www.themanufacturer.com Open in urlscan Pro
2a06:98c1:3120::3  Public Scan

Form analysis
1 forms found in the DOM

GET /

<form action="/" method="get" itemprop="potentialAction" itemscope="" itemtype="http://schema.org/SearchAction">
  <meta itemprop="target" content="https://www.themanufacturer.com/?s={s}">
  <input class="search-field" itemprop="query-input" type="search" name="s" value="" placeholder="Search The Manufacturer" required="">
  <button type="submit" class="search-submit icon-search" title="Submit"><span class="screen-reader-text">Search</span></button>
</form>

Text Content

This website stores cookies on your computer. These cookies are used to collect
information about how you interact with our website and allow us to remember
you. We use this information in order to improve and customize your browsing
experience and for analytics and metrics about our visitors both on this website
and other media. To find out more about the cookies we use, see our Privacy
Policy.

If you decline, your information won’t be tracked when you visit this website. A
single cookie will be used in your browser to remember your preference not to be
tracked.

Accept Decline
Industrial Data & AI



Site Search
 * Log In
 * Subscribe

Edition: UK
 * AUS edition
 * IND edition
 * LAT edition
 * USA edition

 * Site Search
 * Subscribe to newsletter

Edition: UK
 * AUS edition
 * IND edition
 * LAT edition
 * USA edition

Search
Close
Automation Digital Transformation Industrial Data & AI Innovation Leadership
People & Skills Sustainability


Log In Subscribe


Edition: UK
 * AUS edition
 * IND edition
 * LAT edition
 * USA edition

 * Site Search
 * Subscribe to newsletter

Events Multimedia
* Videos
* Podcasts
* Archives
* Webinars
Advertise Magazine Reports Sectors
* Aerospace
* Automation
* Automotive
* Construction
* Chemicals
* Defence
* Energy
* Engineering
* Electronics
* Food and Drink
* Healthcare
* Machine tools
* Materials
* Metalworking
* Nuclear
* Pharmaceuticals
* Renewables
* Research and Technology
* Shipbuilding
* Space
* Textiles
* Transportation
Press Releases Jobs The Manufacturer Live TMMX Awards Top 100 Awards



DATA LESSONS: A REVIEW OF INDUSTRIAL DATA SUMMIT 2023

Posted on 27 Jun 2023 by Joe Bush
   Share this article:
 * Share on Linkedin
 * Share on Twitter
 * Share on facebook

Read More
Industrial Data Summit 2023 was held at Villa Park, Birmingham

The sixth annual Industrial Data Summit took place at Villa Park, Birmingham, on
26 April where the UK’s most senior manufacturing data professionals came
together to talk data and analytics in their businesses.

In the opening keynote of Industrial Data Summit, Rob Clifford, Head of Digital
and Data, BAE Systems, spoke about applying lessons from digital transformation
in the public sector to the manufacturing industry. The manufacturing sector is
the latest domain to embrace the digital and industrial data transformation
ethos. While there is ground to make up, there’s also huge opportunity to
accelerate change by learning from successes and failures in other sectors.

The Manufacturer spoke to Rob around the topic of cyber security within
manufacturing. The full interview can be seen in the Jan/Feb issue of The
Manufacturer magazine. We often talk around the importance of people here at The
Manufacturer and the second keynote of the day saw Kat Dixon, Analytics Business
Partner at Babcock International, discuss the realities of creating an
industrial data-driven culture, warning that businesses often have a tendency to
dive into the technicalities of digital transformations.

However, no amount of new technology is going to work without first having the
right foundations and organisational culture. A full interview with Kat can be
found in our Mar/Apr issue. In this article, however, we look at the findings
from the summit’s eight discussion tables, where the table hosts share the
popular discussion topics of the day and their key takeaways.

AI and Machine Learning

Tom Vaughan, Solutions Engineer, Peak AI

Four key points came out of our discussions. Number one was to identify the
process and see where it can be improved. In order to get to an AI and machine
learning base, you first need to understand the process you’re trying to
improve; establish what the challenge is across the business and the process
improvement that can take place.

Number two was industrial data strategy and systems. What is the quality of data
and systems you currently use? Are they interconnected or are they siloed? And
do you have the foundation layer to be able to bring in an AI or machine
learning model?

Quite a large part of the discussion, spoken about passionately, was the change
management side of data. When bringing in AI and machine learning, it’s
important to remember that it’s not there to replace people, it’s there to
enhance people.

We heard some great examples of where machine learning could be deployed within
a business. The best results have been where machine learning perhaps performs
the algorithm and builds the model, but then allows people to make the final
strategic decision.

Finally, topic four was around the AI and machine learning models themselves and
how to get an effective AI and machine learning model within your business. That
involves the three previous elements – process improvement, having a great data
strategy and change management.

Data Strategy

Jennifer Belissent, Principal Data Strategist, Snowflake

One of the big issues that we discussed was what is a data strategy? We started
with the idea that perhaps an industrial data strategy doesn’t exist on its own
– it’s an element or an enabler of a larger business strategy.

So, when thinking of wider business objectives, manufacturers need to ask what
they are trying to do as an organisation; whether it’s to reduce cost, increase
revenues, improve customer experience or reduce risk. What needs to be done with
data then flows from that, enabling the whole data value chain from data use
through to investments in the pillars of people, process and technology.

It’s not just about having a data strategy that encompasses technology, it’s
about the people and the processes that we need to set up. Therefore, we also
spoke about where you start; a number of people highlighted the fact that they
had a data strategy but were unsure of how to implement it.

We discussed how to identify data champions; whether it’s from parts of the
organisation that are more data mature, or people within the organisation that
have an affinity to data and really want to experiment and innovate. From there
broader communities can be developed, creating a snowball effect. These
champions should be given the opportunity to share what they’ve done and to
showcase their work as a means of professional development.

We also spoke about what else is required to scale i.e., the communications
element of a data strategy, and how you spread the word on what data is in the
organisation, so everyone understands their role; whether it’s capturing,
protecting or using data. We spoke about getting more people involved in this
data-driven culture.

The ‘D’ in chief data officer doesn’t just stand for ‘data’, it stands for
diplomacy as well, because it’s about bridging gaps within different
organisations and rationalising the requirements across the business.

Data Governance

Niall Buswel, Procurement Data Lead, Jaguar Land Rover

It was very valuable to hear what everyone collectively has been going through
and it was reassuring to know that we’re generally facing the same challenges.

Even though the main topic was data governance, we also spent a lot of time
talking about data quality and how organisations are approaching that. As well
as discussions around ownership and stewardship of data, we also spoke about the
standards and policies around data governance and how we get business buy in,
throughout the organisation, from the top floor to shop floor.

Another theme focused on delivering value. You can have all the best policies
and data quality metrics in place, but how do you showcase that to the senior
leadership team and the wider business? How is it impacting day-to-day
operations and how are you delivering value for others throughout the
organisation?

IoT and Sensors

Matt Mould, Partner, Storm Reply

We learned a lot about where people are in their journey with IoT and the good
news is there are similarities in terms of where everyone’s at. One universal
complexity seems to be a wide, disparate collection of machines that have to be
understood to satisfy leaders that a plant is operationally efficient, and that
energy is being tracked. The challenge, however, is that a lot of separate
vendors own the machines that need to be changed.

We also tackled the issue of talent attraction in this space. Manufacturing was
referred to as the backbone of the country, yet it is operating in the
background. Therefore, attracting the talent to compete with other, more
recognisable industries, is also a challenge.

We also debated why we extract data from machines and why we integrate data from
other systems? But these questions need to be answered in the context of the
business case to enable investment in this space, particularly with IoT and
sensors. Because the impact on the bottom line isn’t always immediately obvious.

Data Collaboration

Sean Robinson, Manager, Software Solutions, Novotek UK & Ireland

Our discussions kept returning to working backwards from goals that matter to
leadership, and then translating that to things that are very specific. It’s a
lot easier to get people to collaborate around something that’s understood (such
as launching a new product, changing a product definition or even building a new
factory), than it is to collaborate around industrial data for a more nebulous
goal that may be seen as detached from day-to-day business operations.

Some of the cultural challenges around that collaboration relate to people
feeling a sense of ownership and protectiveness for their system. It was
interesting to note how few people were able to say that they had clear data
owners.

That links back to keeping data specific; know what you’re doing in support of
the organisation’s goals and strategy, but then understand how collaboration has
to happen. From there you can create a culture that’s more capable to think
about specific improvement initiatives or more detached exercises, because there
will be a higher level of trust.

Security was supposed to be a big part of our discussion table. I would argue
that what we ended up concluding was that more often than not, the technical
security aspects of enabling collaboration, even around sensitive data, are less
of a barrier than culture. You need people who are domain experts in what really
matters.

Overly rigid rules can be enacted if no one truly knows what is safe and what
should be available for both internal and external collaboration. Therefore, the
technical aspect of data can play a secondary role to an overzealous culture.

Predictive and Advanced Analytics

Richard Jeffers, Founder and Managing Director, RS Industria

There were four recurring themes on our table. The first was around the
importance of starting with the problem at hand rather than falling in love with
the technology. It’s not about deploying data science for the sake of it.

Linked to that is the fact that you can do a lot with some relatively
unsophisticated tools – statistics can take you a long way before you need to
move into AI and machine learning. To solve any of these problems, you need to
bring together the people who understand data and the maths with the people who
understand the domain; you’ve got to have people who are embedded into the
problem, as well as people who are embedded into the technology.

The most important discussion point was around culture change because people
have to respond to the alerts and insight that come from the data and use that
to drive improvement activity in the business. The maths won’t actually deliver
any business value without cultural change.

Data Collection and Integration

Mike Kiersey, Head of EMEA Technology Organisation, Boomi

We touched on just about every aspect of data as part of our conversations, and
there was some great sharing of best practices and ideas, particularly around
keeping things simple, starting small and growing from there. Businesses can go
quite broad with data, very fast, but that won’t necessarily yield the desired
results. Much like the second keynote address, we also touched on culture and
data literacy.

Do people really understand why they’re making data-driven changes and who is
leading the way on that journey? Do we really know what and how much data we
need and how long we need to keep it? And how do we make it easy for people to
access, organise and use?

Data Leadership

Phil Ware, Digitalisation Manager, Edwards Vacuum

We looked at building a strong data-driven culture, implementing robust data
management practices and developing a strategic approach to leverage data to
achieve business objectives. I think the last one answers all the other
questions.

If you know where you’re going, you know what you’re looking for and you can
communicate that. That drives your data strategy all the way through the
business. Everyone understands what you’re trying to do and why you need the
data, and they want to find out what data you need.

Cross collaboration was a common theme; getting people who work in different
areas to understand data from different parts of the business, creating a data
flow. Knowing what to do with a piece of data and how it impacts the customer is
critical. Leadership was also highlighted; the senior team need to drive down.
If not, the shop floor can still drive back up. Get good information and feed
that upwards to drive culture change at the top.

Rob Clifford, Head of Digital and Data, BAE Systems

Being a data leader doesn’t mean you do it all yourself. It takes bravery,
intellectual curiosity, but doesn’t require you to be a data ninja. It’s more
important that you create the conditions for other people to succeed and give
them the cover that they need from senior teams if it doesn’t go the way it
should. That’s what the real leadership part is. And it may be cliché but those
are the leaders we all enjoy working with.

For more stories on Industrial Data and AI click here.

 * AI
 * DataScience
 * DataVisualisation
 * Digital Transformation




READ NEXT

Industrial Data & AI


MANUFACTURERS ARE TURNING TO AI-POWERED MACHINE VISION TO SUCCEED

 * AI
 * Automation
 * IndustrialRobots


READ NEXT

Industrial Data & AI


BEING FORCE CHOKED BY DARTH VADER IN THE NAME OF MANUFACTURING JOURNALISM

 * Automation
 * Digital Transformation
 * Innovation




SIGN UP TO RECEIVE THE BEST OF THE MANUFACTURER DIRECTLY TO YOUR INBOX.


 * 
 * 
 * 
 * 

 * About Us
 * Contact Us
 * Privacy Policy
 * Cookie Policy


© Hennik research 2023 All Rights Reserved
Web Design by