Category Archives: ESG

ESG

In the face of climate change, growing social awareness and the need for ethical governance, there is an emerging need to set new standards for how companies operate. ESG is the actions implemented by a company through the lens of its environmental (E), social (S) and corporate governance (G) impacts. The aim of this initiative is to promote sustainability and social responsibility in the wider business community. In line with this, companies seek to strike a balance between generating profits and caring for the environment. The obligation for companies to report their ESG activities will be gradually extended, depending on the size of the entity and the specifics of its operations. Starting in 2024, the obligation will cover companies with more than 250 employees and by the end of 2027, it will also cover small and medium-sized enterprises with more than 10 employees. The purpose of this article is to show that the importance of sustainability continues to grow and ESG issues are becoming a key area of focus in business [1].

ESG indicators 

The three ESG areas mentioned above—namely environment, society and corporate governance—are an integral element necessary to be taken into account by companies that care about their image as socially responsible organisations. Effective management of each of these areas, through companies taking specific actions related to them, is key to achieving this goal. A fundamental action to be taken is monitoring, which allows awareness of the intensity of the impact exerted in each area. 

  1. Environment 

To effectively monitor the impact on nature, consideration should be given to areas such as: 

  • greenhouse gas emissions, 
  • energy consumption, 
  • carbon footprint, 
  • hazardous waste production, 
  • emissions (such as substances or noise) to the environment, 
  • emissions to the aquatic environment. 
  1. Society 

In order to effectively monitor relations with employees, customers, investors and the local community, it is important to consider areas such as: 

  • supporting diversity, 
  • minimising disparities, 
  • ensuring work-life balance, 
  • respecting employee rights, 
  • ensuring employee safety. 
  1. Corporate governance 

In order to effectively monitor how the management board operates, the following areas should be taken into account: 

  • fiscal transparency, 
  • countering corruption, 
  • structure of the management board, 
  • remuneration for the management board and employees, 
  • respect for shareholder rights [2]. 
Fig. 1. Graphic showing ESG indicators
Source: https://www.iberdrola.com/about-us/esg-responsible-management

Impact of ESG on companies’ operations

ESG issues have a significant impact on the actions taken by companies and their strategies. Operating a sustainable and conscious business is now a necessity in order to maintain a leading position in the market. Implementing an ESG strategy brings with it a number of valuable values for a company, as outlined below. 

  • Increased customer loyalty 

Companies that actively engage with environmental, social and corporate issues build a bond of trust with their customers. Customer loyalty increases as consumers are more likely to support companies that take action for social and environmental good. 

  • Improving the image 

ESG-compliant actions build a positive corporate image in local communities, among customers, investors and business partners. A company that cares about the environment, supports local communities and applies high ethical standards is seen as a responsible actor and a reliable partner that cares equally about social and environmental well-being. 

  • Stable market position 

Companies that effectively implement ESG strategies can enjoy a more stable position in the market. By integrating environmental, social and corporate factors into their operations, the company minimises the risk of reputational crises, which translates into operational stability and long-term growth [3]. 

Innovative Technologies and the Achievement of Sustainable Development Goals 

Technologies such as Artificial Intelligence, Big Data and Blockchain are effective tools for monitoring and understanding an organisation’s social and environmental impact. Artificial Intelligence, used in data analysis, enables the identification of consumers’ needs, which allows companies to understand them better and plan in advance the necessary actions to be taken. Big Data analytics, on the other hand, makes it possible to process and analyse extensive data sets, enabling more “targeted” business decisions based on the non-obvious information contained in this data. Blockchain technology, on the other hand, ensures the security and immutability of data, which is key to ensuring transparency in business processes. By using these technologies, companies can develop effective sustainability strategies, taking advantage of the opportunity to digitise and automate business processes. As a result, companies can create business models that not only generate profits but are also socially responsible and environmentally friendly. Moving towards the use of advanced technologies in the area of ESG is becoming not only a trend, but also a necessity for companies wishing to be leaders in sustainability [3, 4]. 

ESG at BFirst.Tech 

BFirst.Tech considers sustainability one of the most essential elements of the company’s strategy. With many innovative, proprietary and environmentally friendly products, we are able to meet our customers’ needs. For the second decade BFirst.Tech has been setting the standard for solutions in reducing noise pollution in working environments and urban agglomerations, generating, aggregating and providing management information (including data for non-financial ESG reporting) and monitoring and analysing information on the state of the industrial infrastructure of companies.  As we are aware of the climate changes taking place, it is environmental activities that are particularly important to us, which is why we focus on them when building the company’s strategy.  

Summary 

ESG is a key element in building the long-term value of companies in contemporary business. Implementing an effective ESG strategy allows companies to positively influence environmental protection, stakeholder relations and governance within the organisation. It also carries a number of precious values for the company, such as a more lasting relationship with customers, an increase in the company’s reputation, or a strengthening of its position in the industry. Thus, by implementing ESG, a company can become an indispensable part of the environment positively affecting the quality of life of the community. 

References

[1] About ESG—Polish ESG Association 

[2] ESG co to jest? Kogo dotyczy i jaki ma wpływ na przedsiębiorstwo? (ESG—what is it? Who does it apply to and what impact does it have on a company?) (sterrn.pl) 

[3] Zrozumieć ESG: Definicja, Znaczenie i Wpływ na Biznes (Understanding ESG: Definition, Importance and Impact on Business) (boringowl.io) 

[4] ESG, Blockchain, and AI – Oh My! | Barnes & Thornburg (btlaw.com) 

Application of Machine Learning in Data Lakes

In the digital age, there is a growing need for advanced technologies. It means not only for collecting but especially for analysing data. Companies are accumulating increasing amounts of different information that can improve their efficiency and innovation. Data Engineering offered by BFirst.Tech can play a key role in the process of using data for the benefit of a company. This is an area of sustainable products for effective information management and processing. The article presents one of the opportunities offered by the Data Engineering area. For example the integration of Machine Learning with Data Lakes. 

Data Engineering – an area of ​​sustainable products dedicated to collecting, analysing and aggregating data 

Data engineering is a process of designing and implementing systems for the effective collection, storage and processing of large sets of data. This supports the accumulation of information such as website traffic analysis, data from IoT sensors or consumer purchasing trends. Firstly, the task of data engineering is to ensure that information is skillfully collected. What is more, it is stored but also easily accessible and ready for analysis. Data can be effectively stored in Lakes, Data Storages and Data Warehouses. Such integrated data sources can be used to create analyses or feed artificial intelligence engines, which ensures comprehensive use of the collected information (see the detailed description of the Data Engineering area (img 1)). 

Data Engineering

img 1 – Data Engineering

Data lakes used for storing sets of information    

Data lakes enable storing a huge amount of raw data in its original, unprocessed format. Thanks to the possibilities offered by Data Engineering, data lakes are capable of accepting and integrating data from a wide variety of sources. For instance, text documents, images, IoT sensor data. It makes it possible to analyse and utilise complex sets of information in one place. The flexibility of data lakes and their ability to integrate diverse types of data make them extremely valuable to organisations facing the challenge of managing and analysing dynamically changing data sets. Unlike Data Warehouses, Data Lakes offer greater versatility in handling a variety of data types, made possible by advanced data processing and management techniques used in Data Engineering. However, that versatility also raises challenges in the area of storing and managing such complex sets of data. It requires data engineers to constantly adapt and implement innovative approaches.[1, 2] 

Information processing in data lakes and the application of machine learning   

The increasing volume of stored data and its diversity pose a challenge in the area of effective processing and analysis. Traditional methods are often unable to keep up with the growing complexity. What is more, they lead to delays and limitations in accessing key information. Machine Learning, supported by innovations in Data Engineering, can significantly improve those processes. Using extensive data sets, Machine Learning algorithms identify patterns, predict outcomes and automate decisions. Thanks to the integration with Data Lakes (img 2), they can work with a variety of data types. That is to say, structured to unstructured, enabling more complex analyses. Such comprehensiveness enables a more thorough understanding and use of data that would be inaccessible in traditional systems.

Applying Machine Learning to Data Lakes enables deeper analysis and more efficient processing. It facilitates the process by advanced Data Engineering tools and strategies. This enables organisations to transform great amounts of raw data into useful and valuable information. That is important for increasing their operational and strategic efficiency. Moreover, the use of Machine Learning supports the interpretation of collected data and contributes to more informed business decision-making. As a result, companies can adapt to market demands more dynamically, and create data-driven strategies in an innovative way. 

Data Lake

img 2 – Data Lakes

Fundamentals of Machine Learning, key techniques and their application  

In this paragraph, let’s discuss Machine Learning. as an integral part of the so-called artificial intelligence. It enables information systems to learn and develop based on data. Different types of learning are distinguished in that field: Supervised Learning, Unsupervised Learning and Reinforcement Learning. In Supervised Learning, each type of data is assigned a label or score that allows machines to learn. For example, to recognise patterns and create forecasts. That type of learning is used in image classification or financial forecasting, inter alia. In turn, Unsupervised Learning, in the case of which unlabeled data is used, focuses on finding hidden patterns and is useful in tasks such as grouping elements or detecting anomalies. Reinforcement Learning is based on a system of rewards and punishments. It helps machines to optimise their actions under dynamically changing conditions, e.g. games or automation. [3]

In terms of algorithms, neural networks are excellent for recognising patterns in complex data, such as images or sound. It also forms the basis of many advanced AI systems. Decision trees are used for classification and predictive analysis, for example in recommendation systems or sales forecasting. Each of those algorithms has unique applications and can be tailored to the specific needs of a task or problem. As a result, it makes Machine Learning a versatile tool in the world of data. 

Examples of applications of Machine Learning 

The application of Machine Learning to Data Lakes opens up a wide spectrum of possibilities. We can enumerate from anomaly detection, through personalisation of offers, to optimisation of supply chains. In the financial sector, such algorithms effectively analyse transaction patterns and identify anomalies or potential fraud in real time. That is crucial in preventing financial fraud. In retail and marketing, Machine Learning enables the personalisation of offers to customers. It happens by analysing purchase behaviour and preferences, increasing customer satisfaction and sales efficiency. [4] In industry, the algorithms contribute to the optimisation of supply chains by analysing data from various sources – as weather forecasts or market trends. It helps predicting demand and manage inventory and logistics [5].

They can also be used for pre-design or product optimisation. Another interesting application of Machine Learning in Data Lakes is image analysis. Machine Learning algorithms are able to process and analyse large sets of images and pictures. They are used in fields such as medical diagnostics, where they can help detect and classify lesions in radiological images, or in security systems, where camera image analysis can be used to identify and track objects or people.  

 CONCLUSIONS  

The article emphasises developments in the field of data analytics, highlighting how Machine Learning, Data Lakes and data engineering influence the way organisations process and use information. Introducing such technologies into business improves existing processes and opens the way to new opportunities. The Data Engineering area introduces modernisation into information processing, characterised by greater precision, deeper conclusions and faster decision-making.  That progress emphasises the growing value of Data Engineering in the modern business world, which is an important factor in adapting to dynamic market changes and creating data-driven strategies. 

References 

[1] https://bfirst.tech/data-engineering/ 

[2] https://www.netsuite.com/portal/resource/articles/data-warehouse/data-lake.shtml 

[3] https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained 

[4] https://www.tableau.com/learn/articles/machine-learning-examples 

[5]https://neptune.ai/blog/use-cases-algorithms-tools-and-example-implementations-of-machine-learning-in-supply-chain