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.
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are pushing these PLM household names into a niche group of their own and it stands to reason that they too will become outdated dinosaurs and become extinct paving the way for the next generation of technology.
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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:
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.
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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.
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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.
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