advanced analytical tools.
Let’s delve a bit deeper.
What is Advanced Analytics?
Advanced data analytics is the study of current or past events and deciding the course of action to take today so a business can improve future business outcomes. An enterprise can apply a wide range of tools, such as math, statistics, artificial intelligence (AI), machine learning, and programming tools in pursuit of this target. Advanced analytics relies on three categories of data – structured, semi-structured, and unstructured – to help in these predictions.
Is advanced analytics the same as business intelligence? While BI looks at what happened and the many events associated with the occurrence, advanced analytics is more forward-looking and attempts to predict future outcomes based on decisions taken in the present.
The reason more businesses are turning to advanced analytics is that it offers so much more than just ways to improve operational efficiencies. With advanced analytics, businesses can:
- better anticipate customer needs and understand their preferences
- discover new opportunities
- make informed decisions
- improve operations and products, and
- reduce data security threats by proactively identifying and eliminating risk areas.
What Are the Techniques Used in Advanced Analytics?
Numerous advanced analytics techniques can be used by businesses looking to generate meaningful insights from their collected data. Some are more complex than others and each has its own set of benefits to the business. Let’s take a look at some advanced analytical methods in use today:
- Big Data Analytics: In this method, large volumes of data – structured, semi-structured, and unstructured data – are studied to detect patterns that humans might miss. Typical examples include market trends or customer preferences.
- Machine Learning: Everyone talks about Machine Learning today. This AI technique is designed to assist software applications in predicting future outcomes based on historical data. Businesses find use for machine learning in recommendation engines and fraud detection.
- Data Mining: This method takes off from big data, using the mined information to derive useful insights that help in the decision-making process. Here, statistical models and machine learning are applied to the data collected, processed, and analyzed by big data analytics.
- Data Visualization: As the name suggests, this method is primarily used to share complex information with a larger target audience. The method breaks down complex concepts into easier demonstrations that can be easily understood.
What Are the Benefits of Advanced Analytics?
Companies that have implemented advanced analytics in their operations have noticed a significant boost in several key areas, giving them a competitive edge in the marketplace. Some of the key benefits include:
- Higher Return on Investment (ROI): When advanced analytics is deployed in all areas of an enterprise’s operations, the first result is usually improved efficiency, thus reducing operational costs. While the cost savings don’t immediately mean ROI, the saved funds can be re-invested into a different project, and that can generate additional ROI. Additionally, the insights gained can help businesses analyze market trends and direct funds into better customer service or improve supply management to eliminate bottlenecks.
- Predicting Problems and Opportunities: Advanced analytics can study past trends and predict future challenges or anticipate a new opportunity. The analytics even can create different business scenarios by modifying the data. This can help demonstrate better outcomes, reveal worst-case scenarios, cut costs, help understand risk, and even improve decision-making.
- Enable Deeper Insight: When applying advanced analytics, structured, semi-structured, and unstructured data are combined for to gain deeper insights and help in better decision-making. Unstructured data, usually found in emails, business documents, social media sites, and web pages, gives businesses a complete view of the organizations, allowing for more accurate decision-making.
- Better Risk Management: Advanced analytics presents businesses with accurate data that helps reduce risks by anticipating future business outcomes. Armed with the right knowledge, an enterprise can practice improved enterprise risk management and significantly reduce operational, financial, or strategic risk.
What are Some Examples of Advanced Analytics in Action?
As we’ve mentioned before, advanced analytics has the potential to drive business growth and can be instrumental in optimizing operations across the organization. Its applications can be seen in numerous industries, a few of which are detailed below.
Retail
The right application of advanced analytics can help in:
- Customer segmentation, to make targeted marketing and sales efforts.
- Demand forecasting, to help optimize inventory and improve the supply chain.
- Price optimization, to analyze customer behavior, market conditions and competitor pricing to correctly price their products.
- Personalization, to recommend products based on customer’s purchases and browsing history.
- Fraud detection, to identify and restrict fake reviews or fake accounts.
- Sentiment analysis, to study customer reviews and feedback and identify areas for improvement.
Finance
The industry benefits from advanced analytics with:
- Credit risk assessment, that can predict a possibility of default and fix appropriate loan terms.
- Fraud detection, to identify and take action against fraudulent activity.
- Risk management, to identify and manage risk.
- Personalization, to recommend financial products based on past behavior.
Ed-tech
Advanced analytics can help with:
- Student performance prediction, to predict a student’s performance based on past performance.
- Adaptive learning, to create personalized learning experiences as per students’ progress and needs.
- Student retention, to identify factors in student dropout rates and improve retention.
- Plagiarism detection, to identify and prevent fraudulent activities.
- Student success prediction, to predict a student’s result based on past data.
Also Read: What is workforce analytics and why is it important for business?
Conclusion
Done right, advanced analytics is worth the investment in time, resources and effort. However, it requires the commitment and participation of management and people who must work together and strive to create a data-driven culture. It requires change, undoubtedly, but the resultant increase in efficiency across the organization and reduced costs makes it a step that cannot be ignored in today’s competitive times.
Frequently Asked Questions
Q1. What is Advanced Analytics?
Data rules the world today and businesses take all their operation-critical decisions based on insights derived from data. However, as technology advances, so do business operations and traditional methods of data collection and analysis no longer suffice. Simply put, advanced analytics is the study of current or past events to decide the course of action today to improve future business outcomes.
Q2. What Are the Types of Advanced Analytics Techniques?
Advanced analytics calls for the collection and study of vast amounts of operational and other business data. Every business has individual requirements. However, the most commonly used advanced analytics techniques include:
- Big Data Analytics, where large volumes of structured, semi-structured and unstructured data are studied to detect patterns.
- Machine Learning assists software applications in predicting future outcomes by learning from historical data.
- Data Mining uses mined information to derive useful insights for the decision-making process.
- Data Visualization is used to simplify complex information to share with a larger target audience.
Q3. What Are the Top Benefits of Advanced Analytics?
Some of the key benefits of advanced analytics include:
- Higher Return on Investment: Improved efficiency leads to reduced operational costs, where the saved funds can be reutilized in different projects, thus generating RoI.
- Predicting Problems and Opportunities: The study of past trends helps predict future challenges or anticipate a new opportunity before it arises.
- Enable Deeper Insight: The combined results of studying structured, semi-structured and unstructured data provides deeper insights and helps in better decision-making.
- Better Risk Management: The insights and knowledge gained can help a business be better prepared for risks and significantly reduce operational, financial, or strategic risk.
