How Data Analytics Can Help the Industrial Sector


The lifespan of companies is decreasing and it will continue to decrease in upcoming years. Phenomena such as digital transformation and data analytics are becoming more important to reverse this trend.

company lifespan

There are countless sectors that can benefit from digital transformation and from the use of data analytics. The industrial sector, especially the manufacturing industry, is one of the top 10 sectors using data analytics to increase its competitiveness.

This sector has several specificities when it comes to its relationship with data analytics. To benefit from the use of analytics, it is first necessary to understand the specificities of data analytics in the industrial sector – industrial data analytics.

Industrial data analytics and its relationship with digital transformation in the industrial sector

The emergence of phenomena such as the Internet of Things (IoT) and Industry 4.0 created the need to adapt the methods and concepts previously in place. In this context, industrial big data emerges – defined as the large amount of information collected by industrial equipment.

The technological development in the industry standardised the use of sensors integrated into machines that collect data generated during the production process.

Experts believe that industrial big data emerged as a result of the adoption of the industrial Internet of Things in production processes.

Data, characterised by their complexity and diversity, are collected on a large scale, as almost all factory devices with internet access are permanently collecting information. Although this data is crucial for companies, so far, we are only talking about the collection of it – without any processing or direct use – industrial big data.

This vast amount of information becomes more relevant, the more efficient and assertive its collection, analysis and interpretation. This complete and complex process is known as industrial data analytics.

The importance of this is increasing, and it is estimated that in 2026, this market will be valued at 36.73 billion dollars (compared to 13.6 billion in 2020). This increase is associated with the accelerated digital transformation of the industrial network and the unequivocal advantage that the ability to interpret and read data has in the implementation of the growth and evolution strategies of organisations.

Benefits of implementing data analytics in the industrial sector

By approaching data transversally, from the past to the future, it becomes possible to develop advanced statistical models that allow the discovery of increasingly specific and personalised business insights and foresee the most plausible situations in the future. With this type of solutions, it will be possible to:

  • Improve performance
  • Increase monitoring
  • Make faster and more efficient decisions
  • Control costs
  • Improve customer service

In the Industrial sector, this skill incorporates the advantages of data analysis with the specific advantages of an industrial environment. These include:

Balance between automated and manual work

The use of automation in manufacturing is becoming more mainstream. The global manufacturing automation market is expected to grow 8.8% annually by 2025. Although automation represents possible efficiency gains, such as higher productivity and consistency in quality, there are certain roles that require manual human labour.

Industrial data analytics allows you to study these factors to strike a balance between manual and automated work. Additionally, the analysis of the work teams allows to size teams and monitor financial indicators during the implementation of automated processes.

Process optimisation

One of the major advantages of data analytics is the optimisation of processes and the ability to increase the operations’ productivity.

In equipment, this can be achieved by implementing prescriptive maintenance models that maximise the lifespan of all components.

The implementation of data analytics enables the automatic creation of production plans, usually associated with a complex process with multi-variables, in which the result is the best solution. This will allow the reduction of the time required to build a plan and the analysis of multiple flexible scenarios – thus obtaining an optimised production plan.

Lastly, regarding anomalies, the use of data analytics allows inconsistencies in equipment and production facilities to be identified. Mathematical algorithms find patterns that allow processes to be refined. For example, through analytical tools, it is possible to identify whether a machine is efficient if it works during shorter intervals. These types of insights are essential for lean process improvement and optimisation.

Energy efficiency

The industrial sector is the world’s biggest energy user – consuming around 54% of the world’s total delivered energy. Energy represents an overwhelming cost for industrial organisations. Given that there are governments that reduce taxes for less polluting companies, one of the ways used to reduce these costs is to adapt energy consumption or make it more efficient. Greater energy efficiency can increase competitiveness and improve productivity – a huge advantage for companies.

The use of data analytics in energy efficiency allows us to predict the energy market and consequently improve energy decision-making. This becomes especially relevant as increasing environmental concerns may create a trend towards investment in renewable energies. It is estimated that by 2030, renewable energies could account for up to 27% of global energy consumption. Industrial analytics anticipates these changes so managers can adapt their strategy and increase investment in energies with future relevance.

Types of data analytics

The traditional approach to analytics is to study the data history. Nowadays, this analysis is not enough, as analysing the present is also important to understand what’s happening right now.

On the other hand, supporting the prediction of future events, suggesting what should be done or saying what is most likely to happen, is increasingly relevant for value creation and building a sustainable and resilient value chain. There are four types of data analytics with the following characteristics:

Descriptive analytics

Descriptive analytics analyses past data. Historical data are collected, organised and analysed with the goal of answering the question ‘what happened?’ To do this, tools supported by phenomena such as business intelligence and big data can be used. It is the most common analytical method, but, when observed individually, it is not able to justify the observed phenomena.

Diagnostic analytics

Diagnostic analytics is more in-depth than descriptive analytics. This uses exploratory data analysis techniques to answer the question ‘why did it happen?’It is seen as the basis of causal analysis as it aims to find correlations between data. When applied in the industrial sector, you may find a relationship between, for example, the temperature of a factory, and the speed at which a machine operates. This is possible due to the use of processes such as data mining and machine learning.

Predictive analytics

Predictive analytics uses historical data, and, with the help of algorithms, statistical methods and machine learning analyses patterns to answer the question ‘What will happen?’ Predictive analytics continues descriptive and diagnostic analytics because in order to be able to predict what might happen, it is first necessary to understand what happened and why it happened. This type of statistical analysis is useful for a range of sectors such as marketing, finance, industry and retail.

Prescriptive analytics

Prescriptive analytics is the most advanced type of analytics and aims to answer the question ‘What should we do?’ This type of analysis uses heuristics, simulations and artificial intelligence to develop multiple hypothetical scenarios, present the possible decisions and their implications. This is the analytical model that most supports decision making and, consequently, has the greatest impact on the actions of managers.

How to implement data analytics practices in the Industrial sector

Implementing a data analytics strategy in an industrial environment comes with challenges. The processes and departments make it complex to link and coordinate the various people and technologies in an organisation. It is necessary to consider best practices for implementing a data analytics strategy and to take into account the specificities of an industrial analytics strategy:

    • Map the material flow to better understand the business
    • Analyse available data collection resources
    • Verify which IoT devices are available, which machines have data collection systems, identify employees with qualifications to work with data analytics, and what the budget is.
    • Select the improvement points to address

In a manufacturing environment, there are processes that are more relevant when it comes to understand production and make forecasts. For example, in a food packaging company, the machine responsible for sealing the packages is essential – both for the production process and for counting the units of finished product.

If this machine does not collect quality information, it will never be possible to predict breakdowns or fluctuations in production time. It is essential to select which points of the production process could benefit most from the application of data analytics.

    • Map the information flow from the Gemba to the different information systems in use

To forecast, it is necessary to have access to the data history. This mapping allows you to check the data collected and learn how accessible it is.

    • Implement new data collection methods or update the methods currently used

Current data collection devices may be insufficient to correctly represent the industrial environment. It is necessary to understand at which stages of the production process there are gaps in data collection and put in place correction mechanisms. Then, new historical data relevant for future forecasts will be generated.

    • Organise and transform relevant data into a database ready for analysis

Data collection does not ensure the ability to use data for decision-making or process optimisation. It is necessary to select which data are relevant for analysis and organise them. For example, if a factory has significantly changed its production process, the data collected before that change may not be relevant for analysis.

  • Use the transformed database to build models and evaluate the return on investment of the different viable solutions
  • Operationalise the selected model in the production process

To fully achieve the benefits of analytics and implement new tools and processes, change management is key. It is necessary to set the organisation up for success by putting people at the centre, involving the different end users from the initial mapping phase, and phasing implementation into pilot teams for testing and improvement.

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