Data is the new gold
- we help you to raise it

The amount of available data plays a decisive role in the development of powerful artificial intelligence (AI). The quantity of data significantly influences the AI's ability to recognise patterns, make predictions and solve complex tasks effectively.

For these reasons, the amount of data plays a crucial role in training an AI:

1. Better model accuracy: A large amount of data enables AI models to recognise more comprehensive and accurate patterns. The more data the model can process, the more accurately it can make predictions and filter out irregularities.
2. Diversity and representativeness: An extensive database covers a greater variety of scenarios and variations, which improves the AI's ability to react robustly in different situations. It ensures that the model is not just fixated on specific or rare data patterns.
3. Increasing robustness: With more data, the AI can generalise better and is less susceptible to overfitting. It learns not only to understand exceptions, but also to apply general rules, which increases the stability of the model.
4. Fault tolerance: A large amount of data also makes it possible to better process anomalies or incorrect data points in context without negatively influencing the overall model. This contributes to the reliability of the model predictions.
5. Enabling deep learning: Especially for deep neural networks, which have a large number of parameters and complexity, large amounts of data are required to make the models meaningful and powerful.

The amount of data is not only an advantage, but a necessity in order to utilise the full potential of artificial intelligence.

Below you will find typical barriers to data quantity:

As already described, the training of artificial intelligence (AI) is dependent on a sufficient database in order to develop precise and effective models. One of the biggest challenges here is the balance between the need for a large quantity of data and the strict requirements of data protection.

Data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe, set strict rules on what data can be collected and processed. This can restrict access to large data sets and thus reduce the quantity of data needed to train AI.

As the public becomes more aware of the need to protect their personal data, companies have a duty to ensure transparent and compliant data collection practices. These requirements may lead to a more cautious data policy that further reduces the quantity of available data.

In order to train AI models effectively, a balance must be found between the required data quantity and data protection requirements. Innovative approaches such as federated learning, data trusts or synthetic datasets offer the opportunity to utilise data without jeopardising data protection. These solutions can help to overcome the challenges of data protection and provide AI systems with a sufficient database.

An often underestimated challenge is the heterogeneity of IT landscapes, which can make it difficult to capture and utilise large volumes of data.

1. Data fragmentation: In many companies, data is distributed across different systems, platforms and technologies. This fragmentation makes it difficult to create a consistent and comprehensive database that is required for training AI models.
2. Incompatible data formats: Different systems often use divergent data formats, making it difficult to integrate and consolidate this data into a unified data set. This can lead to gaps in data quantity, which is essential for effective AI training.
3. Different data quality standards: Heterogeneous IT landscapes result in data from different sources having different quality standards. This inconsistency can hinder the availability of a standardised and reliable database.
4. Silo data storage: Data is often stored in isolated silos, which makes it difficult to access for analysis purposes. Such silos prevent all relevant data from being brought together for AI training, which further limits the quantity of data.

To overcome the challenges posed by heterogeneous IT landscapes, a targeted approach to data integration and standardisation is required. The use of data warehousing, the implementation of interfaces and middleware as well as the promotion of a company-wide data strategy can help to create a high-quality and comprehensive database.

An often overlooked obstacle to maximising data quantity are mental silos within organisations that can significantly hinder the effective use and integration of data.

Typical effects of mental silos in organisations:

1. Fragmented mindset: Mental silos occur when departments or teams within an organisation view their data as individual property. This isolated mindset leads to data not being shared or systematically integrated, which limits the total amount of data that can be utilised for AI.
2. Knowledge transfer: A lack of communication between teams can lead to valuable data sources remaining unknown or unutilised. This misses the opportunity to fully utilise and combine data for AI training purposes.
3. Interrupted collaboration: When teams work in mental silos, there is often a lack of a shared vision or strategy for data processing. This leads to inefficient data management systems and impairs data collection.
4. Corporate culture: A corporate culture that encourages silo thinking slows down innovation. This mindset can limit access to large amounts of data and thus minimise the quantity of data required for AI projects.

To overcome mental silos and maximise data quantity, companies should foster a culture of collaboration and open data sharing. Initiatives such as interdisciplinary teams, shared data governance strategies and data literacy workshops can help to break down mental silos and create a comprehensive data foundation for AI applications.

As we have no doubt conveyed to you by now, data is at the heart of any successful AI initiative. Choosing the right sources for data collection is crucial to effectively train AI models and gain valuable insights. In addition to the internal data you already collect in your organisation, here are some suggestions for possible sources of external data:
 

1. Data platforms: Platforms such as Kaggle, for example, offer a wealth of open data sets that are collected and provided by the community. They are not only an almost inexhaustible source for various data categories, but also offer competitions and forums for the exchange of knowledge and experience.
2. Data search engines: Specialised search engines, such as Google Dataset Search or other industry-specific tools, make it easier to identify and access publicly available datasets. They act as hubs for discovering relevant and up-to-date data.
3. Data crawlers: These automated tools continuously search the internet to collect structured and unstructured data. Data crawlers are particularly useful for efficiently extracting large amounts of data and making it available for analyses. We also operate our own data crawler service, which we use for PRICE monitoring, among other things. However, the capabilities of our crawlers go far beyond pure price recording. Do you have specific requirements in this area? Talk to us!
4. Crowdsourcing: In this method, data is collected through the collective participation of many people. Platforms such as Amazon Mechanical Turk make it possible to create large and diverse data sets through the collaboration of the global community, which is particularly valuable for the collection of training data for machine learning.

5. Public databases and government data: Many governments and public institutions make their data available for free to promote transparency and encourage innovation. Such databases often contain statistical information, survey data and geographic data.

   Examples include:

   GovData: The data portal for Germany is the central open data portal for public administration in Germany and offers access to a variety of data from different areas, such as the environment, economy, transport and more.

   Destatis: The Federal Statistical Office provides comprehensive statistical information on a variety of topics, including the economy, population, health and education. This data is often available in the form of reports and datasets.

   Federal Environment Agency (UBA): The Federal Environment Agency provides data on the environment and climate protection. This includes information on air quality, water, soil and emissions.

These sources offer a wide range of opportunities to collect the high-quality and relevant data needed to develop powerful AI projects. Of course, the choice of the appropriate data source depends on the specific requirements and objectives of your project.