With all the buzz in the information technology industry around artificial intelligence (AI) and machine learning (ML) you'd think that every organization was using these tools or planning for how they are going to use them. After all, the promise is that AI and ML will help organizations harness the ever-growing volumes of data being generated by automating and augmenting human analytic processes and decision-making.

Most retail brands know their data is a valuable resource for decision making in business operations. As tech continues to evolve, major brands are leveraging predictive analytics to fully exploit the benefits of their data. However, with new tech comes the need for education. Common misunderstandings of these tools include the difference between predictive analytics and machine learning and the human element that's needed to optimize your results. Is predictive analytics the same as machine learning?

Oracle has been named a leader in notebook-based Predictive Analytics and Machine Learning (PAML) solutions by Forrester Research, earning the highest average current offering score as well as the highest possible score for its solution roadmap. The Forrester Wave: Notebook-Based Predictive Analytics and Machine Learning Solutions, Q3 2018 report recognizes that Oracle Autonomous Data Science Cloud Service "provides the standardization and controls that enterprises need" and "makes it easy to put models into production by offering visual tools to create APIs with automatic load balancing." According to Forrester, PAML solutions are defined as "Software that provides enterprise data scientist teams and stakeholders with 1) tools to analyze data; 2) workbench tools to build predictive models using statistical and machine learning algorithms; 3) a platform to train, deploy, and manage analytical results and models; and 4) collaboration tools for extended enterprise teams including businesspeople, data engineers, application developers, DevOps, and AI engineers." Forrester evaluated the strengths and weaknesses of the top notebook-based PAML vendors across 24 evaluation criteria, which were grouped into three categories: current offering, strategy and market presence. Of the nine vendors Forrester evaluated, Oracle was one of the two companies recognized as a leader. "With Oracle Autonomous Data Science Cloud Service, Oracle has a winning solution for our customers to build and deploy artificial intelligence and machine learning models on the Oracle Cloud," said Greg Pavlik, Senior Vice President and Chief Technology Officer, Oracle Cloud Platform.

According to SAS, SAS Visual Data Mining and Machine Learning offers users a single platform to solve complex analytical problems. Combining data preparation, visualization, advanced analytics and model deployment, it unifies the entire machine learning process, from data access/transformation and preparation to scoring, in one environment. Running on the SAS Viya engine, SAS Visual Data Mining and Machine Learning includes the latest statistical, machine learning, deep learning and text analysis algorithms that accelerate structured and unstructured data explorations, while also supporting popular open source languages.

Organizations no longer need to run through hoops to get relevant data about their market, business, clients, and so on. With the digitization of the business world, obtaining and gather information/data is no longer a challenge. While data collection is easy today, extracting meaningful insights from the available data remains a stiff task. This where machine learning and predictive analytics can help. By encouraging an integrated data-driven approach, machine learning and predictive analytics enable organizations to make more accurate decisions.

SAP SE (NYSE: SAP) today announced it was ranked as a leader in "The Forrester Wave: Predictive Analytics And Machine Learning Solutions, Q1 2017." According to Forrester, "SAP offers comprehensive data science tools to build models, but it is also the biggest enterprise application company on the planet. This puts SAP in a unique position to create tools that allow business users with no data science knowledge to use data-scientist-created models in applications. SAP's solution offers the data tools that enterprise data scientists expect, but it also offers distinguished automation tools to train models." A finding in the report is that the predictive analytics and machine learning (PAML) market is forecasted to experience a 15 percent compound annual growth rate (CAGR) through 2021.*

Machine Learning involves algorithms that learn from and make predictions on data and, generally speaking, more data means better predictions. Combine that with the vast amounts of data that most organizations are now generating, and the transformational potential of Machine Learning is nothing short of amazing. It's no surprise that Predictive Analytics and Machine Learning are two of the hottest areas in analytics today, as organizations see their potential to help with Digital Transformation. Enterprises are investing heavily with the hope of reaping big business benefits from smarter business processes & better decisions that improve their Return on Investment. Let's be honest though, many of us don't understand how the complex algorithms that make Machine Learning work translate into measureable business results, and there is so much hype that it's difficult to separate great marketing from great products.

To stay competitive in a digital economy, businesses increasingly need to move beyond simple reporting and descriptive analytics to a more predictive approach that puts artificial intelligence (AI) strategies to work to engage with customers in new ways. So how can you find a practical way to start applying AI in your business? One path forward follows three steps: leverage predictive models to improve how you engage with customers, put machine learning to work to improve those models, and then validate your models. In this post, I will focus on the validation of predictive models First let me provide a quick overview of predictive analytics and machine learning, and explain why validation is important when you apply these approaches. Predictive analytics is about using algorithms to predict the result of a measurement that you can't make, based on measurements that you can make.

Recognizing cross-industry interest in massive data ingest and analytics, we teamed up with O'Reilly Media on a new book: The Path to Predictive Analytics and Machine Learning. In this book, we share the latest step in the real-time analytics journey: predictive analytics, and a playbook for building applications that take advantage of machine learning. Chapter 1: Building Real-Time Data Pipelines We begin with a review our previous O'Reilly book: Building Real-Time Data Pipelines – Unifying Applications and Analytics with In-Memory Architectures. It covers the emergence of in-memory architectures and provides a framework for building real-time pipelines that serve as the foundation for machine learning applications. Chapter 2: Processing Transactions and Analytics in a Single Database This chapter details the shift from Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) to converged, multi-model systems designed for Hybrid Transaction/Analytical Processing (HTAP). Chapter 3: Dawn of the Real-Time Dashboard Data visualization is arguably the most powerful method for enabling humans to understand and spot patterns in a dataset.