Modern Data Quality: Navigating the Landscape
Tags: database, ai, data, machinelearning
Published: 2024-04-23 12:35:39

Data quality isn’t just a technical issue; it’s a journey full of challenges that can affect not only the operational efficiency of an organization but also its morale. As an experienced data warehouse consultant, my journey through the data landscape has been marked with groundbreaking achievements and formidable challenges. The latter, particularly in the realm of data quality in some of the most data-intensive industries: banks, and telcos, have given me profound insights into the intricacies of data management. My story isn’t unique in data analytics, but it highlights the evolution necessary for businesses to thrive in the modern data environment.

Let me share with you a part of my story that has shaped my perspective on the importance of robust data quality solutions.

## The Daily Battles with Data Quality
In the intricate data environments of banks and telcos, where I spent much of my professional life, [data quality issues](https://www.digna.ai/why-data-issues-continue-to-create-conflicts-and-how-to-improve-data-quality) were not just frequent; they were the norm.

## The Never-Ending Cycle of Reloads
Each morning would start with the hope that our overnight data loads had gone smoothly, only to find that yet again, data discrepancies necessitated numerous reloads, consuming precious time and resources. Reloads were not just a technical nuisance; they were symptomatic of deeper data quality issues that needed immediate attention.

## Delayed Reports and Dwindling Trust in Data
Nothing diminishes trust in a data team like the infamous phrase “The report will be delayed due to data quality issues.” Stakeholders don’t necessarily understand the intricacies of what goes wrong — they just see repeated failures. With every delay, the IT team’s credibility took a hit.

## Team Conflicts: Whose Mistake Is It Anyway?
Data issues often sparked conflicts within teams. The blame game became a routine. Was it the fault of the data engineers, the analysts, or an external data source? This endless search for a scapegoat created a toxic atmosphere that hampered productivity and satisfaction.

## The Drag of Morale
Data quality issues aren’t just a technical problem; they’re a people problem. The complexity of these problems meant long hours, tedious work, and a general sense of frustration pervading the team. The frustration and difficulty in resolving these issues created a bad atmosphere and made the job thankless and annoying.

## Decisions Built on Quicksand
Imagine making decisions that could influence millions in revenue based on faulty reports. We found ourselves in this precarious position more often than I care to admit. Discovering data issues late meant that critical business decisions were sometimes made on unstable foundations.

## High Turnover: A Symptom of Data Discontent
The relentless cycle of addressing data quality issues began to wear down even the most dedicated team members. The job was not satisfying, leading to high turnover rates. It wasn’t just about losing employees; it was about losing institutional knowledge, which often exacerbated the very issues we were trying to solve.

## The Domino Effect of Data Inaccuracies
Metrics are the lifeblood of decision-making, and in the banking and telecom sectors, year-to-month and year-to-date metrics are crucial. A single day’s worth of bad data could trigger a domino effect, necessitating recalculations that spanned back days, sometimes weeks. This was not just time-consuming — it was a drain on resources.

## The Manual Approach to Data Quality Validation Rules
As an experienced data warehouse consultant, I initially tried to address these issues through the manual definition of validation rules. We believed that creating a comprehensive set of rules to validate data at every stage of the data pipeline would be the solution. However, this approach proved to be unsustainable and ineffective in the long run.

The problem with manual rule definition was its inherent inflexibility and inability to adapt to the constantly evolving data landscape. It was a static solution in a dynamic world. As new data sources, data transformations, and data requirements emerged, our manual rules were always a step behind, and keeping the rules up-to-date and relevant became an arduous and never-ending task.

Moreover, as the volume of data grew, manually defined rules could not keep pace with the sheer amount of data being processed. This often resulted in false positives and negatives, requiring extensive human intervention to sort out the issues. The cost and time involved in maintaining and refining these rules soon became untenable.

Comparison between Human, Rule, and AI-based Anomaly Detection Table 1:1


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/z99e9tm4k153yvq8f21r.png)

## Embracing Automation: The Path Forward
This realization was the catalyst for the foundation of digna.ai. Danijel (Co-founder at Digna.ai) and I combined our AI and IT Know-How to create AI-powered software for Data Warehouses. This led to our first product [Digna](https://www.digna.ai/), we needed intelligent, automated systems that could adapt, learn, and preemptively address data quality issues before they escalated. By employing machine learning and automation, we could move from reactive to proactive, from guesswork to precision.

Automated data quality tools don’t just catch errors — they anticipate them. They adapt to the ever-changing data landscape, ensuring that the data warehouse is not just a repository of information, but a dependable asset for the organization.

Today, we’re pioneering the automation of data quality to help businesses navigate the data quality landscape with confidence. We’re not just solving technical issues; we’re transforming organizational cultures. No more blame games, no more relentless cycles of reloads — just clean, reliable data that businesses can trust.

In the end, navigating the data quality landscape isn’t just about overcoming technical challenges; it’s about setting the foundation for a more insightful, efficient, and harmonious future. This is the lesson my journey has taught me, and it is the mission that drives us forward at dext.ai.

This article was written by Marcin Chudeusz, CEO and Co-Founder of Digna.ai a company specializing in creating Artificial Intelligence-powered Software for Data Platforms. Our first product, Digna offers cutting-edge solutions through the power of AI to modern data quality issues.

Contact us to discover how Digna can revolutionize your approach to data quality and kickstart your journey to data excellence.