Get an Inside Look at Watson Studio Portfolio

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Get an Inside Look at Watson Studio Portfolio

Take30 with a Data Scientist – 5/26/20

In this session, we join forces with our IBM partner to discuss the latest developments and answer your questions on Watson Studio.

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Is Data Science-as-a-Service Right for your Organization?

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Is Data Science-as-a-Service Right for your Organization?

Take30 with a Data Scientist – 5/19/20

DSaaS is a way to accelerate your progress towards production-ready AI by leveraging platforms, technology, open-source methodology, and even data science teams to fill the gaps in your own AI environment. It’s an optimal way for organizations new to AI to get started without capital expenses in technology or people.

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More Data, More Insight: The Value of Data Enrichment for Analytics

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More Data, More Insight: The Value of Data Enrichment for Analytics

Take30 with a Data Scientist – 5/12/20

This session can be coupled with Data Enrichment for Analytics on May 13th @ 11am. It highlights enriching client data with 3rd Party Syncsort data to gain additional insight in your AI models.

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It’s All About The Use Cases

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It’s All About The Use Cases

Take30 with a Data Scientist – 5/5/20

What makes a good AI use case? Learn about Ironside’s approach to defining and developing AI use cases and what questions you should be asking to curate a use case catalog of your own.

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Paul Hardy | VP, Advisory Services

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Ethics & AI

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Ethics & AI

Take30 with a Data Scientist – 4/28/20

AI presents a whole new set of ethical challenges — and many questions about them. Join us to hear our thoughts on ethical machine learning, removing biases and privacy, and share your own questions and concerns.

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Getting Started with AI

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Getting Started with AI

Take30 with a Data Scientist – 4/21/20

If your organization is ready for AI but not sure where to start, don’t miss this chance to get expert advice from our lead data scientist on how to avoid common pitfalls and the questions you and your team should be asking about AI.

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AI Jump-Start at Cartus to Create a Competitive Edge

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ANALYTICS | CASE STUDY

AI Jump-Start at Cartus to Create a Competitive Edge

COMPANY OVERVIEW


Having moved over 2.3 MM people for work in the last 30 years alone, Cartus is the largest global relocation management company (RMC) in the world by volume. For more than 60 years, Cartus has helped relocating employees and their families find their way to new homes, new communities, and new experiences. They do it through an unwavering focus on listening and delivering services, solutions, and expertise that respond to their needs and the needs of their client companies around the world.

BUSINESS NEED


Cartus had a strong understanding of business intelligence and data warehousing. They had a structured data environment with some rigor around data governance and business processes for how data was managed during the life cycle.

They were looking to build a data science program that would provide increased customer value, leverage their wealth of data, and help drive them forward and transform from a traditional RMC to a “data-first,” “technology-first” organization within their industry.

SOLUTION: AI JUMP-START


Ironside’s data scientists, data engineers and Experience Design team engaged with Cartus on an “AI Jump-Start” to implement predictive analytics capabilities to enhance their relocation products for their corporate clients with decision support. Ultimately, this would allow Cartus to enable the sales and client services organizations to make their products even more attractive to their customers.

We applied the 5 phases on the AI Jump-Start to ensure that the end result was tailored to Cartus’ needs:


Provide AI Education


  • Educated both business and technology leadership at Cartus to develop a process to define key elements of strategy and consensus building

  • Established a partnership between key stakeholders across the organization – not just technical but also people who had a deep understanding of the products, business and processes and how they worked

  • Secured executive buy-in early in the process by meeting with the CEO to understand Cartus’ objectives and their plan for investment in data science to transform their organization and how they go to market


Establish AI Governance


  • Identified and established a Steering Committee made up of key stakeholders

  • Chartered the program and created a forum for communication with channels and process

  • Established a cadence for communicating results and prioritizing activities through the group of stakeholders


Develop AI Use Cases


  • Talked to process experts and corporate clients across the organization

  • Heard from the personas that were important to them in terms of the products they were trying to build and learned what their pain points were

  • Applied design thinking to the process and envisioned what a decision support product would look like to support the objectives of the individuals

  • Built a portfolio of data science use cases


Pilot the AI Solution


  • Ran a series of rapid experiments using DataRobot on AWS Platform to determine where there were gaps in the data and whether a particular use case would be feasible or not

  • Managed risk very early in the process so Cartus didn’t over-invest in any one direction before knowing if there was efficacy in that use case

  • Continuously communicated back to the organization to keep people up to speed on progress and results

  • Pivoted and documented what was learned when met with a roadblock/obstacle

  • Provided recommendations around how the organization might achieve goals through increased data governance, data quality, data modeling and data collection activities

number 5

Deploy Technology for Speed & Scale

  • Used the use cases and business processes to gain understanding about the user experience and how it all cohesively came together in a product

  • Built an environment where data scientists can get access to highly granular, detailed information in a raw state, which is critical for doing machine learning, early feature engineering, and the data prep process

  • Implemented a data wrangling toolset that is accessible to a variety of different skill levels within the data science team or for those using data science methods to solve problems

  • Leverage Corporate Data Lake on AWS on S3 to be able to access data from variety of sources

  • Leverage AWS VPC to securely access OnPrem, the AWS Data Lake, and within other Cloud Platforms

  • Set up DataRobot to manage the lifecycle of a machine learning model

  • Expressed results in a way that made sense, was easy to navigate and was contextual to the analysis that we were doing

  • Provided full transparency so they would understand the lineage of any decision or advice

  • Put model management monitoring in place after deployment to monitor and learn in order to lower the cost of ownership and operating cost of the machine learning environment on an ongoing basis


KEYS TO SUCCESS


For this initiative, Ironside partnered with DataRobot on AWS EC2 to be the catalyst for a data science practice within Cartus: their Leading Edge Analytics Practice, or LEAP. This partnership enabled the non-data scientist knowledge workers to contribute and made the life cycle of the machine learning model easily manageable within a few clicks of the entire platform.

Cartus is a lean team that was trying to build this capability out within the organization in an intelligent and effective manner.

As a consulting organization, we expect certain hiccups along the way and are capable of processing and dealing with any obstacles. When things get busy, we can bring in the right amount of resources to address the issues, and when it slows down, we can ramp down. We encourage our clients to be smart in terms of how they spend their money very early on in the process. This was advantageous to Cartus – we were working with them to build a long-term, sustaining program.

By working collaboratively with Ironside throughout this entire process, Cartus was able to see many benefits, including:

  • Reduce experimentation cost and risk by reducing cycle times

  • Increase model effectiveness through greater accuracy

  • Enable the organization to up-skill existing talent

  • Put statistical methods in the hands of BI analysts so that more accurate and unbiased business analysis can be done than what is possible with visual methods alone

WHY DATAROBOT?


Cartus wanted to be smart, run lean, and create a great amount of leverage in their data science capability. Cartus did not have any data scientists on staff when we started the process, but they did have operations researchers, industrial engineers and data analysts/domain experts that knew a lot about their business, the data and the processes. We felt that DataRobot on AWS was the perfect platform to be the catalyst for a data science practice within Cartus.

Cartus has used DataRobot to help their corporate clients forecast volume (using time series), predict individual move time and expenses, and even be able to determine when a relocating employee is at risk for exceeding the budget or experiencing a delay in the process.

ENABLE AI AT YOUR ORGANIZATION


Ironside’s AI Jump-Start is an accelerator offering comprised of targeted consulting and a pre-integrated suite of tools that that support the full data governance, management and data science life cycle, with DataRobot being an integral link in that value chain.

The platform is an answer to a recurring problem that we’ve observed in our clients who are new to data science, or perhaps more used to doing traditional data warehousing and BI. They have sometimes struggled to implement a program and architect an environment that is optimized for machine learning and the high-speed agile iterations that are more germane to data science.

We’re seeking to remove this friction from the process and more rapidly create value with machine learning by simplifying this critical early phase of their journey.


GET STARTED TODAY

IRONSIDE IS A DATAROBOT PARTNER


Through our experiences we’ve formulated a point-of-view on how data science should work in practice and how to develop use cases that generate real value. We also have a core belief that if we seek to enable more non-data scientists to answer questions in a data science way, using statistical or machine learning methods over visual methods alone, that they and their organizations would be much smarter, and mature faster in their use of data for decision making.

DataRobot is a manifestation of our core values. Our partnership is a combination of platform as well as the compatibility of our approach and people.


DataRobot AI Partner 2019

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