Preparing Data for Successful AI-Powered Solutions

In today's data-driven world, many data analysts face delays in accessing data warehouses and spend extra time searching for the specific data they need. Despite efforts to streamline queries and improve database efficiency, they often struggle because the data doesn't always reflect the real-world complexities beyond basic data types and tables.

Modern enterprises are frequently updating and rebuilding their data warehouses. On average, an enterprise data team handles hundreds of databases, which can lead to overlapping data pipelines. These databases are usually created separately to meet various on-the-spot requests from different business teams.

The consequences of these practices are clear: we end up with outdated data pipelines, unused data products, and higher IT cloud costs. Traditionally, data teams have focused mainly on reducing costs and improving database efficiencies, often overlooking how the data will be used by Business teams.

But even the best data storage and movement strategies won't help if the resulting data isn't used effectively. It's not just about getting rid of outdated databases and pipelines; it's about creating data models that business teams will use. One way to do this is by setting up a semantic layer based on business metadata. This helps business teams find and use the data they need more easily. It also helps improve data quality by reusing existing data warehouses and filling in any missing data.

An added benefit of this approach is the ability to reverse engineer, which allows businesses and experts to better understand different areas and the common practices used across them.

This can be the starting point for creating data quality assurance that focuses on safety, security, and trust.

By implementing a system for ensuring data quality, the responsibility for maintaining good data is shared across the organization, reducing the load on the data team.

Additionally, connecting all data models to an enterprise-wide business glossary creates a shared set of terms and definitions used across the company. While some of these glossaries might focus on specific areas, the first step is to make them accessible to everyone, adding important business context to the company's data resources.

Making data ready for AI means making data modeling more accessible and promoting a better understanding of data across the various businesses within the organization.

This change empowers businesses to use data more effectively, unlocking valuable insights that can drive success.