Seminars

18:00 - 20:00  |  Self-Service Analytics Theatre

MEETUP: Beating the Odds & Benchmarking for Data Analytics

Wednesday 13 November 2019

ABOUT

BROUGHT TO YOU BY LONDON PROJECT DATA ANALYTICS MEETUP GROUP

TALK 1: BENCHMARKING AND DATA ANALYTICS FOR IMPROVED PROJECT PERFORMANCE

Tim will introduce the subject of Benchmarking and illustrate with professional and personal examples. He will describe how considered use of data can drive continuous improvement, support target setting and
foster sharing and learning across an organisation and with outside peers; and will explain how benchmarking can be applied to improving Project Performance and illustrate with examples of cost, schedule and performance benchmarking in addition to benchmarking for project best practice.

Project Management topics that will be covered;
- Benchmarking
- Continuous Improvement
- Target Setting
- Knowledge Management
- Best Practices

The presentation will explore the potential for data analytics to enhance benchmarking by providing more insights around project plans, risks and practices and in real time show leading indicators of project progress and potential issues.

TALK 2: BEATING THE ODDS: HOW TO MAKE YOUR DATA PROJECT OR TEAM PART OF THE 15% SUCCESS STORY

Making data science a success is really hard with up to 85% of projects and initiatives around big data and data science failing, according to Gartner. The reasons are complex but often misunderstood. As project management begins to grapple with the opportunities presented by data science those responsible for implementation need to proceed with care.

What is so different about data that it needs new approaches? This talk will focus on the requirements for data science success and looks at a future after the hype:

Motivation: Vanity project or aligned business strategy with senior leadership buy-in?
Requirements and preparations: Solid foundations or duct taped data silos and constant fire fighting bad data?
Hiring: Unicorns with the right skill sets to be a commercial data scientist or expensive mis-hires?
Delivery: Models in production serving business needs or undocumented proof of concepts on laptops?
Retention: Roadmap of game changing projects or abandoned team and expensive write-offs?

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