As the next-generation AI technology, Generative AI is gradually changing the face of business enterprises. Essentially, this technology encompasses advanced techniques for machine learning, deep learning, and natural language processing. Generative AI is also capable of working with a variety of complex data structures from data sources including warehouses.
For instance, Generative AI in data warehousing can improve data accuracy and consistency. With capabilities like data pattern and anomaly identification, it can correct any data inconsistencies, thus improving the data quality. At the same time, Generative AI cannot function realistically without the support of a data warehouse. Hence, it makes sense for data-centric organizations to integrate Generative AI into their data warehouses.
As Generative AI continues to evolve, here’s a look at some “winning” strategies for organizations to integrate this AI technology into their data warehouses.
Data Pipeline
By integrating Generative AI, data organizations can leverage this technology to prepare new text files for querying by large language models (LLMs). With a data pipeline, they can perform the following operations:
- Assemble unstructured text from a variety of text sources like business emails, customer interactions, and transcripts, and ingest this content to cloud platforms.
- Convert the text into tokens where each token represents a word or a character. Additionally, by chunking the tokens, they can be understood by vectors.
- Convert the tokens and chunks into numerical vectors to understand their meaning.
- Load the vectors into the vector database for data storage and indexing.
- Implement the LLM to search and query the vector database.
Through the integration of data pipelines and Generative AI, the language model can query the vector database and generate a natural language response to any customer query.
Data Management
Inaccurate and inconsistent data is one of the many challenges for data warehousing. Generative AI in data management can automate the data cleansing process. By improving data quality, it makes it easier for data analysts to retrieve accurate data and business insights.
Here are some of the best practices of effective data management for Generative AI:
- Data collection: As Generative AI models train on large data volumes; enterprises must provide the model with high-quality data collected from diverse datasets. These datasets can remove any “bias” from Generative AI models.
- Data labeling: This practice is all about labeling the data with the correct information including data type, source, and context.
- Data governance: With a data governance framework, organizations can ensure the integrity and security of their collected data. This framework also defines the procedures and policies for storing the data in warehouses.
Data Security
Going forward, data security is a major challenge for Generative AI models. Some of the common pitfalls include:
- Data sequestration and confidentiality
- Inimical raids through manipulation of inputs to Generative AI models
- Implicit manipulation of the Generative AI models
Here are some of the effective strategies to secure Generative AI models:
- Data security measures like discrimination sequestration and allied literacy during training of Generative AI models.
- Techniques like inimical training and input sanitization can improve the model's resilience to inimical raids.
- Modifying and authenticating Generative AI models through methods like model watermarking and blockchain verification.
Data Engineering
In data engineering, Generative AI tools can generate synthetic data as well as code for data pipelines. For instance, with Generative AI in data lakes, organizations do not have to define the data lake using a GUI or JSON template. Once they have defined the data lake specifications and constraints, Generative AI can automatically create the data lake.
Similarly, Generative AI can transform how the data lineage is tracked and managed. In data warehousing, Generative AI can improve data quality and accuracy by:
- Generating the warehouse schemas automatically.
- Generating the data queries automatically.
- Identifying and rectifying data errors.
- Predicting future trends.
Data Analytics
Finally, Generative AI can integrate and transform data analytics and visualizations. For instance, Generative AI models can automate code generation for applications used in data analytics. Some LLMs can also generate synthetic data for machine learning algorithms when real-time data sources are not available, expensive, or protected for privacy.
Conclusion
To summarize, by integrating Generative AI technology into data warehouses, organizations can create a competitive advantage through the generation of high-quality and accurate data. This is why many data solution providers are currently embedding Generative AI into their solutions.
Over the years, Wissen has enabled its customers to embrace digital transformation through services in application development, intelligent automation, AI and machine learning, and Big Data Analytics. In the domain of Big Data Analytics, we provide both consulting and implementation services. Through our services in Intelligent Automation, we have enabled the seamless integration of Generative AI with machine learning and analytics.
We can help you leverage your investments in Generative AI and data warehousing. If interested, contact us now.
