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Data Maturity in Manufacturing

Posted by Shammah Banerjee | 28-Jul-2021 12:30:07

Today’s manufacturing industry is awash with huge amounts of data that could offer significant insight into operations and strategy. However, many enterprises face challenges with data quality, accessibility, literacy and insight within their organisations. Research produced by Cognizant and MuleSoft in partnership with Nimbus Ninety highlighted some of the key areas for improvement for manufacturers, as well as outlining the steps needed to achieve data maturity.

Manufacturing leaders gathered online to hear the headline insights that came out of the research, and to discuss the path forward to data maturity.

THE STATE OF DATA MATURITY IN MANUFACTURING

The session was kicked off with an overview of the research from Ronnie Abraham, Head of Manufacturing, Transportation & Logistics at Cognizant UK & I. He began by asking the question, “how data-driven is the sector?” According to the research conducted, the industry average for manufacturing was 6.8 out of 10, with automotive demonstrating greater maturity at 9 out of 10.

Ronnie also explored the insights around which teams were traditionally most data-driven within manufacturing organisations: finance, procurement & logistics, engineering and quality were the teams topping that list, with sales & marketing, strategy & future business planning and design lagging amongst the least data-driven. With many of the individuals interviewed coming from B2B organisations, Ronnie concluded that more respondents from B2C may have seen the sales & marketing teams rise higher up the data-driven list, as data becomes more important in customer-facing organisations. This could be a substantial part of why the automotive industry ranked so high on the data-driven spectrum. But there are opportunities for the other sectors if they can move towards a more forward and outward-looking use of data. 

A point from Paul Gosling, CTO of Thales UK, gave the group some food for thought: “Today’s data challenge is like developing sonar systems in submarines, where you're looking for very small numbers of signals within lots of noise. The mass of data makes it critical to differentiate between the noise and the signal, and that’s the challenge many businesses face.”

Indeed, with some of the enterprise manufacturers being much older organisations, the legacy systems that currently hold data today were not designed with analytics or AI in mind. They’re very customised to specific needs: resulting in this muddling of sound between “noise” and “signal”. Ronnie suggested that this issue required a pan-enterprise - rather than a departmental - approach in order to move towards unified, quality data.

Another interviewee from the research challenged the industry to think about value: “Everyone says data is the oil of the 21st century, but what is the price per barrel?” The difficulty, Ronnie argued, is that the cost to derive each unit of insight changes per organisation and demonstrating ROI is even harder. Getting sponsorship from the board level for data strategy projects can become challenging, without this clear ROI. Interoperability of data across the supply chain is a huge point of contention for the industry as well. With a lack of common taxonomy to share data across a supply chain, data interoperability plummets and data silos rise. The next challenge for manufacturers is understanding how to ensure interoperability to build value for the industry across the supply chain. 

Ronnie then laid out the steps to start a value-realising data journey. Some of those key steps are as follows: 

  1. Define value. What does it mean from both a customer/employee perspective and a monetary standpoint?
  2. Start small. Starting with a smaller problem means that the issues are easier to resolve, and can then be scaled. 
  3. Audit data quality. Establishing data standards is vital to ensure that all data going into the data lake is of the same quality. 
  4. Adopt a data culture. Building a future-proof data culture means that the organisation can face future disruptions in a data-driven way. Use proof of concepts to gain momentum and business champions. 
  5. Prepare for automation. Prepare for future demands now and lay the foundations for automation. 

Download the Data Maturity in Manufacturing report to learn more about the recommendations to optimise data strategy and unlock hidden value. 

THE JOURNEY TO MATURITY

The group, made up of senior data and IT leaders in the engineering, automotive, construction, aerospace and defence industries, then went into a roundtable discussion to examine the major barriers to becoming data driven in the industry and the next steps to data maturity. The key issues that came up from attendees were: data ownership, asking the right questions, legacy systems, data quality and unity, and organisational culture. 

Data ownership is a critical problem across the industry with more and more organisations having data responsibility sitting across business users and IT teams. IT is an enabler of that data insight, but, as one attendee argued, sometimes it makes more sense for the data to sit with the business users who are closer to what the data means and what they’re trying to get out of it. One attendee argued that giving ownership back to the business functions can help ensure that governance and accountability are in place as well. On the flip side, the business users aren’t the IT experts, so bringing parts of the organisation around a single purpose where needed is crucial to progress in this area. 

Asking the right questions was a big theme throughout the conversation - and that is key at all stages of the process. When a data strategy or project is being established, it is vital to ensure that it is aligned with business OKRs. Ronnie commented that too many organisations he had worked with in the past had seen project failure due to lack of alignment with business objectives and strategy. When it comes to more granular decisions about what reports to produce and what insights to pursue, it is still crucial to ask the question, “what do I need and why?” One attendee argued that it goes back to understanding what you’re trying to achieve; another noted that it’s about choosing the right use case and not just randomly producing reports. 

The discussion around legacy systems was heavily linked to that around data quality and unity. For many organisations, the complexity of infrastructure and numerous different legacy systems that house the data create huge barriers for data and business teams alike. One attendee commented that his global organisation has multiple different PLMs and ERPs - and that’s just in the UK! 

With data spread so disparately across the organisation, the ability to do proper analytics is hindered as the data is both unstandardised and there are likely to be multiple data sets representing the same thing. This can result in a lack of clarity when it comes to delivering insight. The attendees responded to a poll on this topic, asking “does your organisation have a defined integration strategy and approach for consistent access to data within legacy systems?” Answers were encouragingly split across “we do not, but are in the process of defining this” (38%) and “we do” (38%), with only 23% saying “we do not”. The big question comes around how can organisations embark on a major transformation, vs. how can they get value now? 

Organisational culture was the final major part of the discussion. Skilling teams up and empowering them to self-serve and use the data that they have is a big part of pan-enterprise data maturity. As Ronnie outlined the different teams on varying stages of the data-driven journey, it’s important to bring all teams up to a similar standard to ensure that the organisation as a whole is functioning as a data-driven organism. Attendees also discussed the value of having a graduate programme that is focused on data management and analytics, in order to bring fresh thinking into the organisation in a structured way. 

With valuable pointers and lessons learnt shared from all attendees, the journey to data maturity for manufacturers is clear: the challenge now is to embark on it.

 

Download the Data Maturity in Manufacturing report here to gain access to insights from data leaders across the industry, and to understand the steps to data maturity.

Topics: Event reports

Written by Shammah Banerjee

Shammah is the Senior Editor at Nimbus Ninety. She tracks down the most exciting stories in business and tech, produces the content and gets to chat with the biggest innovators of the moment at Chief Disruptor LIVE.

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