What is advanced analytics?
Advanced analytics is the collection and examination of data using sophisticated methods and tools. These methods move past traditional business analytics and use machine learning, artificial intelligence, or simulations to discover deeper insights so companies make educated decisions.
Businesses use predictive analytics software to automate and streamline these analytics methods. Many of these platforms support dataset creation, data visualizations, predictive models, structured data, and unstructured data. This applies to both stored and real-time data.
Types of advanced analytics
Below are the four main types of advanced analytics.
- Data mining involves sifting through large datasets to uncover trends, patterns, and other insights that aren’t necessarily obvious on the surface level. Some methods of data mining include regression analysis, anomaly detection, and text mining.
- Predictive analytics combines current data with historical data, resulting in future outcome prediction. A common example of predictive analytics is the weather forecast.
- Prescriptive analytics works hand-in-hand with predictive analytics. Prescriptive analytics calculates a business’ next steps based on the predicted outcomes. Deep learning and other advanced methods are often used in this kind of advanced analytics.
- Big data processes massive amounts of data and uses it to derive outcomes and predictions. This type of data grows exponentially over time and is notoriously hard to keep up with.
Benefits of advanced analytics
Many companies decide to invest in advanced analytics methods to better understand their customers, grow their business revenue, expand their target market, and make better decisions. Other advantages include:
- Sophisticated insights. Businesses make more informed decisions based on historical behavior and future predictions.
- Consumer relationship improvement. By understanding their target market and ideal customers, companies can use those insights to better performance across all teams and departments.
- Scaling and automation. This provides the ability to include a wider number of variables. Less time is spent building reports, and more time is spent strategizing and acting on the data at hand.
- Less risk. Using predictive analysis makes it possible to forecast certain outcomes based on key business decisions.
- Faster decision-making. Advanced analytics methods process large quantities of data faster than humans or other methods of analysis, resulting in quicker decisions.
- Increased personalization. Advanced methods like artificial intelligence and big data can create personalized predictions and risk factors for specific groups of people. This can help improve customer retention and acquisition.
- Increased company-wide visibility. It’s easier to share business operations and details with the rest of the company.
Advanced analytics best practices
When working with different advanced analytics methods, follow these tips and best practices to ensure success.
- Clean your data. Be sure to maintain the business’s dataset and clean up any anomalies.
- Don’t cut corners. Setting up these methods takes time, effort, and patience. Take your time at the beginning of the process because that will affect the end results.
- Don’t rely on predictive data. Sometimes systems go down, or predictive analysis gets things wrong. Maintain the ability to make human-driven observations and decisions.
- Hire analytics specialists. Highly technical setup processes are common with advanced analytics methods and require very specific knowledge.
- Maintain data security. Limit access to sensitive data. Strict data security is vital for both customers and employees.
Advanced analytics vs. business analytics
Advanced analytics involves many kinds of analytical methods, including predictive analysis, prescriptive analytics, and data mining. The main goal of advanced analytics is to surface patterns, trends, potential risks, and future outcomes. Advanced analytics methods answer the questions why, what if, and what else.
Business analytics analyzes an organization’s data and performance, which are both diagnostic measurements. Maintaining and observing these datasets helps increase revenue, productivity, and efficiency. Business analytics methods answer the questions what, when, and how many.
