Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Executive Briefing: Profit from AI and machine learning—The best practices for people and process

Tony Baer (Ovum), Florian Douetteau (DATAIKU)
1:15pm–1:55pm Wednesday, 09/12/2018
Data-driven business management, Strata Business Summit
Location: 1E 14 Level: Non-technical
Secondary topics:  Machine Learning in the enterprise
Average rating: ***..
(3.40, 5 ratings)

What you'll learn

  • Learn how to evaluate your organization’s readiness to benefit from AI/ML, identify the right AI/ML approaches for the problem, identify the right people for your teams and ensure productive collaboration, and measure and track the benefits that AI/ML contributes to the business

Description

For projects employing machine learning or deep learning, the acid test doesn’t come from making that first “win.” Instead, the true test is making successes from embracing AI consistent and repeatable. Like most new technology innovations, for AI, the spotlight has initially been on the technology. Because the AI practice in the enterprise is still in its infancy, there is less knowledge about the “soft” side: understanding how to build the teams of people that make AI happen and creating the processes that can make success repeatable.

Dataiku and Ovum are collaborating on a jointly sponsored primary research study to address the knowledge gap on “the soft side” of making AI work for the business, conducting a qualitative survey of specially selected leaders and practitioners in the field, including chief data officers, chief officers and directors of data science, and chief officers and directors of analytics. Tony Baer and Florian Douetteau summarize the lessons learned from this research and identify best practices for ingraining AI into the business, based on actual experience in the field.

Tony and Florian outline the best practices that early adopters have identified for challenges such as:

  • Securing buy-in: Ensuring all relevant stakeholders and line organizations are involved to secure support from the bottom up and championship from the top down
  • Operationalizing AI into the business: Ensuring processes are in place for embedding AI into the business and tracking the benefits that the business is realizing
  • Problem selection: Having a robust process for identifying the right business and operational problems for applying AI
  • Team building: Identifying the right mix of players and roles and the best mix of skills and temperaments
  • Selecting the most optimal AI strategy: Outlining criteria for identifying the best problem-solving approaches (e.g., machine learning and/or deep learning, and the requisite models and frameworks)
  • Keeping processes agile: Building a culture that encourages constant challenging of assumptions and rewards “failing fast”
  • Maintaining alignment: Ensuring the the AI models and datasets remain relevant to the business through constant evaluation and reevaluation for detecting “drift” or degradation of models and datasets
  • Mastering collaboration: Ensuring communications channels are open for the regular exchange of ideas between practitioners and the business to keep projected properly aligned
  • Preserving accountability: Developing practices for documenting and “explaining” the purpose and intent of models

Join in to learn key action items for keeping your machine learning and deep learning projects aligned with the business.

Photo of Tony Baer

Tony Baer

Ovum

Tony Baer is a principal analyst at Ovum, where he leads the company’s research in big data, middleware, and the management of embedded software development in the product lifecycle. Tony has defined the architecture, use cases, and market outlook for big data and led the industry’s first global enterprise survey on big data adoption. Tony has been a noted authority on data management, integration architecture, and software development platforms for nearly 20 years. Previously, he was an independent analyst whose company, onStrategies, delivered software development and integration tools to vendors with technology assessment and market positioning services. He coauthored some of the earliest books on the Java and .NET frameworks, including Understanding the .NET Framework and J2EE Technology in Practice. His career began as journalist with leading publications including Computerworld, Application Development Trends, Computergram, Software Magazine, InformationWeek, and Manufacturing Business Technology.

Photo of Florian Douetteau

Florian Douetteau

DATAIKU

Florian Douetteau is the CEO of Dataiku, a company democratizing access to data science. After starting programming in his early childhood, Florian dropped out of the prestigious École Normale maths courses to start working at a startup that later became Exalead, a search engine company in the early days of the French startup community. His subjects of interests include data, artificial intelligence, and how tech can improve the daily work-life of tech people.

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Comments

Armel Traore dit Nignan | ASST DIRECTOR - ANALYTICS CONSULTING
09/12/2018 11:16am EDT

Mr Baer, any chance you’d share your slides? Great presentation. Thanks