Why enterprise data management is the relevant basis for machine learning

Machine learning (ML) applications are becoming more common and accepted recently, especially driven by innovations like large language models (LLMs).

However, the success of ML applications highly depends on the quality of the data. Among other things, this is part of a company’s so-called “data management”. Therefore, effective data management becomes critical for successfully using ML technologies.?

The following article describes the link between ML and data management in more detail. It further describes why data management is important for companies that want to enable and extend the use of ML for themselves or their customers.

Rising demand for ML and increasing corporate adoption

Machine Learning research and applications have become increasingly popular in recent years due to advances in computing power and higher availability of data, but also because of better user experience. In late 2022, OpenAI launched its chatbot ChatGPT, a specific ML application based on large language models. Through its interface, LLMs have made machine learning tangible for the general public, allowing users to interact with the model using natural language and experience concrete and fascinating results.?

This sparked a lot of hype about machine learning in 2023.

As machine learning gains widespread attention and adoption, the demand for corporate ML applications surged notably in 2023. Forecasts also predict a continued rise in the integration of ML within companies in the coming years. Alongside popular applications like natural language processing (NLP) and specifically LLM chatbots, various other areas of ML are witnessing increased demand. This includes functions such as product recommendations on e-commerce platforms, demand forecasting, predictive maintenance, and numerous others.

Proprietary data enhances ML models

In many cases, leveraging proprietary or customer data for machine learning models yields superior results compared to using off-the-shelf models. This approach allows for tailoring the model to specific use cases and data characteristics, resulting in enhanced accuracy and performance.

For example, a customer support chatbot enhanced with a Retriever-Augmented Generation (RAG) model that uses proprietary data, such as product manuals and customer inquiries, can provide highly personalized support. By utilizing this domain-specific information, the chatbot provides precise, context-related solutions to users’ problems.

This integration not only boosts the chatbot’s effectiveness but also aligns responses with the company’s standards.

Similarly, when implementing tools like code assistants for software development, utilizing a model trained on the company’s own codebase can provide more relevant and effective suggestions. This ensures alignment with existing coding practices and standards, ultimately improving productivity and code quality. Therefore, using proprietary data can greatly improve ML models.

However, the utilization of proprietary data comes with the responsibility of managing and maintaining that data effectively.

Importance of data management for companies using ML

As machine learning applications become increasingly integrated into business operations, companies are realizing the critical role of high-quality data. Effective data management can help here. It ensures that this data is collected, stored, processed, and utilized efficiently.

Effective data management is not merely a standalone task; rather, it serves as a foundational element of an enterprise data strategy, ensuring that data practices align with the company’s long-term objectives.

By implementing robust data management practices, companies can maximize the potential of their proprietary data, leading to enhanced decision-making, increased efficiency, and a competitive edge in the market.

But what exactly does the term data management mean for companies?

Data management, tracing back to the dawn of digital data, has evolved alongside digital technology itself. As a result, data management has grown into a multifaceted and dynamic field with a broad scope and definition.

Today, data management encompasses a diverse array of activities. These activities can be categorized into several key disciplines:

• Data collection: This marks the initial and foundational step of the data management process, focusing on acquiring information relevant to the company’s objectives.?
• Data Storage: Once data is collected, it must be stored in a manner that facilitates efficient access and management. This involves leveraging databases, data warehouses, or cloud storage solutions to organize the data securely.?
• Data Processing: The objective here is to prepare data for analysis. This involves transforming raw data into a suitable format for analysis.?
• Data analysis: Data analysis is the examination of datasets to extract insights. It employs statistical, algorithmic, or machine learning techniques to identify trends, patterns, and relationships. Businesses leverage data analysis to make well-informed, data-driven decisions that align with their goals and objectives. Analysis can take various forms, ranging from predicting future outcomes based on historical data to uncovering underlying factors behind past events.
• Data Security and Privacy: This aspect focuses on safeguarding data from unauthorized access and ensuring that collected data is used in compliance with privacy laws.?
• Data governance: Data governance involves the overall management of data’s availability, usability, integrity, and security within an organization.

How enterprise data management supports ML in particular

The principle that “ML models can only be as good as the data on which they are trained on” underscores the importance of data quality in ML. And we have realized that it makes sense to add domain data to targeted ML applications and that it is therefore worth thinking about a sustained strategy for the data involved. This involves a solid data management concept, as defined above. So what are the most important components of data management from the previous definition for ML and AI? Effective data management supports machine learning by the following:

• Ensure that the right data sources have been selected to achieve the best performance based on the objectives.
• Provision of high-quality data for training and validating ML models through data storage and data processing.
• Implementing data governance policies that align with legal compliance, such as GDPR, and ethical standards for data usage.
• Protection of sensitive and confidential data against breaches and unauthorized access.?
• Enabling the scaling of ML operations as the volume and complexity of data grow through data storage solutions.

Advancements in machine learning and LLMs propel organizations towards enhanced enterprise data management for quality, governance, and scalability

In summary, the growing demand for machine learning applications, fueled by advancements like LLMs, is driving increased adoption by businesses. Effective data management has become essential for ensuring data quality, governance, privacy, and scalability as machine learning integrates into business operations. As machine learning evolves, the strategic significance of proficient data management will further rise, emphasizing its crucial role in leveraging machine learning and artificial intelligence in business processes.

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