{"id":73627,"date":"2024-05-02T14:51:00","date_gmt":"2024-05-02T12:51:00","guid":{"rendered":"https:\/\/intellias.com\/?post_type=blog&p=73627"},"modified":"2024-08-12T03:26:47","modified_gmt":"2024-08-12T01:26:47","slug":"introduction-to-predictive-analytics-in-the-cloud","status":"publish","type":"blog","link":"https:\/\/intellias.com\/predictive-analytics-cloud\/","title":{"rendered":"Introduction to Predictive Analytics in the Cloud"},"content":{"rendered":"

Today\u2019s customers and users expect instant gratification. To meet their needs \u2014 and do a whole range of other things we\u2019ll discuss in this article \u2014 you need to implement the same technology that sets those high expectations: predictive analytics.<\/p>\n

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\u2019s 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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

Intellias is a global technology partner that helps clients with digital transformation. We\u2019re seeing wide adoption of predictive analytics in cloud infrastructure. We\u2019ve recently helped customers build cloud-based predictive analytic solutions for use cases ranging from real-time fraud detection<\/a> to predictive fleet management analytics<\/a>. We\u2019ve also built business intelligence platforms<\/a> that rely on cloud-based predictive analytics, and helped manufacturers use cloud predictive analytics solutions to cut maintenance costs and downtime<\/a>.<\/p>\n

We know you have a lot to consider when \u200cyou 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\u2019ll introduce the tools available for your transition to cloud-based predictive analytics. We’ll also explore specific cases demonstrating how to optimize cloud<\/a> investments and forecasting, using client case studies from various global industries<\/a> at Intellias.<\/p>\n

The evolution of predictive analytics<\/h2>\n

\"What<\/p>\n

Let\u2019s define terms. Predictive analytics is a branch of advanced analytics that uses historical data, statistical, and machine learning algorithms to predict future outcomes.<\/p>\n

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.<\/p>\n

The history of business analytics predates the computer<\/a>\u2014Lloyd\u2019s of London pioneered predictive analytics for insurance in 1689. The term \u201cbusiness intelligence\u201d dates back to 1865. But our story begins in the computer era.<\/p>\n

As early as the 1950\u2019s and 1960\u2019s, 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.<\/p>\n

In the 1980\u2019s, 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 \u201cbusiness intelligence,\u201d or BI, to refer to systematic analysis of business data for making data-driven decisions.<\/p>\n

In the 1990s, data mining began in earnest to make predictions based on historical customer data. By 2005, Roger Magoulas coined the term \u201cbig data\u201d 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.<\/p>\n

Up to this point, nearly everyone stored and processed their data on local servers or computers. We still call local computing \u201con-premises,\u201d or on-prem. Building a data center takes a big up-front investment, so smaller companies struggle to compete.<\/p>\n

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\u2019s hard to respond to growth in demand for computing power.<\/p>\n

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\u2019s a waste of resources.<\/p>\n

Given all these limitations, predictive analytics wasn\u2019t always cost-effective with on-prem infrastructure.<\/p>\n

Everything changed again in 2006. That\u2019s 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<\/a> to companies who needed data infrastructure.<\/p>\n

With AWS, any business could use Amazon\u2019s 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.<\/p>\n

Suddenly, every company could use predictive analytics in the cloud<\/a>. This didn\u2019t 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\u2019s because cloud computing unlocks several unique advantages for predictive analytics. Critically, cloud infrastructure enables real-time data analytics.<\/p>\n

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<\/a> are a necessity for AI-driven competitive advantage.<\/p>\n

As Paramita Ghosh at Dataversity<\/a> puts it, \u201cUltimately, 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.\u201d<\/p>\n

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

Cloud computing benefits for predictive analytics<\/h2>\n

The exploding cloud computing landscape makes compute-intensive technology more affordable and available. Companies that couldn\u2019t scale on-prem now have access to massive computing power. Any company can use predictive analytics in the cloud<\/a>.<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n
<\/td>\nOn-prem challenge<\/th>\nCloud advantage<\/th>\n<\/tr>\n<\/thead>\n
Scalability<\/strong><\/td>\nPhysical limitations on scaling create performance bottlenecks<\/td>\nEasily scale up or down based on actual storage and consumption<\/td>\n<\/tr>\n
Costs<\/strong><\/td>\nHigh upfront infrastructure costs<\/td>\nPay-as-you-go model reduces upfront costs, making cloud-based predictive analytics more accessible<\/td>\n<\/tr>\n<\/tbody>\n
Accessibility<\/strong><\/td>\nLocal storage limits data accessibility and opportunities for remote collaboration<\/td>\nSince data is stored centrally, it\u2019s equally accessible from anywhere<\/td>\n<\/tr>\n
Collaboration and Integration<\/strong><\/td>\nIt\u2019s hard to connect data sources stored at different sites or with multiple technologies<\/td>\nRemote integration of various data sources means all your data is accessible to your team<\/td>\n<\/tr>\n
Data Security<\/strong><\/td>\nWhen you run an on-prem facility, you\u2019re responsible for your own security measures<\/td>\nCloud providers invest heavily in security, ensuring protection for sensitive data<\/td>\n<\/tr>\n
Automatic Updates and Maintenance<\/strong><\/td>\nMaintenance and manual updates are time-consuming and can lead to downtime<\/td>\nCloud service providers handle updates and maintenance tasks automatically<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n
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Learn more in our Intellias Cloud Computing vs On-Premises Comparison Guide<\/p>\n

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Different types of cloud services and their impact on predictive analytics<\/h2>\n

There\u2019s a lot to know when shopping for predictive analytics using cloud computing. It\u2019s important to understand a few different categories of cloud services.<\/p>\n

Infrastructure as a Service (IaaS)<\/h3>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

Platform as a Service (PaaS)<\/h3>\n

\"PaaS<\/p>\n

PaaS provides a complete, ready-to-use platform for developing, deploying, and managing predictive analytics applications.<\/p>\n

Similar to IaaS, PaaS offers the ability to scale up and down \u2014 along with several other benefits. PaaS can make it faster to develop with pre-built tools, libraries, and frameworks specifically designed for data analysis<\/a> 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<\/a> required for predictive analytics.<\/p>\n

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.<\/p>\n

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

Software as a Service (SaaS)<\/h3>\n

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

SaaS predictive analytics solutions offer user-friendly interfaces and pre-built models. These help non-technical users leverage predictive analytics even if they don\u2019t 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.<\/p>\n

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 \u201cone size fits all\u201d approach. These tools may not fully align with your organization\u2019s specific requirements, and customization options can be limited.<\/p>\n

\n\n\n\n\n\n\n\n
Cloud Service Type<\/strong><\/td>\nDescription<\/th>\nImpact on Predictive Analytics<\/th>\n<\/tr>\n<\/thead>\n
Infrastructure as a Service (IaaS)<\/strong><\/td>\nVirtualized computing resources over the internet.<\/td>\nGives businesses more control over their data infrastructure. Ueful for customizing predictive analytics environments.<\/td>\n<\/tr>\n
Platform as a Service (PaaS) <\/strong><\/td>\nWeb-based platforms with tools and services for application development.<\/td>\nStreamlines development and deployment of predictive analytics applications. This reduces the need for manual coding and configuration.<\/td>\n<\/tr>\n<\/tbody>\n
Software as a Service (SaaS)<\/strong><\/td>\nSoftware applications delivered and updated over the internet.<\/td>\nMakes cloud analytics software readily available. SaaS can help address technical skills gaps. User-friendly tools can be suitable for non-experts and those without much IT support.<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n

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.<\/p>\n

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.<\/p>\n

Industry applications of predictive analytics in the cloud with Intellias<\/h2>\n

\"Industry<\/p>\n

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.<\/p>\n

Big Data for Retailers: A Platform for Equipment Monitoring in Supply Chains<\/h3>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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Big Data for Retailers: A Platform for Equipment Monitoring in Supply Chains.<\/p>\n

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Predictive Analytics for Automotive Component Manufacturing & Retail<\/h3>\n

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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

post_cta title=”Scalable Big Data Analytics Platform Unlocking Sales, Marketing Insights, and New Business Opportunities for Growth and Success.” link=”https:\/\/intellias.com\/data-analytics-platform-in-automotive-retail\/” link_title=”Read more” background=””][\/post_cta]<\/p>\n

IoT for Manufacturing Hubs: Industrial IoT Predictive Maintenance Solution<\/h3>\n

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<\/a> and unplanned downtime.<\/p>\n

The solution Intellias helped them build uses IoT and predictive cloud analytics.<\/p>\n