How to create a data-democratised organisation in the chemical industry

Most people in the chemicals and process manufacturing industry believe data democratisation is the same as data access.  

Here, we highlight the difference between these two concepts and how you can harness data democratisation to make data work for you and grow your business.  

What is data access?

Data access focuses on enabling people to obtain data from a source, such as a database or a data warehouse.  

It is a necessary but insufficient condition for data democratisation. 

What is data democratisation?

Data democratisation goes beyond providing access to data.  

It involves helping everybody in an organisation, irrespective of their technical know-how, to work with data comfortably, feel confident talking about it and, as a result, make data-informed decisions powered by data.

Why does data democratisation matter?

Accenture’s Closing the Data Value Gap report found that most firms still don’t realise the full potential of their data.  

Only 32% of business executives surveyed said they could create measurable value from data, while just 27% said their data and analytics projects produce actionable insights.  

The challenge for organisations in the digital age is not capturing data, but turning data into insights that empower employees to make more informed decisions, improve productivity, and drive competitive advantage.  

Having more people with diverse expertise and the ability to access data easily and quickly will enable your organisation to identify and take action on critical business insights.  

This cannot be accomplished by small groups of technologists and data scientists walled off in organisational silos.  

It requires a much more extensive and diverse approach, including frontline workers, such as production and logistics operators, maintenance and quality technicians, and their first and second-line managers. 

Eight steps to reaching data democratisation

The goal is to have anybody use data at any time to make decisions without barriers to access or understanding.  

That is a challenging task, but understanding the concept and having a step-by-step guide can help you navigate straightforwardly. Here’s how to do it.

1. Set data expectations

Define what you want your data to do. Think about how it will deliver tangible value for your organisation. Ask yourself how your people can improve existing operating models and remove waste.  

By outlining these clear and identifiable goals, leaders can start to define how different roles across the organisation must work with data to achieve them.  

Different groups will be accountable for creating value with data in different ways. Most employees within the organisation will be users who will need to review relevant data and act quickly.  

Production operators, for example, must access raw material and energy consumption data, quality, safety and environmental variables. They should also be able to relate them to many other process parameters in an easily consumable way. Understanding how these parameters connect and impact the results in real time creates excellent value.  

Maintenance technicians can access data from previous maintenance registered in maintenance management system (SAP, for example) in combination with production rates, OEE (overall equipment effectiveness) or plant shutdowns. This makes it easier to understand the correlation between the variables. With this information in their hands, they can perform their own improvement sessions, focusing, for example, on maintenance planning or execution procedures. 

Note that this can be obvious to a manufacturing manager or operations director but not to a frontline worker. From the moment they have access to the correct information in the right way, the decision-making population increases exponentially.

2. Break down data silos  

One of the main barriers to data democratisation is the siloed nature of data within an organisation. Organisations should break down these silos to democratise data and create a centralised data repository accessible to everyone. 

3. Invest in a fit-for-purpose tool 

A legacy of the traditional approach to data usage persists in many organisations: a small group of specialists benefitting from most of the investment in data tools and training, along with universal access to data.  

A slow and progressive build-up of tools and work processes is often redundant or provides conflicting data or work processes. Wasted time doing manual paper-pushing work can be eradicated.  

The appropriate tool must enable more employees to take advantage of data in their work, providing the data and solutions that deliver the right insights.  

Most general users need simple information presented in an easily consumable way that allows them to make better decisions.  

This will empower them to take actions that will improve productivity and deliver business value.

4. Experiment on a small scale: test, test, test

Chemical companies can consider different combinations of digital levers. A one-size-fits-all approach won’t work in today’s dynamic environment.  

A little experimentation with different digital designs – with varying emphasis on differentiation, collaboration or capturing more value – can yield unexpected, positive benefits in the long term.  

In this regard, chemical enterprises can test the solution locally before a global rollout and with key teams by involving them early in the development process.

5. Get support to solve issues

A technical team or service must be available, and the route to reach them must be straightforward and efficient.  

When workers try to gain experience and find difficulties using a tool, the proper response makes a big difference in the buy-in. So, having the needed support when problems occur is fundamental to maintaining sustainability in using data tools. 

A no-code platform gives autonomy to the most skilled workers of each team to perform needed configurations or help their teams themselves.  

This encourages the use of data and boosts accountability and engagement.

6. Train workers and make using data routine 

All workers must be able to read, interpret and communicate data effectively. It is essential to ensure that all employees, regardless of their role or technical know-how, are trained in data literacy.  

This will help them feel comfortable working with data and using it to make informed decisions. 

Another important topic is to set team champions or coaches within the different teams. These people are usually the ones that feel more confident working with data and digital tools.  

They will serve as references to help users discuss issues and help them to find solutions themselves, developing a culture of solving problems and searching for continuous improvement. Note that this is different from the support team.  

Everything that is made usual gets easier to do.  

To avoid overwhelming people, using the data should be accessible and routine. This way, it is essential to discuss with the team the best ways to use and make them part of the work processes. 

7. Encourage a data-driven culture

Once the workers are trained, leaders should encourage the use of data and refer to it.  

They can set the tone for the organisation by championing data and demonstrating how it can be used to drive better decision-making and business outcomes. By doing so, leaders can encourage others to adopt data-driven practices and make more informed decisions.  

Additionally, leaders can provide the necessary resources and support to enable the organisation to work with data effectively, such as providing access to data analytics tools and training employees on data literacy.

8. Foster collaboration across departments 

To democratise data, it’s essential to foster collaboration across departments. By sharing data and insights, everyone across the organisation can make more informed decisions. 

A chemical manufacturing site that has separate teams for production, maintenance, logistics and quality can easily collaborate and share data to improve overall operations: 

• Production, maintenance and quality teams could collaborate to identify trends in quality data that could be linked to specific equipment or maintenance issues. 
• Logistics, production and maintenance teams could collaborate to optimise production planning and scheduling to reduce production costs and maximise plant usage. 

Collaborating saves costs, increases efficiency and improves product quality, helping businesses to scale and boost revenue.

Summary

When discussing digital and data use in the process industry, many imagine advanced tools that predict and optimise processes. Indeed, such tools are important.  

But frontline workers need access to basic, day-to-day technology that helps them better understand their job. They need data and insights in an easily consumable way. 

For too long, data analytics has been the domain of high-level management. But this top-down approach creates a bottleneck, blocking your people from using valuable insights throughout your organisation, resulting in delays that can cost businesses millions in revenue.   

With efficiency and collaboration in mind, we created Cyzag Whiteboard. It makes its impact by unlocking the data and embedding the insights that shop floor workers and operations managers need in the right place at the right time.  

The correct data will translate into faster decision-making and a more agile business.  

Would you like to build a democratised organisation and learn how to make data work for you? Schedule a demo. 

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