Four challenges for implementing data strategy in manufacturing

The Manufacturer’s Dylan Cunningham urges manufacturers to not only take advantage of the huge opportunities provided by data technology, but also ensure they adopt a strategy for long-term data success.

Data has long been a bit of a buzzword in manufacturing. Our Annual Manufacturing Report 2020, which surveyed more than 350 manufacturers across the entire spectrum of UK industry, cut through the hype, highlighting that:

  • 89% of manufacturers agree that data from connected machines and people will improve their supply chain relationships
  • 91% believe that new data technologies will enable staff to work smarter and increase employee engagement
  • 87% expect data to accelerate innovation in design and development

And yet, manufacturing is largely lagging behind other industries in terms of data capability, while at the same time the early adopters are seeing massive gains in productivity and effectiveness.

Having engaged with several early adopters and those making the journey, a number of common challenges shone through.

1. Leap of faith

The first steps on the data journey are often long strides. To see real impact from data projects often requires a lot of information, and therefore a lot of time is spent gathering data before real insights can be deduced.

This need to invest, both financially and in terms of resources for a return that’s seen as far-off and uncertain, poses a real challenge, especially in an industry undergoing turbulence in many other areas.

This is partly due to the fact that ‘data’ is still viewed as a monolithic project that should be implemented all at once, and results should follow immediately. The truth, as always, is more nuanced. Collecting data can start small and simple, without expensive technology.

With some thought and planning, simply recording (robustly) the information already being generated goes a long way towards building a valuable dataset – without much up-front investment. Alternatively, pilot projects can allow you to focus on one area of the business as a proof-of-concept as well as a practice run.

2. Culture and change management

Data is often viewed as a sterile, emotionless, inhuman thing. It doesn’t take much imagination to see where that came from, but in reality, people are the biggest factor in implementing and getting value from data projects.

It’s critical that your organisation’s culture is taken into consideration when planning your data strategy. Are your people ready for data? If not, how can you get them ready?

If your workforce isn’t open to engaging with data, then any data initiatives will exist in a vacuum. With no one taking ownership of processes and projects, data quality will be poor, if it’s even recorded at all.

Further, data won’t be used to inform decisions if people don’t use it, trust it or engage with it, so the value is lost.

As in many other areas, effective change management is required. Communication of the intent and purpose of a data programme, and how it aligns to the company’s goals and values is a good start, and can make a huge difference to engagement and acceptance – while at the same time ensuring that management understand and agree on these things.

Providing adequate training and seeking and accepting feedback are other important aspects of the data change-management process.

3. Organisational coordination

A common source of frustration for data programme managers is the lack of effective utilisation of an organisation’s data capability.

Often, huge investments are made into complex (and brilliant) data gathering, and storage and reporting systems – but these systems exist in isolated siloes across an organisation. This leads to a duplication of efforts across different functions, as well as sinking a huge amount of time and energy into arguing over which version of the truth is correct.

The result? Expensive data programmes that look pretty but generate more trouble and friction than they do value. The worst aspect of this is that it’s held up as an example of why data ‘doesn’t work’, ‘isn’t worth it’ or ‘isn’t right for us’.

Organisation-wide data-management strategies are the key to effectively coordinating and focusing efforts. This means including stakeholders from all functions and levels, and deciding what data needs to be collected, planning out the collection strategy, and putting in place a communications structure to keep everyone’s efforts aligned.

This leads to lower costs and better results in a shorter timeframe – crucial to the success of any project.

4. Keeping it alive

Finally, one of the biggest ways to waste resources with a data project is to let it die shortly after spending so much time, money and effort getting it up and running. Unfortunately, this happens all too often, and it partially links back to the issues discussed above.

A lack of ownership and robustness, as well as poor communication, can lead to employees disengaging with data systems as soon as the ‘champion’ (or champions) driving the change move on to their next project.

The simplest solution is for the champions to stay on, always monitoring and enforcing the data processes in place. This, however, is clearly insufficient, as your data champions are needed elsewhere in the business.

Instead, designing robust processes and carefully ensuring the understanding and buy-in of everyone involved negates this problem. This is easier said than done, and requires commitment from senior management to provide both personal and resource support.

Summing it all up

Admittedly, data is difficult, but there are those in the industry who are doing it well and reaping the benefits. In fact, we’ve gathered a few of them, who will be sharing insights from their journeys at Industrial Data Summit Online – 30 April 2020.

In a highly interactive virtual learning experience, you’ll discover how to approach the challenges through a series of expert-led keynotes and digital discussion sessions with fellow business leaders.

Click here to register, and for more information