“We were not aware of the Data Quality Issues we have,” is a statement I often hear from our customers during our Proof of Value (PoV) sessions that reveals the hidden truths about data quality issues in their various data warehouses, data lakes, and lakehouses.
Today I’m excited to share a narrative that’s close to my heart and resonates with our mission’s core — helping data platforms detect data quality issues early.
In the vast realm of data, the lurking challenges often go unnoticed until they materialize into formidable obstacles. It is important to note that even when these issues might not present dire consequences at the moment they often mold up as data continues to compound into something fatal. It is often best to know what data quality issues your Data Warehouse is facing then you either — change it or accept it. This is much better than being oblivious to the risks. Allow me to peel back the curtain and share some eye-opening insights from the PoVs we executed.
The Eye-Opening Reality in PoVs
In our PoVs, a process where we show how Digna performs in predicting, detecting, and alerting users of data quality issues and what it brings to the customer. We showcase what would have been discovered on time if Digna had been in place during historical data.
Though we inspect only a small subset of customer data, the prevalence of data quality issues is striking. As companies generate and store increasing amounts of data for future business cases, a crucial question arises: Is the data correct? The answer is often unclear once issues like missing values, swapped columns, and other anomalies are brought to light. Let me give you a glimpse into some of the common data nightmares we’ve encountered:
Data Ghosting
This happens when critical data suddenly disappears or becomes inaccessible. For example, in the retail sector, this can manifest as missing transaction records, customer profiles, or purchase histories. The root causes could range from improper data migration, and integration errors, to database corruption.
The Empty Column Crisis
In this scenario, vital information like employee birth dates in HR databases suddenly goes missing. Such issues often arise from internal or external flawed data entry processes, failed system updates, or erroneous data cleansing practices.
Truncated Tragedy
This involves significant errors in financial data, particularly revenue figures. This can manifest as sudden, unexplained drops in reported revenue, potentially leading to misguided business decisions, inaccurate financial reporting, and eroded investor confidence. Causes might include data truncation errors, incorrect data aggregation, or faulty data import/export processes.
Values Inverted
Values Inverted issues occur when data values are mistakenly flipped or inverted. An example of seasonal data could be winter sales figures being recorded under summer months and vice versa. The inversion could stem from incorrect data mapping, coding errors in data transformation scripts, or manual data entry errors.
Mix-Up Mayhem
This happens when data sets get entangled or incorrectly mapped. For instance, German states might be listed in place of Austrian ones in a geographical database. This mix-up can lead to significant issues in location-based analytics, market segmentation, and logistical planning. The underlying causes could be incorrect data linkage, flawed algorithmic sorting, or database merging errors.
Column Confusion
Here, there’s a mix-up in the database columns, like swapping first and last names. This can cause havoc in customer relationship management, legal documentation, and personalized communication. Such problems often originate from errors in data migration, ETL (Extract, Transform, Load) process flaws, or misaligned data schemas during system integrations.
Having been a victim of the above-listed data issues myself as a data warehouse consultant, our team at DEXT.AI developed Digna as a beacon that cuts through this complexity without needing predefined data quality rules. It calculates metrics out of the box and raises the alarm if the data doesn’t align with expectations. A true exemplar of Modern Data Quality and observability, driven by the magic of AI.
How Our PoCs Look Like
Depending on your data history, our approach to unraveling the data quality challenges facing your Data Warehouses, Data Lakes, and Lakehouse varies.
With Data History — Get Report in 3 Days
We inspect 20 tables and provide a report on past data quality issues for these tables within three days of analysis. This alone saves a lot of costs, risks, and potential impact on your Data Warehouse, Data Lakes, and end users. It is important to note the industry standard is three months even with data history.
Without Data History
We configure 20 tables and let Digna run for 1–3 months to monitor and analyze data quality issues in your data warehouses, lake, and Lakehouses.
Introducing Digna: AI Solution for Modern Data Quality
Every PoV and client interaction is a step forward in our journey to perfect data quality. With decades of experience battling data quality issues from data warehouses to data Lakes across various data-centric industries, I am proud to say that Digna is not just a product; it’s a promise to transform your data challenges into success stories.
In the face of daunting data challenges, Digna emerges as the beacon of hope, offering a suite of features to empower organizations:
Automated Machine Learning
Detecting and rectifying anomalies, trends, and patterns effortlessly.
Domain Agnostic
Adapting to your specific data landscape, irrespective of the industry, be it finance, healthcare, or retail.
Data Privacy
Safeguarding data quality initiatives without compromising privacy in the era of stringent data regulations.
Built to Scale
Growing seamlessly with your data infrastructure, from startups to enterprises, ensuring sustainability and reliability.
Real-time Radar
Instantaneous monitoring and issue resolution, preventing data glitches from impacting decision-making processes.
Choose Your Installation
Flexibility to deploy on the cloud or on-premises, aligning with your organization’s needs and security policies.
Join us on this journey to revolutionize the way you handle data. Let Digna be your partner in navigating the complex world of data quality.
Stay data-driven,
Marcin Chudeusz
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