Advanced Analytics: Techniques, Examples, and Benefits

Data is the most important asset for any modern organization, backing most business-critical decisions today. However, fully capturing the potential of the company’s data sources, so that they start yielding impactful business insights, is not a straightforward task and the traditional BI and analytics stack is just not at the level to handle the complex data architecture of a modern-day enterprise. This is where advanced analytics comes into play. Advanced analytics, or “advanced data analytics”, is a term that encompasses a wide range of sophisticated methods for analyzing data. These include machine learning algorithms such as deep learning; natural language processing (NLP) for understanding text-based data; predictive modeling; artificial intelligence (AI); computer vision; and many more.

These techniques can help gain valuable insights from large and complex datasets. We’ve seen it become increasingly popular for businesses and industries looking to unlock new opportunities and gain a competitive advantage.

Advanced Analytics powered by Machine Learning

Machine learning (ML) is a branch of computer science that focuses on the use of data, algorithms and structures to imitate how humans learn, gradually improving its accuracy. Machine learning techniques power advanced analytics, as it allows organizations to analyze large and complex data sets and identify patterns and relationships that might not be immediately apparent to humans. 

At Scribble Data, a key part of this process is Feature Engineering, where we use domain knowledge to create variables that make machine learning models work better. Feature engineering is the process of taking raw data, either structured or unstructured, and creating features (attributes) from it that can be used to build machine learning models that accurately solve a specific problem. This involves selecting and interpreting the raw data and transforming it into useful features. Feature engineering can be a time-consuming process, but it is often the differentiating factor when it comes to getting the best performance from ML models.

In case you’re wondering what feature engineering is, here’s a simple example – suppose you are trying to predict the price of a house based on its size, location, and age. You might start by using the raw size, location, and age of the house as features. However, you might realize that the size of the house is more relevant if you compare it to the average size of houses in the same neighborhood. You could therefore create a new feature called “size relative to average” that divides the size of the house by the average size of houses in the same neighborhood. This new feature would likely be more useful for predicting prices than raw size alone.

In the context of advanced analytics, feature engineering can play a crucial role in helping to extract insights and value from data. 

Common ML techniques that play a role in advanced analytics

• Predictive modeling: ML can be used to predict future outcomes or events, such as customer churn, fraud, or demand for a product.
• Clustering: ML can be used to identify groups of similar objects or events, such as customer segments or fraud patterns.
• Classification: ML algorithms can be used to classify objects or events into predefined categories, such as spam or not spam, or fraud or not fraud.
• Regression: ML algorithms can also predict a continuous outcome, such as a stock price or customer lifetime value (CLTV).

Read this article on 4 Advanced Analytics Techniques to Enhance Your Business.

Examples of Advanced Analytics in Action

Advanced analytics can help drive business growth and optimize operations across several industries. Some of the applications of the ML techniques in advanced analytics described above, used across different industries are as follows:

Retail

1. Customer segmentation: Identify different groups of customers based on shared characteristics, such as demographics, purchasing behavior, and preferences. This can help them tailor their marketing and sales efforts to better meet the needs of each segment.
2. Demand forecasting: Predict future demand for their products, which can help them optimize inventory levels and improve supply chain efficiency.
3. Price optimization: Analyze data on customer behavior, market conditions, and competitor pricing to determine the optimal price for their products.
4. Personalization: Personalize the shopping experience for individual customers by recommending products based on past purchases and browsing history.
5. Fraud detection: Identify fraudulent activity, such as fake reviews or fake accounts, and take appropriate action to prevent it.
6. Sentiment analysis: Analyze customer reviews and feedback to understand how customers feel about their products and services, and identify areas for improvement.

Finance

1. Credit risk assessment: Analyze data on potential borrowers to predict the likelihood of default and determine appropriate loan terms.
2. Fraud detection: Identify fraudulent activity, such as fake accounts or unauthorized transactions, and take appropriate action to prevent it.
3. Risk management: Analyze data on financial markets and individual investments to identify and manage risk.
4. Personalization: Personalize the customer experience by recommending financial products and services based on past behavior and preferences.

Ed-tech

1. Student performance prediction: Predict how well a student is likely to perform in a particular course or program based on their past performance and other relevant data.
2. Adaptive learning: Create personalized learning experiences for students by adapting the content and pace of a course based on the student’s progress and needs.
3. Student retention: Identify factors that may be contributing to student dropout rates and develop interventions to improve retention.
4. Plagiarism detection: Identify fraudulent activity such as plagiarism, and take appropriate action to prevent it.
5. Student success prediction: Predict the likelihood of a student’s success in a particular program or course based on data such as past performance, demographics, and learning style.

Benefits of Advanced Analytics

The most obvious benefit is improved efficiency—companies can use advanced analytics to make better decisions faster than ever before. Additionally, advanced analytics can also help reduce costs by identifying areas where processes can be streamlined or automated. Finally, it can give companies a competitive edge by providing them with insights into their customers’ needs and behaviors that other companies don’t have access to.

Strategies for Unlocking the Potential of Advanced Analytics

While we can clearly see the potential benefits, there are still a few hurdles that organizations need to overcome in order to effectively implement advanced analytics for decision-making. Some of these include:

• Data quality and availability: Organizations need access to high-quality data that is accurate, complete, and relevant to the problem being analyzed. However, many organizations struggle to collect, clean, and integrate data from multiple sources, which can limit the effectiveness of advanced analytics.
• Skilled personnel: Advanced analytics requires specialized skills in areas such as statistics, machine learning, and data science. Organizations may have difficulty finding and retaining employees with these skills, or may have difficulty providing training to existing employees to build these capabilities.
• Technological infrastructure: Advanced analytics often requires sophisticated hardware and software infrastructure to support the processing and analysis of large data sets. Organizations may need to invest in new technology and infrastructure in order to support advanced analytics efforts.
• Organizational culture: Significant organizational changes are usually required for the successful adoption, such as the adoption of new processes and decision-making approaches. Organizations may face resistance to change from employees or stakeholders, which can make it difficult to fully leverage the benefits of advanced analytics.
• Data privacy and security: Advanced analytics often involves the analysis of sensitive data, such as personal or financial information. Organizations need to ensure that they have robust data privacy and security measures in place to protect this data and prevent unauthorized access or misuse.

To overcome these challenges and realizing the full potential of advanced analytics within an organization requires investment in training staff on these technologies as well as developing a culture that embraces digital transformation.

How Scribble Data’s Enrich Platform Can up Your Advanced Analytics Game

What organizations really need today is a platform that allows easy and quick adoption of machine learning to aid advanced analytics use cases, especially where metrics like accuracy, performance and efficiency matter a lot. This is where Scribble Data’s Enrich platform can make all the difference. Simply put, Scribble Data’s Enrich platform enables organizations to use machine learning to build speedy solutions or low-code decision apps to solve persistent business problems.

The Value Delivered by Enrich

There are two main reasons for companies to use Enrich as their go to advanced analytics platform

• Scribble Data’s Enrich Intelligence Platform enables reasonable scale or Sub-ML companies to work on business problems that are persistent in nature. By persistent, we mean problems such as scenario planning or anomaly detection that require an analytics product to aid decision-making at regular intervals– for example, hourly, daily, or weekly. These scenarios require trusted data, with the agility to go from data to a decision-making product at a fast pace. Enrich provides automated data preparation and model building features and greatly reduces the turnaround time for such use cases. Organizations can make data backed informed decisions that make their business operations faster and more efficient.
• Organizations have and continue to collect data from varied sources. This data proliferation both in terms of the number of different data types and the massive volume is a bit too much traditional BI and analytics tech stack. Enrich platform can handle most if not all data sources including both relational and non-relational sources

Conclusion

Despite its challenges, investing in advanced analytics is worth the effort. It becomes especially true if your goal is to increase efficiency across all departments within your organization and saving costs through better decision-making. However, to reap these benefits requires commitment from both management and personnel alike who must strive towards creating a data-driven culture that embraces change while understanding the potential that lies at their fingertips when utilizing advanced analytics.