March 2022

Trends and Resources

Business Trends: The big data tools and related professional competencies required in the chemical industry

Big data is making tremendous changes in various sectors of the economy, and chemical industries can now leverage these new advancements.

Khan, A., Manzoor, M., Saudi Aramco

Big data is making tremendous changes in various sectors of the economy, and chemical industries can now leverage these new advancements. Companies in the chemical industry are starting to realize how they can create additional value from the vast amounts of data they have, and how this data can be efficiently used to make more effective and strategic decisions. However, to leverage this data, there is a need for timely investments in big data projects, including investing in big data competency training and in tools for scalability.

Engineers must understand data science, and how to take command over coding and develop a strong quantitative aptitude. Various big data tools are available on the market that can make big data integration easy by providing a unified platform for the collection and arrangement of information extracted from big data. This article will focus on the competencies and tools necessary to make the investment for big data adoption. It will also review present and future big data adoption challenges, and highlight how entities (such as government, academia and industry) must collaboratively work together to overcome them.

What is big data?

Big data is a vast amount of data of a dissimilar nature that is produced through various sensors in the chemical industry, and this data requires a large volume of storage space and specialized tools for processing. Big data can enable engineers to enhance their analyzing capabilities by providing them with vast amounts of accurate and real-time information from many sources. This also requires visualization and storage tools that can provide them the facility to store their data, and to analyze and forecast the outcomes of their processes. Big data is especially helpful for engineers in the chemical industry, as their work is highly complex and deals with a huge amount of data beyond human capabilities. Engineers are continuously in need of a source that provides them with access to a high volume of data to perform necessary analyses. More data is generated following these analyses; hence, it is important for engineers to store this data to keep a record of simulations and modeling for further optimization and forecasting.

Big data in the chemical industry

The chemical industry requires hardware that can process complex algorithms for multivariate analyses to support decision making. The industry also needs to invest in big data tools and related professional competencies. In the past, traditional engineering did not include such skills and advanced technologies as part of a higher-education curriculum. This is now changing, as these competencies have become more relevant in the modern big data era.

Traditionally, engineers manually make the correlations, identify the relationships in variables, and design hypotheses for limited variables. The challenge is to shift engineers from these conventional methods to the big data era so that all analyses can be done through automated systems with higher accuracies for multivariate analysis.

In the past, it was difficult for the chemical industry to successfully implement the latest technologies in its operations because of the complexity of the domain and the additional prerequisites for the necessary technology integration. However, big data is different from conventional advancements because of the increased awareness and available tools in the market. Through its industry-wide adoption and integration, big data can increase the competitive capabilities of the chemical industry.

Big data scalability

Scalability in the chemical industry is the ability of available systems to grow in response to changing demands for big data adoption. For example, this may include computing and networking, in addition to human resource requirements.

Big data is easily scalable, which allows companies in the chemical industry to adjust the data according to their changing demands. This means that big data contains a large volume of data that is reliable and verified through various platforms that have already been implemented. This includes data from various systems, such as process and management information systems.

Data may contain different versions, including text and video content, in addition to several forms of numerical data from sensors. Obviously, engineers cannot use all this data, as they rely on accurate data that is relevant to their field and processes. If scalability is not considered, then the outcome of the company’s big data utilization, along with the benefits that it can provide, could be diverted from the engineers’ expectations.

For the successful utilization of big data in industry, it is necessary for engineers to have strong analytical tools. Quality will suffer if the scalability is not correctly adjusted. This can raise questions on the competitive capability and on the overall stakeholder satisfaction for these tools, and can risk their profitability. Scalability is not as simple as it appears, and requires careful consideration of the following factors:

  • Random access memory (RAM) status. This is the most critical component often ignored by companies in the adaptation of big data. It is linked with the capacity of the servers and computers to accept and continue the data communication. For the processing of analytics used by chemical engineers, it is necessary for engineers to have servers that do not disrupt the information flow of the application. Companies can easily upgrade RAM memory to reduce challenges.
  • Disk storage capacity. Disk storage space is also an important factor and is required for analytics by big data tools. Systems must have sufficient disk storage to both install and operate the required big data tools without disrupting operations. Big data tools use algorithms that require a lot of disk space to run many iterations and models to arrive at the optimum solutions.
  • Central processing unit (CPU) consumption. Big data tools can be CPU processor hungry, especially when running several solutions simultaneously, which can slow down processing. There can be times when the highest CPU usage is required for several solutions, thus impacting other processes that require CPU usage. Therefore, in the chemical industry, it is important for big data tools to be applied to systems with sufficient CPU processing power.

Scalability can be beneficial, but it is equally important to understand the necessity for it. Despite its significance, each company has a specific situation and need for scalability. Scaling is required when the company is facing problems of downtime and slow performance. Scaling is a better choice (rather than allowing the problem to become a major hurdle) for adapting new technologies related to big data. High latency can be the signal for the right time for scalability. There are scaling-up systems that can be adjusted based on criteria required for data.

Scalability is not only limited to adjusting the volume of information by setting a criterion, but it also includes the initiatives related to big data, such as big data analytics and infrastructure. All the elements and factors related to big data are needed for the system to be scalable, so they can easily reach the desired target without wasting resources. The big data tools available in the market enable companies to scale the infrastructure in both horizontal and vertical directions. Adopting big data tools and replacing conventional methods may enable a facility to scale its existing analytical tools.

Big data tools enable a systematic approach for engineers to work together. There will be a point when the need to scale the number of workers will be felt. Here, scalability will help to scale human resource requirements efficiently and effectively. Investing time in, and paying attention to, the scalability of the analytics team to determine the team size that is capable of meeting demands can provide long-term benefits to companies in the chemical industry.

Cloud computing platform

The major reason for big data technology evaluation is to enable engineers to solve complex problems. They are expected to consistently obtain reliable, repeatable, highly accurate and precise solutions for complex problems, which requires a high degree of computational power.

Investing in a cloud-based computing platform can help address these challenges. This type of platform will contain a collection of servers that are able to support big data analytics. Cloud computing is a powerful technology that provides remote access to the database and analytics through the internet. It is beneficial because it solves the problem of data storage and CPU limitations when utilizing big data tools. This is especially true for engineers that will use big data tools for complex problem-solving algorithms that require higher computational power.

Another benefit of utilizing a cloud platform is cost savings. This is because the service provider will be liable for the security and maintenance of the platform. Users only have to pay for the service provided by the company. The cloud service provider also ensures the availability of virtual resources. There is no need to purchase, maintain, expand or replace hardware when a cloud-based service can be purchased on a pay-as-you-go basis.

The mining algorithms are easily processed by virtual resources. Also, many vendors and contractors can be given access to the cloud for real-time feedback. Normally, computer systems available to engineers are insufficient to process the amount of data and to manipulate the size of algorithms. Therefore, there is a need for cloud-computing technologies that have easily scalable architecture specifically designed for big data. Such systems that can meet these industry requirements are still in development.

Data specialization

In the complex chemical industry, the role of data scientists and big data specialists cannot be ignored. They play an important part in the successful integration of big data in the chemical industry. The data scientist can understand paradigms of information processing and can shift the conventional model to a big data-driven approach.

Another critical role for data scientists is data mining. Data scientists can assist in collecting data and extracting the required logic from it. Data collection, analysis, interpretation, management and arrangement are equally important. It is mandatory for data scientists to convert the data into synchronized patterns, so that the data has a visible trend and can prove a hypothesis.

The chemical industry cannot deny the importance of big data specialists. A big data specialist is a data scientist with domain knowledge and experience specific to the chemical industry. There can be situations when data scientists have specialized knowledge of big data and the know-how about its integration. However, this is not always the situation. A big data specialist is needed for interrogating, understanding and handling large groups of data. There is a significant difference between the job of a data scientist and a big data specialist. The nature of a data scientist is to understand the given data and to use it in a favorable form, while the big data specialist has chemical industry domain knowledge and is familiar with the pattern of big data, making it available for the data scientist. Therefore, it is necessary for companies to invest in both big data specialists and data scientists to successfully integrate big data in chemical engineering.

The best way to overcome these challenges is to scale chemical engineers as data scientists and big data specialists through university courses and/or professional training.

Big data professional competency

Despite the latest big data tools and availability, the chemical industry cannot deny the need for workforce development for the successful integration of big data. Having traditional knowledge and using conventional tools are not enough for the successful utilization of big data. Integrating big data requires a specialized approach, an understanding of programming languages and advanced analytic skills. It is very important for engineers to make the right decisions at the right time, so they must have the right skillsets to accomplish this. The following are some workforce developments required in the chemical industry:

  • Programming language. It is not sufficient to hire chemical engineers and simply ask them to integrate big data into their operations. It is necessary to invest in programming and development areas. There is a need for big data developers who can understand the function of big data technologies, and to use their knowledge to address chemical engineering issues. Necessary programming language skills include Apache Spark, Hadoop with Python and Java.

When engineers have an in-depth competency of programming, they can easily divert it into a wider scope demanded by big data. Having strong programming language and domain competencies can save the industry from the problems created by an enormous volume of big data. While these challenges are difficult to tackle with the restricted amount of funds available to the industry, investments in programming languages are needed. For the implementation of big data in the chemical industry, engineers must integrate big data tools into their legacy software. Normally, the respective programming language provides a library facility that makes it easier for engineers to perform their required big data analytics.

  • Quantitative aptitude and statistics. The competency of quantitative aptitude is necessary to understand big data and interpret it in the desired situation. It is necessary for the company to invest in the quantitative aptitude of the people handling big data. In addition to having the required technology, it is equally important to have a command over linear algebra and statistics. Big data analytics is heavily dependent on statistics, and this demand can increase from simple to complex statistical methods, depending on the project. This statistics literacy is necessary because the algorithms used to make the programming language are based on statistical rules and fundamentals. Therefore, having a command over statistics, other than just domain knowledge, is equally important for the integration of big data.

Companies in the chemical industry often create teams of chemical engineers and statisticians to meet these needs, but these two jobs can be done by just one person to reduce the cost of big data integration. For big data integration, data scientists, statisticians and chemical engineers are working together to avoid security problems in the long run. Today, chemical engineers need to have data science application skills, which are not presently taught in universities, and companies can provide these skills through a comprehensive employee education program.

  • Machine learning (ML). For chemical engineers, the need to invest in programming cannot be mixed with the significance of ML—and its subset known as deep learning. Most companies outsource the job of ML. However, the integration and application of big data becomes easy and effective when a company has integrated ML techniques and chemical engineering competencies. The complex multivariate diagnostic and prediction problems that chemical engineers face is simplified by ML, which includes models such as regression, decision trees and neural networks. Chemical engineers provide timely, accurate and precise solutions to maximize profit. ML techniques enhance this by making the path to achieving the required solution more efficient. If engineers have ML knowledge, which is a part of data science, then they can perform operations at a safe level and, consequently, maximize profit values.
  • Process monitoring. Chemical engineering processes are sometimes complex and long, requiring a full-time person to monitor and observe them. This task can be done with big data tools. Chemical engineering demands predictive, descriptive, diagnostic and prescriptive activities when monitoring these processes. When companies invest in the skill of data science for their chemical engineers, these engineers will be able to provide exploratory data analysis (EDA), and this analysis will revolve around making conclusions and extracting valuable insights from the data. It will automatically cover statistical literacy. Engineers will also be capable of using descriptive and visualization statistics as a tool for big data analytics. EDA will help companies identify solutions that will have the biggest contributions to profitability.


For the successful integration of big data, it is necessary to have the right big data scalability and professional competencies. By making investments in big data scalability factors and acquiring big data competencies, engineers working in the chemical industry will be able to capitalize on the enormous amount of data and to utilize this data for solution development.

The chemical industry needs tools that can collect the desired information in a single platform from multiple sources and provide a facility to arrange it. After having the desired skill set and architecture, the chemical industry must invest in big data tools that meet industry requirements. The chemical industry should drive the switch from conventional analytical methods to big data-driven approaches. HP

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