data-driven

Analytics at ING: Technology solutions to create a real-time, data-driven bank

January 2, 2020

Bas Geerdink explains why and how ING is becoming more and more data-driven, sharing use cases, architecture, and technology choices along the way.

Distributed real-time highly available stream processing

January 1, 2020

Yu-Xi Lim and Michal Wegrzyn outline a high-throughput distributed software pattern capable of processing event streams in real time. At its core, the pattern relies on functional reactive programming idioms to shard and splice state fragments, ensuring high horizontal scalability, reliability, and high availability.

Executive Briefing: How to structure, recruit, operationalize, and maintain your insights organization

January 1, 2020

To many organizations, big data analytics is still a solution looking for a problem. Ricky Barron shares practical methods for getting the best out of your big data analytics capability and explains why establishing an "insights group" can improve the bottom line, drive performance, optimize processes, and create new data-driven products and solutions.

Industrial machine learning

December 31, 2019

The ongoing digitization of the industrial-scale machines that power and enable human activity is itself a major global transformation. Joshua Bloom explains why the real revolutionin efficiencies and in improved and saved liveswill happen when machine learning automation and insights are properly coupled to the complex systems of industrial data.

R you ready for the cloud? Using R for operationalizing an enterprise-grade data science solution on Azure

December 30, 2019

R has long been criticized for its limitations on scalable data analytics. What's needed is an R-centric paradigm that enables data scientists to elastically harness cloud resources of manifold computing capability for large-scale data analytics. Le Zhang and Graham Williams demonstrate how to operationalize an E2E enterprise-grade pipeline for big data analyticsall within R.

An architecture for merging fast data and enterprise applications: The SMACK stack

December 28, 2019

Big data architectures and enterprise/microservice architectures are slowly converging. Big data is transitioning to "fast data," emphasizing streaming over batch processing, while data processing is growing ubiquitous. Dean Wampler explores the SMACK stackSpark, Mesos, Akka, Cassandra, and Kafkaand explains how it addresses the needs of both fast data and the enterprise.

The value of a data science center of excellence (COE)

December 28, 2019

As organizations turn to data-driven strategies, they are also increasingly exploring the creation of a data science or analytic center of excellence (COE). Benjamin Wright-Jones and Simon Lidberg outline the building blocks of a center of excellence and describe the value for organizations embarking on data-driven strategies.

Turning fails into wins

December 28, 2019

Being a data-driven company means that we have to move fast and fail often. But how do we learn to not only be proud of our failures but also turn these fails into wins? Grace Tang explains how to set up experiments so that negative results become epic wins, saving your team time, effort, and money, instead of just being swept under the carpet.

Discover how Adidas is using data science to deliver third-party governance

December 22, 2019

Drawing on examples from Adidas, Kristian Skoeld explains how to create a data-driven third-party governance strategy that delivers greater insights into frontend performance and continuous delivery.

Big data in healthcare

December 15, 2019

While other industries have embraced the digital era, healthcare is still playing catch-up. Kaiser Permanente has been a leader in healthcare technology and first started using computing to improve healthcare results in the 1960s. Taposh Roy, Rajiv Synghal, and Sabrina Dahlgren offer an overview of Kaisers big data strategy and explain how other organizations can adopt similar strategies.

Choice Hotels's journey to better understand its customers through self-service analytics

December 14, 2019

Narasimhan Sampath and Avinash Ramineni share how Choice Hotels International used Spark Streaming, Kafka, Spark, and Spark SQL to create an advanced analytics platform that enables business users to be self-reliant by accessing the data they need from a variety of sources to generate customer insights and property dashboards and enable data-driven decisions with minimal IT engagement.

Data case studies

December 14, 2019

The road to a data-driven business is paved with hard-won lessons, painful mistakes, and clever insights. We're introducing a new Tutorial Day track packed with case studies, where you can hear from practitioners across a wide range of industries.

Data science at eHarmony: A generalized framework for personalization

December 14, 2019

Data science has always been a focus at eHarmony, but recently more business units have needed data-driven models. Jonathan Morra introduces Aloha, an open source project that allows the modeling group to quickly deploy type-safe accurate models to production, and explores how eHarmony creates models with Apache Spark and how it uses them.

Delivering near real-time mobility insights at Swisscom

December 13, 2019

Swisscom, the leading mobile service provider in Switzerland, also provides data-driven intelligence through the analysis of its mobile network. Its Mobility Insights team works to help administrators understand the flow of people through their location of interest. Franois Garillot explores the platform, tooling, and choices that help achieve this service and some challenges the team has faced.

Open the black box: An executive guide to making unstructured data work in finance

December 11, 2019

You need to think about financial data differently to solve the most pressing challenges for your organization. But how do you get to data-driven finance and risk using unstructured data? Michelle Bonat explains how unstructured data (words and text) can be used to solve critical challenges in finance and unlock opportunities.

Transforming healthcare through precision data science: Myths and facts

December 9, 2019

Healthcare, a $3 trillion industry, is ripe for disruption through data science. However, there are many challenges in the journey to make healthcare a truly transparent, consumer-centric, data-driven industry. Sriram Vishwanath shares some myths and facts about data science's impact on healthcare.

Using graph databases to operationalize insights from big data

December 8, 2019

Tim Williamson and Emil Eifrem explain how organizations can use graph databases to operationalize insights from big data, drawing on the real-life example of Monsantos use of graph databases to conduct real-time graph analysis of the companys data to transform the business in ways that were previously impossible.

Winning with data: How ThredUp, Twilio, and Warby Parker use data to build advantage

December 7, 2019

Daniel Mintz dives into case studies from three companiesThredUp, Twilio, and Warby Parkerthat use data to generate sustainable competitive advantages in their industries.

Protecting individual privacy in a data-driven world

November 17, 2019

With the analytic and predictive power of big data comes the responsibility to respect and protect individual privacy. As citizens, we should hold organizations to account; as data practitioners, we must find intelligent ways to analyze data without violating privacy. Jason McFall discusses privacy risks and surveys leading privacy-preserving analysis techniques.

Civic data: Using open source data to create socially impactful data-driven products

November 14, 2019

A plethora of data has been made freely available on the Web. A large chunk of this data is civic data: on education, social welfare, demographics, and the like. Vida Williams explains how this data can be assembled into open source products that can help states, cities, municipalities, nonprofit organizations, and citizens make decisions that will positively impact their immediate neighborhoods.

Let's make the pain visible

October 30, 2019

We've been trying to explain the pain of technical debt for generations, but we've never been able to measure it. What if we could measure the effects of learning curves, collaboration pain, and problems building up in the code? Janelle Klein outlines the Idea Flow Learning Framework, a strategy for measuring the friction in developer experience that introduces a whole new world of possibilities.

A few things engineers can learn from designers

October 29, 2019

Big data is great for feeding ML algorithms, but you quickly face a bandwidth issue when interfacing with humans. The brain is a fantastic information-processing machine and has an unparalleled, innate ability to detect patterns. Sbastien Pierre explains what designers can teach engineers about creating new ways to make large volumes of data understandable at the human level.

Delivering "DARPA hard"

October 26, 2019

The Defense Advanced Research Projects Agency (DARPA) is synonymous with transformational change, developing the seeming impossible into the practical. Matthew van Adelsberg demonstrates how collaborative teams of SMEs, data scientists, and engineers have been organized to achieve DARPA hard results for nearly a decade and offers insights into how companies can do the same.

How to use your data science team: Becoming a data-driven organization

October 24, 2019

Youve decided you need data scientists. You know who to hire. Now, what do you do with them? Yael Garten offers examples of how companies like LinkedIn use data to make business and product decisions. Yael reviews the spectrum of data science, and discusses the culture, process and tools needed to transform companies into data-driven organizations.

Its all about me: From big data models to personalized experience

October 15, 2019

Even though each of us is only 1 of 7 billion, we all want to feel special. Product personalization is transforming the Internet experience to be all about me (and you too!). Yao Morin explores how to create customer-centric experiences through data science and software engineering, using Intuit TurboTax as a case study.