Charlie Holmes

Interview - adopting data analytics at Kohl's

In this interview, Charlie Holmes discusses the data analytics agenda at the department store chain, Kohl's.  Specifically, he considers the drivers for analytics, the talent challenges as well as providing some advice for companies starting a similar program.

Gahl Berkooz

How are big data and analytics changing the PLM landscape?

The PLM vendor landscape first started with a group of niche companies that offered a combined hardware/software solution. They persisted up until 'work stations' and improved functionality in the database space arose that killed these vendors off. This gave birth to today's generation of vendors that for the past 5-10 years have really held the PLM fort.

But today, Big Data is about taking the data-based technology underlying Google (Hadoop) and making this available to everyone. The fundamental changes that will follow in how data is collected, processed and ultimately used are pushing these PLM household names into a niche group of their own and it stands to reason that they too will become outdated dinosaurs and become extinct paving the way for the next generation of technology.

This session discusses:

  • 'A Brief History of PLM' - how is the vendor data management changing?
  • 'Dawn of the Big Data Giants' - how is big data transforming the way we do business?
  • Enabling superior product development through Big Data & Analytics
  • Improving the inclusion of the consumer's voice into product design
  • Enhanced Test Data Management through better mining practices and subsequent avoidance of failure
  • Evolving the Connected Vehicle
  • Wrapping up Big Data capabilities and exploring how best to link this with PLM and product development

Charlie Holmes

Effectively deploying Data Analytics

Most companies are looking to data analytics as a way to provide their executives the transparency they need to run an agile, flexible and profitable business. At Kohl's, they are taking this one step further.

As a multi-brand organisation, their focus is brand clarity, brand alignment and customer-centricity. And data analytics lends itself nicely to this; by exploring, mining and developing existing data as well as incorporating new data from both their customers and their competitive industry at large, the hope is that they will gain true visibility across the corporation and in doing so better support effective decision making in the product development and innovation space. This session covers:

  • Examining all existing datasets and their accessibility
  • Re-evaluating how changes in data infrastructure might make business better
  • Exploring existing brand and customer profiles, and putting a data mining process in place with clear parameters
  • Understanding the team-specific siloed nature of data collection and re-building this
  • Building a data analytics team and fostering the right culture
  • Supporting a 'less-time mining, more-time decision making' policy
  • Creating a centralized data organization that is visually presentable and circles back to innovation
  • Supporting competitive product development through analytics

Charles Cai

Building the Right Big Data and Analytics Team For the Big Impact

Charles has been pioneering Big Data journeys in the enterprise world for many years, not only being instrumental in massive big data initiatives to revolutionize supply demand chains in finance and the oil & gas industries, but more importantly, his evangelism and community work has earned him one of the UK’s 50 Top Data Leaders and Influencers. 

In this talk, Charles shares best practices in strategizing from ground zero, in involving the CFO/CIO early, in gaining buy-in and in new ways of developing in-house and “virtual” Big Data and Data Science teams. Such disciplines are very transferable and there’s a huge market shortage for these in the foreseeable future.

By now, most industries have realized that the right data science team can turn silos of diverse data into game-changing business insights. The next stage is working out how to piece together the perfect combination of technical skills and personality traits which isn’t easy.

This session covers:

  • How to define an enterprise-wide Big Data/Data science maturity model
  • Assessing the characteristics and skills of winning big data teams
  • Fostering the right culture to integrate new data science strategies and practices into your existing organisational structure
  • Highlighting proven big impacts on a few vertical industries using real examples of data strategy to win big on the bottom line

Andreas Schierenbeck

Lifting Data Analytics to the Next Level

In 2015 thyssenkrupp Elevator launched MAX, a game-changing predictive and pre-emptive service solution that extends remote monitoring capabilities to dramatically increase current availability levels of existing and new elevators. Utilizing the power of Microsoft Azure Internet of Things (IoT) technology, MAX makes it possible for an elevator to “tell” service technicians its real needs, including real-time identification of repairs, component replacements, and proactive system maintenance.

thyssenkrupp Elevator is now revolutionizing the industry and started something nobody else in their field has done before: To transform a century-old industry that has relied on established technology until now.

Topics covered in this interview include:

  • Learning from past data experiences
  • Commercial opportunities and commercial limits
  • The retro-fitting formula: how and when to upgrade existing units
  • Taking the ultimate step: moving from preventive to pre-emptive maintenance
  • Utilizing virtual and augmented reality to offer a new quality of remote services

Nick Leeder

The Next Stage in Digital Transformation - Building up Data Analytics

SKF have over 115 factories across the globe with varying manufacturing capabilities and processes and are currently taking those through a complex digital transformation. With legacy machines and processes built and introduced over the last 50 years, the variance in digital adoption capability is a challenge. 

They came to the realization that they had to make some fundamental changes if they were going to gain the full benefits of digitization. With the value understood, they began learning fast how to best invest and harness this opportunity.

This session covers:

  • Connect, Collect, Correlate and Collaborate - making the digital real 
  • Seeing data differently - using smart data and augmentation to bring context to data
  • SKF's perspective on the digital twin
  • Connected products and connected operations - building up PLM analytics