{"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 <\/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 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>\nThe evolution of predictive analytics<\/h2>\n
Cloud computing benefits for predictive analytics<\/h2>\n