Dataiku 是一个集中式数据平台，可将企业的数据之旅从大规模分析转移到企业 AI。通过为数据专家和探索者提供共同基础、最佳实践库、机器学习和人工智能部署/管理的捷径以及集中、受控的环境，Dataiku 是数据驱动型公司的催化剂。联合利华、通用电气和 FOX 新闻集团等客户使用 Dataiku 来确保他们快速行动并随着他们收集的数据量成倍增长。通过消除障碍，Dataiku 确保为影响业务的模型和创造性解决方案提供更多机会，使团队能够更快、更智能地工作。
Dataiku的技术栈描绘了Dataiku在平台即服务 (paas), 应用基础设施与中间件, 和 分析与建模等物联网技术方面的实践。
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Improving Manufacturing Processes with Essilor
Seeing that one of their goals is to find ways to better answer consumer and business needs, the Global Engineering (GE) team was facing the challenge of improving processes and performance of the surfacing machines to significantly improve their production by using the increasing volume of data."We wanted a data science platform that would allow us to solve our business use cases very quickly. Thanks to Dataiku and its collaborative platform, which is agile and flexible, data science has become the norm and is now used more widely within our organization and around the world," said Cédric Sileo, Data Science Leader at Global Engineering, Essilor.
U.S. Venture + Dataiku: Upskilling Analysts to Save Thousands of Hours
The Data and Analytics team at U.S. Venture began in 2018. At the time, the team was doing data warehousing and basic reporting, but soon realized they needed the right people and tools to do advanced analytics at scale — maintaining models and disparate data sources were going to become unmanageable quickly without them. That was their initial pain point: They had people that could have built solutions from scratch for DataOps (but no automation surrounding data collection, prep, and model connectivity) — but it wasn’t the time to reinvent the wheel.Further, the team’s data scientists and analysts were using a varied set of tools and coding mechanisms — one data scientist used R, one used Python, some analysts were using SQL while others used Python, and so on. Resultantly, the individual team members built their own components that lived in different places and were created via their own tools, saved on personal computers, with no visibility for other team members about where projects were and how they were created or functioned. This prevented them from supporting each other and collaborating on projects.
Smarter Predictions with Europcar Mobility Group
Europcar Mobility Group was facing specific challenges regarding car rentals at airports and the ability to accurately predict fluctuations in demand based on changes in the market; for example, the International Air Transport Association — IATA — predicts that routes to, from, and within Asia-Pacific will see an extra 2.35 billion annual passengers by 2037. In order to start tackling the problem, Europcar wanted to build an application using data from different sources (both public and private), including fleet traffic and passenger volume, reservation and billing data, data on the opening of new airline routes, and more. This presented another challenge, as the data comes from different places and is in different formats, but is also massive in volume.
Dynamic Pricing With PriceMoov
PriceMoov’s challenge was that data originating from old SI systems, Oracle, or MySql was dirty and required a full-time developer to perform long ETL (extract-transform-load) steps in PHP for cleaning. Once cleaned, the datasets were painfully entered into a model, as they were custom-built pipelines. And once finished, the replication and deployment process for the next customer was taking weeks.
Scaling Up Data Efforts
In 2017, LINK Mobility decided that they wanted to scale up their data efforts both when it came to handling internal requests as well as externally with customers. LINK Mobility produces a lot of data and saw an opportunity to expand its offerings to provide more data-driven insight to customers surrounding the delivery and performance of their messages and services. They were looking to expand to customer dashboards as well as send additional offers based on that data.LINK Mobility needed to find a tool that would allow them to scale up data requests coming from inside the company as well as be flexible enough to provide data insights to customers without having to use two different tools or platforms to cover their various needs.
How Trainline Gets a Global View of Marketing Acquisition
As an online marketplace, Trainline has always been convinced that data adds value for marketing teams. That’s why early on, they created a technical team within the marketing department tasked with creating aggregated, centralized dashboards focused on Trainline marketing acquisition efforts. This ambitious endeavor called for data science skills and a tool strong enough to blend and support multiple data formats and sources to track acquisition according to certain parameters.Starting from scratch, how could the technical team ensure they ended up with a tool that allowed them to improve and upgrade their own skills while also satisfying the marketing department’s requests quickly and efficiently?
Building a Sustainable Data Practice
The client services department at Orange has a data science team that, until two years ago, was performing mostly ad-hoc analysis for the business and had limited ability to work on more complex machine learning-based projects. In order to scale out the team and expand their scope, they had to overcome several challenges:Tooling: Only those who knew the tool and its proprietary language could work with data, which limited the use of data to statisticians or data scientists. Even then, the data was difficult to access, making projects difficult to get off the ground.Hiring: The data team at Orange was struggling to hire talented data scientists fresh out of university and with lots of ambition as well as creative ideas (traits they were seeking to enliven their data science practice). Unfortunately, this was largely a function of the tooling challenge, as young data scientists were largely looking for jobs where they could work with open-source tools (such as Python or R).
Developing an Elastic AI Strategy
Five years in, data warehouse costs were spiraling out of control, and performance was suffering as the amount of data grew. The company needed to find a solution that would allow anyone across the organization to work with large amounts of data while also ensuring optimized resource allocation.In 2019, Heetch chose Dataiku as their single platform for building data pipelines and processing raw data, paired with Looker for the seamless visualization and exploration of those flows.
Automated Dashboards in Customer Analysis
The primary point of contact between OVH and its users is through its website, where customers can place an order and receive technical advice or support. But the business analysts responsible for disseminating data and insights to inform on the commercialization and optimization of the website were facing issues.The main problem was that the dashboard didn’t combine different data sources for a complete view, so it necessitated ad-hoc analysis, for which the analysts had little time. Additionally, ETL (extract, transform, load) for the dashboard presented concerns for the data architects around data and insights quality, as there was a lack of transparency around exactly what data was being transformed and how.
C3 IoT provides a full-stack IoT development platform (PaaS) that enables the rapid design, development, and deployment of even the largest-scale big data / IoT applications that leverage telemetry, elastic Cloud Computing, analytics, and Machine Learning to apply the power of predictive analytics to any business value chain. C3 IoT also provides a family of turn-key SaaS IoT applications including Predictive Maintenance, fraud detection, sensor network health, supply chain optimization, investment planning, and customer engagement. Customers can use pre-built C3 IoT applications, adapt those applications using the platform’s toolset, or build custom applications using C3 IoT’s Platform as a Service.Year founded: 2009
Altair is a leading provider of enterprise-class engineering software enabling innovation, reduced development times, and lower costs through the entire product lifecycle from concept design to in-service operation. Our simulation-driven approach to innovation is powered by our integrated suite of software which optimizes design performance across multiple disciplines encompassing structures, motion, fluids, thermal management, electromagnetics, system modeling and embedded systems, while also providing data analytics and true-to-life visualization and rendering.
Alteryx, Inc. was formed in 2011 and is a leader in self-service Data Science and analytics. Alteryx provides analysts with the unique ability to easily prep, blend and analyze all of their data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks.Analysts love the Alteryx Analytics platform because they can connect to and cleanse data from data warehouses, cloud applications, spreadsheets and other sources, easily join this data together, then perform analytics – predictive, statistical and spatial – using the same intuitive user interface, without writing any code.