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We are experiencing a Cambrian explosion of Data Science: once a niche discipline – now at the core of the most successful enterprises such as Amazon, Tesla, Alibaba & Co. Every major company has launched one or even several AI Labs, Data Offices or Analytics Excellence Centers. Also midsized companies start analytics projects and data initiatives. In every industry we observe an ever-growing number of new innovative use cases of analytics and data. While a few years ago we only talked about the “data scientist”, today we are looking for data strategists, analytics translators, data engineers, machine learning engineers, data stewards, dataops engineers, data product designers, and countless more specialized roles.
Our passion has become a profession and so expectations and standards on us are growing. However, the major challenges are not technical or analytical: depending on the study 60% to 80% of all analytics projects fail! Due to a lack of user acceptance or because they do not bring any economic benefit. It’s our job to improve this performance by “eating our own dog food”: doing data-driven data science, i.e. testing and proving not only model but business performance. The Predictive Analytics World for Business wants to be the platform and starting point for this: in numerous case study sessions you will find out which applications of predictive analytics really create business value; in the deep dive sessions you will learn new methods, tools and approaches for your daily work; and the keynote sessions gives you a new perspective on upcoming trends and important topics. And finally: you will meet many professionals with similar challenges and exchange solutions.
Predictive Analytics at the Core of the Finance & Insurance Business
At this year’s conference we start a new format: a specialized track on applied predictive analytics in the finance and insurance industry. This one-day track will introduce you to the most critical use cases of machine & deep learning for banks, insurers, investors and funds by presenting you real-world projects from major players. You will learn about how to solve data privacy & regulation issues, manage and utilize your data assets with data governance & data strategy and build and operate great data products with data science & analytics.
Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer’s predictive score informs actions to be taken with that customer — business intelligence just doesn’t get more actionable than that.
Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization.
Predictive Analytics World London often include select sessions on forecasting since it is a closely related area, and, in some cases, predictive analytics is used as a component to build a forecast model.
However, Predictive analytics is something else entirely, going beyond standard forecasting by producing a predictive score for each customer or other organizational element. In contrast, forecasting provides overall aggregate estimates, such as the total number of purchases next quarter. For example, forecasting might estimate the total number of ice cream cones to be purchased in a certain region, while predictive analytics tells you which individual customers are likely to buy an ice cream cone.
Yes. Predictive analytics means the commercial deployment of machine learning (the two terms are often used synonymously). Although the term “machine learning” used to be common only within the walls of research labs, it’s now also used more and more in the context of commercial deployment. Whichever term you prefer, we are discussing technology that learns from data to predict or infer an unknown, including decision trees, logistic regression, neural networks, and many other methods.
Yes. Data mining is often used synonymously with predictive analytics, and, in any case, predictive analytics is a type of data mining.
Yes. Predictive analytics is a form of data science. Moreover, it is the most actionable form. A predictive model generates a predictive score for each individual, which in turn directly informs decisions for that individual, e.g., whether to contact, extend a retention offer, approve for credit, investigate for fraud, or apply a certain medical treatment. Rather than solely providing insights, predictive analytics directly drives or informs millions of operational decisions.
Yes. Predictive analytics is a key method to truly leverage big data. At the center of the big data revolution is prediction. The whole point of data is to learn from it to predict. What is the value, the function, the purpose? Predictions drive and render more effective the millions of organizational operational decisions taken every day.
Yes. Artificial intelligence (AI) is a broad, subjective term with many possible definitions—but by any definition, it always includes machine learning (predictive modeling) as an example of AI technology/capabilities.
No. Predictive Analytics World provides a balanced view of predictive analytics methods and tools across software vendors and solution providers.
No. Predictive Analytics World is focused on today’s commercial deployment of predictive analytics, rather than academic or R&D activities. Separately, there are a number of research-oriented conferences; in predictive analytics’ commercial application, we are essentially standing on the shoulders of those giants known as researchers.
For speaker information and proposal submissions, click here.