Introduction

Data must be your organization’s strongest asset to thrive and scale in today’s marketplace. However, to benefit from data, you require a modern data analytics ecosystem that is agile, scalable, and future-ready.

Modernization is not just a new buzzword, although, it may seem that there are different definitions to it. Data modernization is also not about one action or implementing a suite of tools. It is all about revamping the way you use data and analytics as an organization. People often think that data modernization means migrating to the cloud, however, the approach you take and the benefits you gain are much beyond cloud adoption. Modern analytical capabilities help make smarter decisions with respect to your business.​

Data Analytics and Modernization

Data modernization mandates contemporary data management principles. It involves moving from legacy databases and architectures to modern, cloud-based platforms and scalable architectures, and moving to next-generation analytics tools.

​As more and more data is available, businesses need better and advanced analytics and you should be able to act quickly to meet those requirements. Data modernization enables you to easily adapt to changes and evolve as an organization. Advanced analytics approaches can help with data modernization initiatives and bring forth enhanced capabilities to data platforms.

Advanced analytics is a process that leverages complex visualization and Machine Learning (ML) methods to gain better data-based insights than those with conventional business intelligence alone. Advanced analytics help businesses comprehend big data and derive actionable insights that ensure operational efficiency and customer satisfaction. Leading organizations make use of advanced analytics to detect hidden trends and patterns and make the right decisions for the future in real-time.

How Do Advanced Analytics Methods Help with Data Modernization?

A business that leverages advanced analytics is said to have its data modernization strategy in place. Advanced analytics methods help drive data modernization efforts, efficiency, insight, and innovation. The following are a few ways in which advanced analytics as a field contributes to data analytics modernization:

• Pattern recognition: Detects hidden relationships and patterns within data, which in turn helps to gain new insights and business opportunities.
• Anomaly identification: Identifies outliers and data inconsistencies and helps to clean and standardize data, improving the overall data quality.
• Risk assessment: Identifies potential challenges and risks using predictive analytics and helps ease losses to enhance resource allocation.
• Performance optimization: Drives cost reduction and process optimization by analyzing performance metrics and identifying areas for improvement.
• Personalization: Enhances customer satisfaction and loyalty by designing personalized customer experiences based on data-driven insights.

Technologies Powering Advanced Analytics

Any data platform architecture that utilizes advanced analytics is driven by Artificial Intelligence (AI) and Machine Learning (ML). Advanced analytics use AI to simulate human intelligence. AI makes it possible to manage complex modeling, integrate abstract algorithms, and process huge data volumes. As AI is capable of deep learning, it can use multilayered neural networks to analyze different data types such as text, sound, and images. Advanced data analysis also uses ML for future forecasting. ML models like decision trees, regression, and time series analysis are used to comprehend data and then make decisions. ML enables forecasting by swiftly processing millions of data points.

Traditional Business Intelligence Vs Advanced Analytics: Understanding the Difference

While both advanced analytics and business intelligence (BI) involve data analysis, they have very different purposes and deploy different methodologies. Traditional business intelligence leverages descriptive analytics to offer insights into current and past performance. It primarily depends on structured data and standard statistical techniques. It makes use of tools such as reports, dashboards, and OLAP cubes. This type of business intelligence makes use of historical data and helps in monitoring business operations, identifying trends, and supports decision-making.

Advanced analytics concentrates on prescriptive and predictive analytics to identify future trends and suggest optimal actions. The approach makes use of complex data, including unstructured data and advanced statistical techniques, ML, and AI. It leverages tools, such as statistical modeling, data mining, and ML algorithms. Advanced analytics approaches help to identify hidden patterns, predict future events, improve processes, and move toward innovation. This approach helps to identify multiple customer insights and customer groups based on preferences, behavior, and demographics. They also help to predict the potential revenue a customer will generate over their lifetime.

Identifying market trends and opportunities is another area where advanced analytics methods shine. They help understand product relationships and pinpoint cross-selling opportunities. They point out how price changes affect demand in market segments with specific needs and preferences. Further, they predict future market trends and shifts.

In short, traditional business intelligence helps to understand what has happened and what is happening, while advanced analytics predicts what will happen and how to help make that happen.

Key Benefits of Advanced Analytics for Businesses

Advanced analytics methods offer numerous benefits for businesses that can significantly improve competitiveness and performance. Data modernization is best experienced by leveraging advanced analytics. The following are the key benefits of advanced analytics:

• Enhanced productivity and efficiency: Helps organize operations and remove bottlenecks. This approach also improves inventory management and forecasting. Moreover, they detect anomalies and help prevent financial losses.
• Better decision-making: Provides actionable insights to support informed decision-making and helps forecast future trends and market changes. Also, helps in identifying areas for improvement and cost reduction.
• Better profitability and revenue: Identifies high-value customer segments for special focus. These methods also help with service and product optimization and setting optimal pricing.
• Competitive advantage: Drives innovation by identifying new opportunities and business models. These methods help gain a competitive edge with data-driven insights and further helps in preventing risks.

Requirements to Deploy Advanced Data Analytics

Running advanced data analytics mandates the infrastructure for data collation, storage, processing, and governance. The following are the essential requirements:

• Data collection: Well-organized, competent systems to gather data from different sources (APIs, databases, IoT devices, and so on).
• Data storage: Robust storage solutions (data warehouses, data lakes) to accommodate large datasets.
• Data governance: Clear policies and procedures for data quality, security, and compliance.
• Data processing: Powerful computing resources (on-premises or cloud) to handle complex data transformations and calculations.

Ready to Deploy Adopt a Modern Data Platform Architecture with Advanced Analytics?

Deploy a modern data platform architecture that offers your business the much-needed competitive edge while driving operational excellence. Aretove has a team of data experts who can help you modernize your data infrastructure with advanced analytics capabilities while ensuring optimal data governance. Our organization can empower businesses with advanced analytics by delivering customized data solutions that transform raw data into actionable insights. Let us help your business harness the power of its data to make informed decisions and drive strategic growth.