Introduction to Predictive Analytics in the Cloud

Today’s customers and users expect instant gratification. To meet their needs — and do a whole range of other things we’ll discuss in this article — you need to implement the same technology that sets those high expectations: predictive analytics.

Predictive analytics is a resource-intensive process that requires substantial computational power and robust data infrastructure to effectively derive actionable insights and forecasts from large datasets. With data volumes reaching humongous proportions, it’s essential to process it in the most efficient way. Though it can be run on-premises, the cloud is ideally suited to it. Cloud-based predictive analytics solutions and tools handle this by leveraging scalable computing resources, allowing for the rapid processing and analysis of data. And while the transition to cloud computing can be daunting, the results of employing cloud capabilities are more than worth the effort.

Predictive analytics in the cloud utilizes data stored across cloud infrastructure to make evidence-based projections and, consequently, decisions. Once the data is processed, comprehensive analytics techniques are applied to uncover patterns, trends, and correlations. This involves employing advanced algorithms and machine learning models to extract valuable insights.

Based on these findings, organizations can take actions aimed at achieving optimal tangible outcomes. These actions customarily involve operations optimization, such as resource allocation, supply chain management, and pricing strategies, etc. Additionally, they encompass efficient information management, which is derived from structured, high-quality, and accessible data. Furthermore, organizations can leverage automation by analyzing incoming data, identifying patterns, and triggering predefined actions or real-time alerts without manual intervention. Service modernization is also facilitated through reliable customer and market analytics, leading to improved offerings and experiences.

Intellias is a global technology partner that helps clients with digital transformation. We’re seeing wide adoption of predictive analytics in cloud infrastructure. We’ve recently helped customers build cloud-based predictive analytic solutions for use cases ranging from real-time fraud detection to predictive fleet management analytics. We’ve also built business intelligence platforms that rely on cloud-based predictive analytics, and helped manufacturers use cloud predictive analytics solutions to cut maintenance costs and downtime.

We know you have a lot to consider when ‌you pursue predictive cloud analytics. Our years of expertise may help. Read on to learn about the history of predictive analytics, and the benefits of moving to the cloud. We’ll introduce the tools available for your transition to cloud-based predictive analytics. We’ll also explore specific cases demonstrating how to optimize cloud investments and forecasting, using client case studies from various global industries at Intellias.

The evolution of predictive analytics

Let’s define terms. Predictive analytics is a branch of advanced analytics that uses historical data, statistical, and machine learning algorithms to predict future outcomes.

Predictive algorithms analyze patterns and trends in data. Then, they make educated guesses about future risks and opportunities. Insights from predictive analytics help business leaders make confident recommendations and informed business decisions. For example, anticipating increased demand for a product can help a manufacturer plan to ramp up manufacturing or adjust prices.

The history of business analytics predates the computer—Lloyd’s of London pioneered predictive analytics for insurance in 1689. The term “business intelligence” dates back to 1865. But our story begins in the computer era.

As early as the 1950’s and 1960’s, computer researchers explored predictive modeling to translate data into useful insights. By the 1970s, businesses began to use Decision Support Systems (DSS) to make production and sales decisions.

In the 1980’s, the falling cost of computer disks made data warehouses more attainable. At the same time, the amount of data started to increase dramatically. A growing number of companies provided data-driven insights. Howard Dresner at Gartner brought back the term “business intelligence,” or BI, to refer to systematic analysis of business data for making data-driven decisions.

In the 1990s, data mining began in earnest to make predictions based on historical customer data. By 2005, Roger Magoulas coined the term “big data” to describe processing incomprehensibly large datasets for insights. Apache Hadoop emerged that year and enabled streaming processing of structured and unstructured data from nearly any digital source.

Up to this point, nearly everyone stored and processed their data on local servers or computers. We still call local computing “on-premises,” or on-prem. Building a data center takes a big up-front investment, so smaller companies struggle to compete.

Companies that could afford big data centers had the advantage. Even so, scaling was difficult. Scaling a data center is slow and expensive. It means ordering, installing, and configuring hardware. It’s hard to respond to growth in demand for computing power.

Overbuilding is also a problem. Locally managed IT setup is expensive and involves complex maintenance. If you overbuild, your on-prem infrastructure may go underutilized. That’s a waste of resources.

Given all these limitations, predictive analytics wasn’t always cost-effective with on-prem infrastructure.

Everything changed again in 2006. That’s when Amazon launched Amazon Web Services (AWS). AWS gave customers online access to scalable computing resources. Amazon had built exceptional data centers to manage their retail operation, and realized they had an opportunity to rent virtual servers to companies who needed data infrastructure.

With AWS, any business could use Amazon’s computers to store their data and run their jobs. This marked a major shift in computing infrastructure. Within a few years, Google and Microsoft introduced their own cloud computing platforms. Many others followed suit.

Suddenly, every company could use predictive analytics in the cloud. This didn’t just level the playing field for smaller companies; it also changed the game for companies that had already been using predictive analytics on-prem. That’s because cloud computing unlocks several unique advantages for predictive analytics. Critically, cloud infrastructure enables real-time data analytics.

By 2015, machine learning (ML) was beginning to unlock Big Data and Analytics (BDA). Innovations in artificial intelligence (AI) have accelerated exponentially. Today, a combination of predictive analytics and cloud solutions are a necessity for AI-driven competitive advantage.

As Paramita Ghosh at Dataversity puts it, “Ultimately, the combination of predictive analytics and cloud computing offers enormous potential for businesses looking to stay ahead of the curve in terms of fraud detection, supply chain optimization, and risk management.”

For example, an Intellias customer combined AI and cloud computing with Internet of Things (IoT) to power predictive fleet maintenance software. This solution simplifies processes for fleet workers and saves the business money, giving them an edge over the competition.

Cloud computing benefits for predictive analytics

The exploding cloud computing landscape makes compute-intensive technology more affordable and available. Companies that couldn’t scale on-prem now have access to massive computing power. Any company can use predictive analytics in the cloud.

Different types of cloud services and their impact on predictive analytics

There’s a lot to know when shopping for predictive analytics using cloud computing. It’s important to understand a few different categories of cloud services.

Infrastructure as a Service (IaaS)

IaaS is a cloud computing model where a provider offers virtualized computing resources, such as servers, storage, and networking, over the internet on a pay-as-you-go basis.

IaaS provides a robust and flexible foundation for predictive analytics. This model allows organizations to easily scale up or down their computing resources based on the demands of their predictive analytics workloads. IaaS can handle large volumes of data and complex models efficiently without the inflexibility and ongoing maintenance issues of on-prem data infrastructure.

Of the three types of cloud services, IaaS offers the highest level of flexibility and control. Organizations that want to customize their predictive analytics environments will be happiest opting for IaaS.

IaaS also requires the most technical expertise for set-up, configuration, security, and integration. Many organizations that need virtual data infrastructure turn to partners like Intellias for the necessary expertise. We frequently help customers handle high-performance computing (HPC) options to accelerate the training and execution of complex predictive models.

Platform as a Service (PaaS)

PaaS provides a complete, ready-to-use platform for developing, deploying, and managing predictive analytics applications.

Similar to IaaS, PaaS offers the ability to scale up and down — along with several other benefits. PaaS can make it faster to develop with pre-built tools, libraries, and frameworks specifically designed for data analysis and predictive modeling. There is often a correlation between Platform as a Service (PaaS) solutions and integrated features designed to facilitate connections between your platform and diverse data sources, machine learning tools, and other essential cloud services required for predictive analytics.

Since it abstracts away the underlying infrastructure monitoring and management, PaaS platforms also allow data scientists and analysts to focus on building and refining predictive models rather than worrying about infrastructure setup and maintenance.

Though PaaS offers a balance between control and ease of use, you may run into challenges with vendor lock-in or performance limitations.

Software as a Service (SaaS)

SaaS provides ready-to-use, cloud-based software that makes it easier for companies to implement and use predictive analytics capabilities right away.

SaaS predictive analytics solutions offer user-friendly interfaces and pre-built models. These help non-technical users leverage predictive analytics even if they don’t have extensive data science expertise. SaaS solutions often integrate seamlessly with cloud-based IaaS and PaaS services, such as data storage, data processing, and data visualization tools. SaaS platforms help organizations focus on their core competencies and business objectives rather than worrying about underlying technology.

While SaaS can reduce up-front costs and enable rapid deployment, it can have drawbacks. Since SaaS solutions use pre-built, standardized predictive analytics tools and models, they tend to have a “one size fits all” approach. These tools may not fully align with your organization’s specific requirements, and customization options can be limited.

These cloud services shape how businesses interact with predictive analytics tools. IaaS provides more control over computing environments. PaaS simplifies application development. SaaS makes cloud analytics accessible to a broader audience.

There are great tools in every category. The effectiveness of cloud operations decisions and the selection of appropriate tools depend on the specific requirements and user profiles within your organization.

Industry applications of predictive analytics in the cloud with Intellias

At Intellias, we have seen first-hand that integrating predictive analytics and cloud-based services can be a game-changer in any industry. Here are three examples of how our customers use real-time insights for predictive analytics.

Big Data for Retailers: A Platform for Equipment Monitoring in Supply Chains

A Baltic wireless sensor vendor asked Intellias for help building cloud-based real-time big data analytics platform. Their customer, a European supermarket chain, needed better refrigerator and freezer equipment monitoring. Equipment failures were causing food spoilage and costly repairs. The retailer had adopted wireless monitoring sensors but needed to extract and transfer the data in real time.

Intellias developed a robust cloud-based IoT platform. It processes data from hundreds of sensors across 125 stores. The solution enables real-time monitoring, alerting store managers promptly if temperatures fluctuate.

The platform has saved the end customer millions of dollars in product loss. The solution also found inefficiencies in the supply chain. That discovery helped the end customer save around 20% on energy consumption.

Predictive Analytics for Automotive Component Manufacturing & Retail

A global leader in automotive component manufacturing and retail came to Intellias when they needed a customer data processing solution for their vast retailer network. The platform had to accommodate more than 200 data processing pipelines, processing about 1 TB of data daily from operations. That data comes from operations across Asia-Pacific, Europe, the Middle East, Africa, and the Americas.

Intellias helped this company build a scalable, full-fledged predictive analytics platform on AWS. The solution uses advanced analytics to highlight new business opportunities in specific regions.

Our client can now make robust predictions about inventory and sales. They can avoid overstocks and stockouts while growing sales and profitability and mitigating unexpected expenses.

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IoT for Manufacturing Hubs: Industrial IoT Predictive Maintenance Solution

A global technology and research center turned to Intellias when they needed an intelligent monitoring platform. The goal: to predict and prevent industrial equipment failures and unplanned downtime.

The solution Intellias helped them build uses IoT and predictive cloud analytics.

• Interactive dashboards give real-time visibility into plant assets.
• Edge computing captures and processes sensor data.
• Physics-based forecasting algorithms use that data to predict maintenance issues.

The client’s solution was initially on-premises, but customer demand called for a move to the cloud. Intellias helped move the platform to the AWS Cloud. Fortunately, Intellias is an expert at cloud migration. As an AWS consulting partner it was no problem for us to help move the customer’s platform to the AWS Cloud.

The platform optimizes maintenance inventory, manages the supply chain, and integrates industrial IoT.

Popular cloud platforms for predictive analytics

Global cloud analytics market size, 2024 to 2032 in USD billion

Source: Precedence Research

Today, many industry heavyweights are offering public cloud services. Choosing the right cloud platform can be a challenge. Fortunately, at Intellias, we have years of experience in the cloud and are constantly exploring the latest cloud technologies. And we’re partnered with the hyperscalers, niche players, and emerging tech providers.

Amazon Web Services (AWS)

Amazon Web Services (AWS) pioneered the modern cloud industry. AWS remains a leading cloud provider, and offers a comprehensive set of tools, including:

• Amazon SageMaker for machine learning
• Amazon Redshift for data warehousing
• Amazon QuickSight for visualization

When considering AWS for predictive cloud analytics, assess your specific needs. Are data volume, cloud predictive modeling capabilities, and scalability high priorities for you? AWS is scalable and handles complex analytics workloads efficiently.

Intellias is an AWS Advanced Consulting partner, with expertise in

Microsoft Azure

If you need to build an intelligent IoT ecosystem, you can’t go wrong with Azure’s connected technologies. Azure provides a comprehensive platform for scalable and collaborative predictive analytics and cloud solutions. Services include Azure Machine Learning, Databricks, and Synapse Analytics.

Google Cloud Platform

Google Cloud Platform (GCP) is known for its analytics and machine learning capabilities. GCP provides cloud-native solutions for predictive analytics. This helps businesses to harness the power of data-driven insights.

Consider GCP if you want machine learning capabilities integrated into your analytics platform. Google’s strength in artificial intelligence and data processing can be a significant advantage.

• BigQuery provide for fast and cost-effective analytics
• AI Platform for machine learning model deployment

Intellias is a Google Cloud partner. We provide technical expertise and consulting services across GCP tools and technologies.

Snowflake

Snowflake is a cloud-native data warehousing platform. Snowflake excels in managing and analyzing large volumes of data. It provides a scalable and flexible architecture that separates storage and compute. This model is very efficient for data processing.

Snowflake is a great option to develop and deploy a predictive cloud-native analytics solution.

Consider Snowflake if you want a robust and flexible data warehousing solution. It’s built for large datasets, diverse data types, and seamless data sharing.

Choosing the right platform for cloud-based predictive analytics

There is no universal “best” predictive cloud-native analytics platform. The best solution is the one that best meets your unique scenario. Here’s how to weigh your options:

• Assess Business Needs: Define requirements for data warehousing capabilities and data sharing
• Data Warehousing: Check features like data storage, processing efficiency, and supported data types
• Scalability: Look for flexibility to accommodate changing data storage and processing needs
• Integration: Choose a platform that integrates well with your existing tools and applications
• Cost Considerations: Consider factors including storage costs, processing costs, and any extra fees
• Community and Support: Make sure they provide the resources your team will need

This will help you find the best predictive cloud-native analytics platform for you.

And if you’re struggling to find the best cloud-native predictive analytics provider, you don’t have to pick one! More than 76% of enterprises using cloud solutions opt for a multi-cloud strategy.

Tools and technologies

Many providers offer cloud-based predictive analytic solutions and tools with diverse capabilities. These tools unlock predictive analytics with the scalability and flexibility of the cloud. Some are tied to a cloud vendor. Others integrate with a variety of cloud platforms.

There are a lot of tools and technologies for cloud-based predictive analytics. Here’s a quick overview:

BigML

BigML is a cloud-based machine learning platform. It offers a range of predictive analytics tools. BigML’s intuitive interfaces and automation solutions simplify machine learning. BigML helps build, evaluate, and deploy models with ease.

Amazon SageMaker

Amazon SageMaker is a fully managed service provided by AWS. SageMaker is for building, training, and deploying machine learning models at scale. It includes built-in algorithms, Jupyter notebook integration, and automatic model tuning. SageMaker is a powerful option for cloud-based predictive analytics projects.

Microsoft Azure Machine Learning

Azure Machine Learning is for creation, training, and deployment of machine learning models. Its user-friendly interface simplifies predictive analytics in the cloud. This tool supports various programming languages and integrates with other Azure services.

IBM Watson Studio

IBM Watson Studio is a comprehensive platform for creating ML and AI models. It integrates tools for data preparation, model development, and deployment. IBM Watson Studio can help you build predictive analytics projects.

Google Cloud AutoML

Google Cloud AutoML is Google’s suite of machine learning products. Users can build custom machine learning models without being experts in data science or programming. AutoML includes tools for image recognition and natural language processing. AutoML simplifies cloud-based predictive analytics.

Databricks

Databricks is a unified analytics platform built on Apache Spark. It’s designed to simplify big data and machine learning workflows. Databricks offers collaborative features and scalable data processing capabilities. It’s also integrated with popular machine learning libraries. It’s a great tool for cloud-based predictive analytics projects.

Challenges and solutions in cloud-based predictive analytics

There are many challenges to implementing cloud-based predictive analytics. Here are some of the most common challenges, and how we’ve addressed them with customers.

Data Privacy:

• Problem: Keeping sensitive customer information private.
• Solution: Implemented robust encryption protocols and compliance measures. Utilized cloud-based solutions with built-in privacy features to meet regulatory requirements.

Security Concerns:

• Problem: Cyber threats posing risks to data integrity and system security.
• Solution: Employed advanced cybersecurity best practices. Measures included multi-layered authentication, secure network protocols, and continuous monitoring. Collaborated with cloud service providers to leverage their security features.

Integration Issues:

Problem: Integrating diverse data sources and technologies can lead to interoperability issues.

Solution: Implemented seamless integration strategies, leveraging APIs and middleware solutions. Adopted cloud-native architectures to ease interoperability and ensure data flow across components.

Dynamic Data Needs:

Problem: Meeting the changing needs of the business.

Solution: Utilized cloud platforms with auto-scaling capabilities to handle varying workloads. Applied containerization and microservices architecture for enhanced scalability and resource efficiency.

Data Quality and Consistency:

Problem: Inconsistent or poor-quality data can make predictive models less accurate.

Solution: Implemented data quality checks and cleansing processes. Used data validation techniques. Established clear data governance.

Cost Management:

Problem: Concern that unexpected costs could affect the project budget.

Solution: Conducted thorough cost analyses before implementation. Optimized resource use and cut costs with serverless architectures where applicable. Leveraged cloud providers’ cost management tools.

Skill Gaps and Training:

Problem: Meeting staffing needs for managing and optimizing cloud-based predictive analytics platforms.

Solution: Provided training to build teams’ cloud computing skills. Collaborated with cloud service providers for more training and support.

Intellias is here for your cloud-based predictive analytics needs

We’ve navigated challenges and unraveled complexities. We’ve experienced how the cloud transforms predictive analytics. We’ve addressed concerns including data privacy, compliance, and cost management. Are you ready to take your business to the next level? You’re ready for cloud-based predictive analytics. Contact the Intellias team to get started today.