In a data multiverse of madness: data quality comes first

Monday morning... you receive your weekly data file, only to find the format has changed. You request the data from somewhere else and this version has wildly different data. It's as if you've stumbled into a data multiverse, where consistency seems elusive. But why does keeping data consistent often feel like such a colossal task?

Nowadays, decision makers are captivated by the promise of Generative AI and other advanced ML models. These models, driven by complex algorithms, crave vast amounts of data to learn and make decisions. But, as the saying goes, "garbage in, garbage out." The success of these models hinges on the quality of the data they're fed. Therefore, clean, accurate, and valid data is no longer just a preference; it's an imperative. To fully realize the potential of these models, we must first confront the question: Where is our data coming from, and do we truly know our sources?

As a data science professional, I've traversed the trenches of data wrangling, dedicating countless hours to transforming raw, messy data into actionable insights. The reality is that around 80% of our time is often spent wrestling with data, documenting assumptions, handling outliers, and pruning disruptive elements. This crucial phase of data preparation can't be understated, as it forms the foundation upon which the entire analysis is built.

To ensure good data quality, consider the following recommendations:

1. Establish a Single Source of Truth: Ensure that everyone in your organization is working with consistent, up-to-date information, minimizing confusion and errors.
2. Data Quality Monitoring: Enable systems that can provide alerts when data discrepancies arise, enabling prompt corrective action and maintaining data integrity.
3. Enhance Data Security: Establish guardrails and processes to protect your data against potential breaches and leaks, ensuring sensitive information remains safeguarded.
4. Increase Data Availability and Accessibility: Higher and secure accessibility means that the right stakeholders can access the right data at the right time, promoting collaboration and informed decision-making.

Luckily, all of these recommendations can be achieved with the adoption of Master Data Management (MDM) solutions and cloud platforms (e.g. Snowflake). These solutions aim to add an extra layer of data assurance, source identification, and prevents the propagation of duplicated data sources.

In conclusion, the allure of Generative AI is undeniable, promising transformational advancements in numerous domains. However, before setting sail on this journey, it's essential to take stock of your existing data collection practices and sources.

The road to harnessing the power of these models begins with a commitment to data quality. Only by taming the data multiverse can we hope to unleash the true potential of AI-driven innovation. So, before you dive into the realm of advanced models, make sure your data foundation is solid and ready to support the brilliance of tomorrow.