Building a data-driven organization as the key to improving data quality and the quality of artificial intelligence systems.

Good morning, Pawe?. First of all, thank you for accepting our invitation and agreeing to participate in our podcast.

Pawe? Lubiński:

Good morning. Hello to all the esteemed listeners. Thank you, Olga, for the invitation. I hope I can handle the barrage of questions and provide some valuable insights.

In today's world, where data is becoming an increasingly fundamental asset for businesses, ensuring its highest quality is not only desirable but essential for the effectiveness and efficiency of AI systems. Over the past few years, AI has evolved from a futuristic vision to a daily business reality. From personalizing customer experiences to optimizing internal processes, AI is already everywhere. However, behind every success lies an enormous amount of data that must not only be accessible but also accurate, complete, and consistent. Pawe?, tell us why data quality is crucial for the effectiveness of AI systems?

Data quality is absolutely crucial for the effectiveness of AI systems because it forms the foundation on which these systems are built. In today's era, where data is becoming the new currency, its quality determines whether AI systems can perform their tasks with precision and efficiency or generate errors and chaos within organizations. AI systems, such as machine learning models or deep learning networks, rely on large datasets for training and improving their models.

These data must not only be abundant but also accurate, complete, and consistent—that’s the definition of quality data. Any error, inconsistency, or missing data can lead to incorrect conclusions and decisions made by AI systems. Imagine an AI system tasked with predicting customer behavior in an online store. If the data it learns from contains errors or is incomplete, the system may generate predictions that are far from reality. This will not only affect business performance but could also lead to a loss of customer trust and, of course, damage to the company’s reputation. Moreover, high-quality data allows AI systems to better understand the context and subtle meaning of the information. For instance, in natural language processing applications, high-quality data enables AI systems to better grasp the nuances of language and culture, which is crucial for communication with humans. In a world where data is becoming increasingly complex and diverse, ensuring the highest quality of data is a challenge but also a great opportunity. Businesses that can effectively manage data and use it to build advanced AI systems will gain a competitive advantage. In conclusion, investing in data quality is not just a technical matter; it is now a strategic business decision. By ensuring the highest quality data, organizations can build AI systems that not only meet their expectations but also bring real business value and support long-term success.

From personalizing customer experiences to optimizing internal processes, AI is already everywhere. However, behind every success lies an enormous amount of data that must not only be accessible but also accurate, complete, and consistent. Pawe?, tell us why data quality is crucial for the effectiveness of AI systems? Indeed, data today serves as the foundation for all analyses and forecasts, and its quality determines whether AI systems will generate useful insights or lead us to incorrect conclusions. I would now like to move on to another important issue in data management. What are the main sources of data typically acquired by organizations?

In organizations, the main sources of data can come from both internal and external sources. Internal sources include data generated by the organization itself, such as transactional data, customer data, production data, and data from enterprise management systems. These data are directly related to the business activities of the organization and form the basis for many strategic decisions. On the other hand, external data sources can come from a variety of places, including government organizations, educational institutions, or commercial data providers, such as demographic data from a national census or statistical data from government statistical offices. Market research data conducted by research firms can also be highly valuable for organizations to better understand the market and make strategic decisions. In the digital era, data can also be collected from the internet using forms and online surveys.

This allows organizations to collect data very efficiently and quickly. All these sources, both internal and external, are key to building a data-driven organization and form the foundation of all decision-making processes. Companies can look for good data sets in various places. As I mentioned, these include internal IT systems, such as ERP and CRM systems, internal sources of customer data, transaction information, product details, and business operations. We have government databases, where we can obtain demographic, statistical, and other public data, for instance, from national statistical offices. Universities and other educational institutions often conduct research and gather data that can be useful for businesses. Then, of course, there are commercial data providers—companies specializing in collecting and selling data, which can provide valuable information about markets or customers for a price. The internet and social media are also significant sources.From social media, we can extract data that provides insights into the behaviors and preferences of our customers. We can pull market research from studies that allow us to use data from research firms, delivering detailed reports on topics of interest for our future business decisions. Additionally, businesses can benefit from partnerships, especially strategic ones, where data from other organizations can serve as a valuable resource. A good example here would be research and development projects. By leveraging these diverse sources, organizations can compile a comprehensive set of data that helps them make strategic decisions and build a competitive edge in the market.

I agree with you. Both internal and external data sources play a crucial role in building a competitive advantage. Today's technological advancements allow for increasingly efficient data collection from the internet and social media, offering new possibilities to analyze customer behavior and preferences. Let’s move on to the next topic, which will allow us to look at data from a more structured perspective. There’s a lot of talk about different types of data, but how would you most simply categorize and describe them?

This categorization is fairly straightforward. Structured and unstructured data are the two primary types of data, differentiated by their organization and storage format. Structured data is organized in a specific way, typically in tables, which makes it easier to store and process. Examples of structured data include relational databases, like those used in database management systems, where data is stored in rows and columns. Due to their organized nature, structured data is easy to analyze using traditional analytical tools and query languages such as SQL. On the other hand, unstructured data lacks a defined structure and can come in various forms, such as text, images, videos, emails, or social media posts. Because of its diversity and lack of a uniform structure, unstructured data is harder to analyze and requires advanced tools like machine learning algorithms and natural language processing to extract the most useful insights. Large companies may seek data sets from their internal systems by integrating external sources. For structured data, organizations can use ERP systems, CRM systems, and internal databases that store transactional data, such as customer information and other key business details. Meanwhile, unstructured data can be sourced from emails, text documents, social media, and other digital channels that provide valuable insights into customer behavior or market trends. To effectively manage this data, companies can implement modern technological solutions like data fabrics or data meshes—look them up on Google—that enable real-time data integration and management across different systems and applications.

These technologies help create a unified source of information, allowing organizations to better leverage both structured and unstructured data. This, in turn, helps them make more informed business decisions. However, managing unstructured data can be a significant challenge, especially when it comes to data stored in Word documents, spreadsheets, emails, PDFs, or presentations. Such data often ends up in repositories accessible to unauthorized individuals, posing security risks that must always be considered. For this reason, organizations should place great importance on identifying and securing unstructured data, as well as implementing effective data management strategies to ensure the integrity and security of all data, regardless of its structure.

Thank you for framing the context around managing different types of data. Moving from discussing data structures to the practical challenges they bring, I’d like to ask about the most common data quality issues organizations face. Pawe?, what aspects of data quality pose the biggest challenges?

As we’ve been discussing, data can be a valuable source of information for organizations, but it can also come with errors that lead to serious consequences. The most common data quality issues include missing data, which can cause difficulties in analysis and decision-making. For example, if a company lacks complete customer data, it may struggle to understand customer needs and preferences, potentially only seeing a fraction of the market without realizing it. Another common issue is duplicate data, which can lead to incorrect conclusions and decisions. A company may have two copies of the same data and mistakenly believe it has more customers than it actually does. These things happen more often than you might think. We also encounter typical data errors, like incorrect data entry and lack of data control. For instance, a company might enter incorrect customer data, making it hard to identify their needs and preferences. Common errors often occur in Excel, where there’s no data type control, and one can input anything.

The system then has to analyze this, leading to considerable issues. Another problem is data inconsistency. Discrepancies between different systems or sources can make data integration and analysis difficult. For example, if a company has data in different formats, it might struggle to combine it into one dataset—whether it’s centimeters, millimeters, or kilometers, these are entirely different numerical values. Another issue is excessive data. Excessive data can make it difficult to manage and analyze effectively. For instance, a company may have too much data and struggle to determine which is most important. In the context of AI solutions, we use pre-processing techniques like feature importance to identify which features are most critical. This helps in managing problems related to excessive data, but it requires a solid understanding of the correlations within the data, which can be quite a burden.

Understanding data quality challenges is key to managing data effectively. With that in mind, could you share your approach to addressing data quality challenges in organizations?

To avoid these issues, most organizations can implement effective data management strategies. Here’s my “golden seven” for addressing data quality challenges: First, define roles and responsibilities. Establishing who is responsible for data management—collection, storage, analysis, and reporting—is the first step in creating clear data governance structures. Second, create a data department. A dedicated data department can be an effective way to establish clear governance structures, with responsibility for managing data, including collection, storage, analysis, and reporting. This formalizes the roles and responsibilities I just mentioned. Third, establish procedures. Setting up procedures for data collection, storage, analysis, and reporting ensures that everything happens in a clear, consistent way, following well-defined guidelines. Next, we need to define standards. Establishing data quality, security, and storage standards is essential for successful data governance. Without these standards, procedures and department responsibilities won’t be effective.

Then, implement monitoring systems. These systems track data quality and alert the organization whenever the quality drops below a certain threshold. This acts as a safety net, signaling when something’s wrong and needs attention. To complement monitoring, set up reporting systems. Reports provide insights into data quality levels and any issues, turning raw monitoring data into actionable intelligence. Finally, implement a training system. Providing employees with the necessary skills and knowledge to manage data at all levels is critical. From the data department to procedures, standards, and monitoring systems, they must understand what’s happening beneath the surface. With these steps, organizations can avoid data quality issues and ensure that their data is accurate, consistent, complete, and reliable.

Now that we’ve covered data quality and management, let’s move on to the next topic related to transforming organizations into data-driven enterprises. What are the key steps that should be taken to successfully transform an organization into one that relies on data for strategic decision-making?

Olga, transforming an organization into a data-driven enterprise is a complex process that requires careful planning and execution. To present this clearly, I’ll outline a concise overview of the key steps. First, define the vision and goals. It’s crucial to understand how data can support the organization’s strategic objectives. The vision should encompass how data will be used to make decisions, improve operational efficiency, and create new business opportunities. As for the goals, they should be measurable and aligned with the organization’s overall strategy, whether it’s increasing revenue, improving customer satisfaction, or optimizing operational costs. The second critical step is assessing the current state of the organization. This involves analyzing data assets and identifying what data is currently available. We need to assess the quality and storage of these data. It’s important to understand which data are crucial for our organization and what gaps exist as a result. We must also evaluate the technological infrastructure to determine whether our current IT systems are sufficient for handling, processing, and analyzing the data. This might involve assessing database management systems, analytical tools, and even cloud infrastructure.

The third step is to develop a data strategy, which involves planning data collection, managing data quality, and data governance. This means defining what additional data are needed and how they will be collected. It might, for instance, include integrating external data sources. We need to create policies and procedures that ensure data is accurate, consistent, and up-to-date, and establish rules for data storage, access, and protection. After that, we can move on to implementing new technologies, starting with data management systems and deploying modern systems that allow for the efficient handling of large datasets.

Along with this, we need analytical tools. Implementing data analytics tools that allow visualization and interpretation of data in a way that decision-makers can understand is crucial. Even popular tools like Power BI can effectively measure all KPIs and provide real-time insights, instead of performing post-mortem analysis with a week's delay. Of course, this also includes data processing platforms, such as using cloud platforms and big data technologies to process and analyze large volumes of data.

Another critical step, which I believe is very important at this stage, is training employees. We need to emphasize the development of technical and analytical skills and change their mindset. Training in analytics tools often focuses on programming languages and data processing technologies. There are a lot of options, and we need to select the tools that best align with our technology stack and development plans.

We must provide training on data analysis, interpreting results, and making data-driven decisions. The principle of being data-driven is to base decisions on data rather than intuition. In this case, intuition can be quite misleading. Finally, there’s the mindset shift: promoting a culture where decisions are made based on data rather than instinct. This is challenging because it involves overcoming ingrained beliefs and shifting to hard data—believing that four is always less than five, regardless of personal perception. When it comes to organizational culture change, this is a crucial step in the shift to a data-driven approach.

In an organization, we must promote the value of data and encourage employees to view data as a key organizational resource. We must foster cross-departmental collaboration to ensure that data is used in an integrated and holistic way.

Simply put, we need to treat data with the seriousness it deserves. Most importantly, leadership must set the example. Organizational leaders should make data-driven decisions and promote the value of data. Lastly, in the seventh stage, we focus on monitoring and evaluation—setting success metrics, conducting regular reviews, and continuously adjusting the strategy. Defining key performance indicators (KPIs) that track progress in the transformation will provide us with real-time insights into whether we are moving closer to success and if our transformation is progressing properly. Conducting regular reviews helps us evaluate whether goals are being achieved and what changes might be necessary. If needed, adjusting the strategy based on monitoring results and changing market conditions allows us to mitigate risks that could stem from poor decisions. By carefully executing all these steps, an organization can successfully transform into a data-driven enterprise, leading to better decision-making. However, it’s also important to allow employees to explore data and uncover nonlinear, unexpected correlations, rather than just focusing on calculating KPIs. People should engage in exploratory data analysis to provide the organization with new insights on how we can better adapt in the market through data-driven steering.

It's clear that this process requires both strategic planning and the right technology implementation, as well as the development of employee competencies. An interesting perspective on how cultural change within an organization plays a crucial role during such a transformation. Staying on that topic, I’d like to ask how organizational culture affects the success of the transformation.

Organizational culture plays a key role in the process and success of transforming a company into a data-driven enterprise. It’s a collection of values, norms, behaviors, and practices that essentially shape how the organization functions.

Organizational culture influences every aspect of the organization’s operations—strategy, structure, processes, and effectiveness. To achieve lasting business success, an organization must be built on a strong organizational culture. Transforming this culture is a complex process that requires careful planning and effective change management. This involves introducing and embedding new values, norms, and behaviors, always thinking in a data-driven manner. All this is aimed at improving performance and achieving better results. Cultural change is often necessary when a company needs to adapt to a changing environment, seize market opportunities, or create a competitive advantage. Leaders and managers play a huge role in building and transforming organizational culture.

By implementing even simple strategies, they can promote their company’s culture and ensure employees are more engaged and committed. However, leaders often don’t place enough emphasis on the importance of organizational culture, especially in crisis situations—this is where a lot can be lost. To effectively carry out this transformation, key players need to be involved in co-creating the change. The more people engaged in the process, the higher the likelihood of success. It’s also important to tailor the plan for cultural change to the specific organization because what worked in one company may be entirely irrelevant in another. A truly individualized, localized approach is needed. Organizational culture also affects how a company pursues its strategic goals, solves problems, and adapts to changes. As Peter Drucker, the management expert, famously said, "Culture eats strategy for breakfast."

This means that without a strong organizational culture supporting the strategy, the organization will not be able to achieve its goals. In the context of digital transformation, organizational culture plays a critical role in managing change. Companies need to identify weak points and determine what resources and capabilities are necessary to carry out the transformation. A short-term strategy is needed to meet immediate operational needs, followed by a focus on a long-term strategic vision.

To sum up this long answer, organizational culture is a key factor determining the success of transforming into a data-driven enterprise. A strong organizational culture supports strategy, improves efficiency, and enhances the ability to innovate and grow. Leaders and managers must emphasize the importance of organizational culture and involve key players in co-creating change to effectively carry out the cultural transformation. This was a brief overview of a very important and complex topic, but I hope I’ve managed to answer your question.

I completely agree with you. A strong culture that supports openness to change and innovation is the absolute foundation that determines the success of transforming an organization into a data-driven enterprise. Looking at how various organizations approach digital transformation, I’d like to now ask you about examples of companies that have successfully undergone this process. Which companies can serve as models for digital transformation?

The transformation into a data-driven enterprise is a process that requires careful planning and execution. Many companies around the world have successfully undergone this transformation, achieving significant business benefits. Of course, we cannot forget about Amazon, which is one of the most well-known examples of a company that has been data-driven since the very beginning. Amazon uses data to optimize its logistics processes, personalize customer experiences, and make strategic business decisions based on that data. Walmart, one of the largest retailers globally, has also undergone a data-driven transformation. The company now uses data to optimize its logistics processes, manage the supply chain, and, again, make strategic business decisions. This is always a crucial factor. We also have an example from Poland—Grupa Ciech, a company that successfully implemented a data-driven transformation as part of its XCFO initiative. The goal of the transformation was to create an organization where business decisions are based on effective data analysis, and management reporting focuses not only on providing data but also on signaling opportunities and risks, as well as presenting dynamic scenarios to support decision-making.

Even risk management and identifying opportunities and barriers, with data being delivered to the system to combat these challenges through well-prepared data sets, is a great solution that Grupa Ciech has executed exceptionally well. These successes demonstrate the effectiveness of promoting a culture where decisions are based on data rather than intuition. Leaders and managers placed the right emphasis on the importance of organizational culture and were actively involved in key processes, co-creating change and successfully carrying out the cultural transformation.

Thank you for those inspiring examples of companies that have successfully undergone digital transformation. It’s clear that the key to success lies in the skillful integration of technology with organizational culture, combined with a strategic approach to data management. This shows how much value data can generate when properly utilized for decision-making. Now, I’d like to focus on the technologies that can support such a transformation. What tools and technological solutions are most important for companies looking to become data-driven?

The transformation of an organization into a data-driven enterprise requires the use of appropriate technologies to support that goal. Modern technologies not only enable data collection and analysis but also integrate with various areas of the company, allowing for more informed business decisions. If we were to list such technologies, we must start with ERP systems, which are the backbone of transformation, integrating key business processes into one system. Modern ERP solutions, based on database technology and in-memory processing, are highly scalable and can be tailored to a company’s specific needs. This allows organizations to effectively manage finances, resources, production, and the supply chain within a single integrated environment. We cannot forget about cloud computing, which plays a key role in the transformation, offering flexible storage and processing of large data sets—essential for advanced analytics and machine learning. For example, Danone uses cloud computing for significant process optimization and decision-making. Closely following data is AI and machine learning as a technology.

This enables the automation of analytical processes and the identification of data patterns. These tools allow organizations to predict market trends, personalize customer offers, and optimize business operations. For example, companies use AI to analyze customer data, leading to a better understanding of their needs and preferences. Interestingly, one of the technologies also worth mentioning here is IoT (Internet of Things), which enables real-time data collection from various devices and sensors. This allows companies to monitor their operations, manage resources, and optimize production processes. IoT is particularly useful in industries where it helps monitor machines and prevent breakdowns. Big Data analytics also plays a critical role, enabling the processing and analysis of vast amounts of data, which is crucial for decision-making in businesses that handle large data volumes.

Through advanced analytics tools, organizations can gain valuable insights that support their strategies. These decisions and analyses can happen in real time, based on vast amounts of data, which sometimes carries a different weight and urgency. Companies may want to consider Big Data analytics as they grow and accumulate more data. Additionally, blockchain is an important technology, providing secure and transparent data storage. This is particularly important for data management, regulatory compliance, and applications like supply chain tracking or identity management. Further, in the realm of IoT, edge computing allows for data processing closer to the source, reducing latency and increasing operational efficiency.

Small devices can process data locally and then send only the results back to the system, which is particularly important for IoT applications that require real-time decision-making. To summarize, transforming into a data-driven enterprise requires not only the implementation of appropriate technologies but also a cultural shift and a revised business strategy. Companies must be ready to adopt new technologies and use them to create business value because organizational culture will be strongly tied to the technologies adopted. Ultimately, the success of the transformation will depend on the organization’s ability to integrate technology with business processes and use data to make better decisions.

Key technologies certainly lay the foundation for modern companies that want to effectively use data in their business processes. However, the implementation of technology is only part of the success. As we discussed earlier, ensuring high data quality is equally important. Let’s focus on how data quality impacts the accuracy and effectiveness of AI models.

Data quality plays a crucial role in creating highly accurate and effective AI and machine learning models. AI models, such as machine learning systems and deep neural networks, rely on large data sets to learn and refine their models. These data must be reliable, up-to-date, and well-prepared. There’s a saying, though a bit crude, that reflects the reality: "garbage in, garbage out." If poor-quality data or questionable data enters our model, that’s exactly the type of decision-making we can expect from it. If we have data with errors, such as incorrect formats, wrong units, missing or inconsistent data, or excessive data that doesn’t represent the full population, we can only expect our AI model to function partially. The impact of using incomplete data may seem insignificant at first, but when you consider the consequences of a model making mistakes, the risk to business operations and decision-making becomes clear.

We could list many AI failures caused by poor data. For instance, Amazon once withdrew an AI-based recruitment tool because it was biased toward male candidates. The reason was that the system had been trained on historical data dominated by male applications, leading to discrimination against women. Microsoft also launched a famous chatbot, Tay, which learned to communicate through social media but quickly started expressing offensive and inappropriate content due to the negative data it had been trained on from the internet. Another example is a Chinese facial recognition system that failed to identify people with darker skin tones because it was trained primarily on data from people with lighter skin tones.

We can clearly see that incomplete, inconsistent, or biased data can lead to serious issues, such as incorrect forecasts or even discriminatory decisions. This highlights that the success of AI models largely depends on well-prepared data. I’d like to now address a widely discussed topic regarding AI implementation. Is being a data-driven organization essential for effectively implementing AI?

In my opinion, digital transformation is an inseparable part of modern business, and a key aspect is transforming an organization into a data-driven enterprise. When it comes to implementing AI-based solutions, an organization should first become data-driven before building advanced AI models. Why is this so important? Why should an organization be data-driven? Let me explain. First, decision-making foundation: A data-driven organization uses data as the main asset for decision-making. Data provides objective information that helps managers and leaders make informed decisions, minimizing risks and increasing operational efficiency. Without a solid data foundation, decisions may be based on intuition or incomplete information, leading to errors and inefficiencies. Data allows organizations to better understand customer needs and preferences, as well as monitor market trends. This enables companies to adapt their products and services to changing customer expectations, which boosts satisfaction and loyalty. In addition to this, there’s process optimization: Data enables the identification of areas that need improvement and the optimization of business processes. Through data analysis, companies can spot inefficiencies, reduce operational costs, and increase their efficiency. What are the downsides of not being data-driven? First, intuition-based decisions: Without data, decisions often rely on intuition or experience, which can lead to incorrect conclusions. The lack of data makes it harder to assess the effectiveness of actions and make strategic decisions. Organizations that are not data-driven may struggle to adapt to changing market conditions, resulting in a lack of flexibility. Not having access to up-to-date data makes it difficult to spot new trends and adjust strategies. There’s also limited innovation potential: Data is a key element of innovation. Without it, it’s hard to identify new business opportunities, test new ideas, or implement innovative solutions.

So why should we take a step back before implementing AI? Why should we first become data-driven?

The key is to ensure that we are prepared to generate the right data. What we care about most is data quality. AI models are only as good as the data they’re trained on. Without high-quality data, AI models may produce inaccurate results. Therefore, an organization must first ensure data quality before starting to build AI solutions. Another factor is understanding the data: Before implementing AI, an organization must understand what data it has, how it’s structured, and what insights can be extracted from it. Without this understanding, it’s difficult to define goals and expectations for AI models.

Data integration is also very important. A data-driven organization integrates data from various sources, which allows for more comprehensive use of AI's potential. Before implementing AI, an organization must ensure data integration to avoid issues related to incomplete or inconsistent data. Now, once we are data-driven, I suggest three quick steps for implementing AI. First, define business goals: A data-driven organization can clearly define the business goals it wants to achieve with AI. This could include improving operational efficiency, increasing revenue, or better understanding customers.

Next, use advanced analytics: A data-driven organization has access to advanced analytical tools that support AI model development. This allows the company to create more precise and effective models.

Finally, monitor and optimize: A data-driven organization can continuously monitor model performance and make necessary adjustments.

This enables the company to continually optimize its solutions and adapt them to changing market conditions. Becoming a data-driven enterprise is a crucial step before implementing AI solutions. A data-driven organization has a solid foundation for decision-making, better understands its customers and the market, and has the ability to optimize processes. Without these foundations, AI implementation carries a high risk and could lead to incorrect results. That’s why we focus on building a data-driven culture first, and only then do we invest in advanced AI technologies.

Thank you for providing such a comprehensive argument for why organizations should focus on becoming data-driven before implementing AI solutions. As we conclude our conversation, I’d like to ask you about the future of data. What do you see as the main trends in data development?

Today, in the era of digital transformation, optimizing data quality is becoming an integral part of business strategy. Data is the heart of modern organizations, and its quality directly impacts a company’s ability to make sound decisions and maintain competitiveness. In this context, the future of data optimization is closely linked to the dynamic development of technology and the growing importance of data-driven approaches. What are the future trends? First, advanced machine learning algorithms: These are increasingly used for automated data cleansing and quality improvement. Techniques like deep learning can identify patterns and anomalies in data, enabling automatic correction and completion. Another key trend is integration with cloud technology. Cloud computing offers flexible resource scaling and access to advanced analytical tools, allowing organizations to manage large data sets and optimize data quality in real time.

This is incredibly important. It’s also essential to approach data management ethically. Organizations will need to develop advanced frameworks to ensure privacy, security, and ethical data usage. This will be crucial for maintaining customer trust and compliance with legal regulations. Additionally, Intelligent Process Automation (IPA) will be a significant factor.

It will allow for faster and more precise data processing, improving data quality and usability. Lastly, although this may seem a bit futuristic, quantum computing has the potential to revolutionize data processing. When it becomes available, it will enable the analysis of massive and complex data sets at unprecedented speeds. This could greatly accelerate processes related to data quality optimization. In summary, the future of data quality optimization is tied to the rapid development of technology and the growing importance of data-driven approaches.

Organizations must first become data-driven before implementing advanced AI solutions to fully harness the potential of data and achieve lasting business benefits. Through advanced algorithms, cloud technology, quantum computing, and ethical data management, organizations will be able to optimize data quality effectively and make better business decisions. This will allow them not only to increase operational efficiency but also to build a lasting competitive advantage in a rapidly changing business landscape.

Thank you, Pawe?, for the inspiring conversation, your valuable insights, and the thorough discussion on the topic. We live in interesting times, and our conversation has shown how crucial data is in today’s business, as well as how important data quality and optimal management are. I’m sure our listeners followed our discussion with great interest.

Thank you as well for the conversation, Olga. I realize we’ve only scratched the surface. If any of the listeners are interested in learning more about the data-driven culture or AI development, feel free to connect with me on LinkedIn or join the Polish ML Community meetings, where we share knowledge on AI. Thank you very much.