High-quality data — and lots of it — is an essential ingredient in AI success. The reliability of AI outputs is directly related to the quality of data used to train the AI model. High-quality data enables AI models to learn accurate patterns so they can extract meaningful insights.
Quality is more than just correctness. Datasets must include all the information needed to make predictions and follow a consistent format to maximize performance. The data should also be up to date, reflect current conditions and relate directly to the problem the AI model is trying to solve.
Unfortunately, many organizations are struggling with siloed data and poor data management practices. Data is captured as part of routine business processes, but it remains isolated in individual applications and data stores scattered throughout the IT environment. It isn’t effectively managed or kept up to date as part of an overarching data governance program.
Data Repository Models: A Primer
That’s why AI initiatives often start with data migration. Organizations need to migrate data from legacy systems to modern data platforms and consolidate data stored in individual systems and cloud environments. The data must be cleansed and standardized to ensure the performance and accuracy of AI models.
A key step is determining which type of data repository to use. There are three primary models.
Data Warehouse
In the late 1980s, IBM researchers Barry Devlin and Paul Murphy developed the data warehouse model to store large amount of data for analysis. It creates a framework for data to flow from individual systems and operational environments to a centralized repository.
Data warehouses are designed to store structured data in a hierarchical system of files and folders so it can be queried and analyzed efficiently. They have a shorter learning curve than modern data repositories, but they are far less flexible.
The changing nature of data has made data warehouses somewhat impractical. Today, as much as 90 percent of data is stored in unstructured formats, such as text documents, videos and email. As a result, data warehouses are considered legacy solutions with limited use cases.
Data Lake
Data lakes were developed in 2011 to overcome the limitations of data warehouses and data marts. They can house large volumes of structured, semi-structured and unstructured data and do not require “extract, transform, load” (ETL) operations to prepare data for querying and analysis. Raw data can be stored and accessed directly.
Major cloud providers, including AWS, Google and Microsoft, have incorporated data lake capabilities into their platforms. Data lakes also use open formats and are built using open-source tools and frameworks to minimize vendor lock-in.
However, the flexibility of data lakes comes at a price. Data lakes have lower performance and a longer learning curve than data warehouses, and sacrifice the structure and governance of the older model. Some organizations have created hybrid environments combining data lakes and data warehouses, but that increases complexity.
Data Lakehouse
The data lakehouse has emerged as the modern, purpose-built answer to the hybrid data lake and data warehouse environment. It uses a so-called “medallion” or “multi-hop” architecture with layers that incrementally improve the structure and quality of the data.
After ingestion, data goes through cleaning, validation and normalization processes. In the final layer, data is aggregated and filtered to create contextually meaningful data sets for use in analyses, applications and dashboards.
Like a data lake, a data lakehouse supports structured, semi-structured and unstructured data and uses open-source formats and toolsets. However, a data lakehouse provides an audit trail of data transformation and governance features that aid in regulatory compliance.
The Importance of a Strategic Approach
By documenting data assets and data flows, organizations can get a sense of which data repository model will help them meet their objectives. Migrating data to cloud object storage provides a cost-efficient platform for aggregated data that can scale on demand. As part of the process, organizations should develop a data governance program to ensure that data is collected, stored, used and managed according to best practices.
Cerium’s data platform and data analytics specialists are here to help you select the right data repository model and prepare your data for AI adoption. Contact us to discuss your needs and objectives.
