How to Address Data Integrity Issues

It starts with one missing value, one duplicate row… and suddenly your entire system can’t be trusted. Because data issues don’t fail loudly. They compound silently. Here’s what keeps pipelines reliable 👇 - Null value checks Missing fields in key columns can quietly break logic and downstream outputs. - Duplicate checks Repeated records distort metrics, models, and business decisions. - Primary key validation Every record must be unique, or nothing stays consistent. - Referential integrity Broken relationships between tables lead to incorrect joins and insights. - Data type & format validation Wrong formats or types cause subtle but costly errors. - Range & outlier checks Values outside expected limits often signal deeper issues. - Freshness & volume checks Unexpected delays or spikes usually point to upstream failures. - Schema change detection Even small structural changes can break entire pipelines. - Distribution drift checks Data patterns shifting over time can silently degrade models. - Business rule validation If domain logic breaks, the output becomes unreliable. - Aggregation & historical checks Totals and trends must stay consistent across layers and over time. Data quality issues don’t crash systems. They corrupt them. What’s the one check your pipeline is missing right now? Follow Sumit Gupta for more such insights!!

Data Quality isn't boring, its the backbone to data outcomes! Let's dive into some real-world examples that highlight why these six dimensions of data quality are crucial in our day-to-day work. 1. Accuracy:  I once worked on a retail system where a misplaced minus sign in the ETL process led to inventory levels being subtracted instead of added. The result? A dashboard showing negative inventory, causing chaos in the supply chain and a very confused warehouse team. This small error highlighted how critical accuracy is in data processing. 2. Consistency: In a multi-cloud environment, we had customer data stored in AWS and GCP. The AWS system used 'customer_id' while GCP used 'cust_id'. This inconsistency led to mismatched records and duplicate customer entries. Standardizing field names across platforms saved us countless hours of data reconciliation and improved our data integrity significantly. 3. Completeness: At a financial services company, we were building a credit risk assessment model. We noticed the model was unexpectedly approving high-risk applicants. Upon investigation, we found that many customer profiles had incomplete income data exposing the company to significant financial losses. 4. Timeliness: Consider a real-time fraud detection system for a large bank. Every transaction is analyzed for potential fraud within milliseconds. One day, we noticed a spike in fraudulent transactions slipping through our defenses. We discovered that our real-time data stream was experiencing intermittent delays of up to 2 minutes. By the time some transactions were analyzed, the fraudsters had already moved on to their next target. 5. Uniqueness: A healthcare system I worked on had duplicate patient records due to slight variations in name spelling or date format. This not only wasted storage but, more critically, could have led to dangerous situations like conflicting medical histories. Ensuring data uniqueness was not just about efficiency; it was a matter of patient safety. 6. Validity: In a financial reporting system, we once had a rogue data entry that put a company's revenue in billions instead of millions. The invalid data passed through several layers before causing a major scare in the quarterly report. Implementing strict data validation rules at ingestion saved us from potential regulatory issues. Remember, as data engineers, we're not just moving data from A to B. We're the guardians of data integrity. So next time someone calls data quality boring, remind them: without it, we'd be building castles on quicksand. It's not just about clean data; it's about trust, efficiency, and ultimately, the success of every data-driven decision our organizations make. It's the invisible force keeping our data-driven world from descending into chaos, as well depicted by Dylan Anderson #data #engineering #dataquality #datastrategy

As information security practitioners, we are entrusted with the critical responsibility of protecting the confidentiality, integrity, and availability (CIA) of data. While each component of the CIA triad is essential, today I want to focus on the importance of INTEGRITY and why it must never be overlooked.  Confidentiality ensures that sensitive information is accessed only by authorised personnel. Availability guarantees that the information and systems are accessible to those who need them when they need them. INTEGRITY ensures that data remains accurate, consistent, and trustworthy throughout its lifecycle. It is the foundation upon which critical decisions are made, from patient care to financial transactions. When integrity is compromised, the consequences can be devastating. Take the tragic case of the Therac-25 medical radiation incidents. 💉 Between 1985 and 1987, six patients suffered severe radiation overdoses due to a combination of software bugs and design flaws in the Therac-25 machine. These incidents highlight the dire consequences of failing to maintain the integrity of systems and data. Read more about the incident here: https://lnkd.in/gey8kk4c To uphold integrity, consider these actionable steps: 🔶 Tighten access controls and authentication mechanisms 🔶Rigorously test and validate systems before any update goes live—lessons learned from Therac-25 🔶 Establish Secure System Configurations (system hardening, regular patches, monitor systems, etc.) 🔶 Deploy Detective Controls (system audits, file integrity checkers, and antivirus systems to identify and alert on unauthorised changes) 🔶 Establish clear incident response and recovery procedures  🔶And importantly, cultivate a culture of integrity. Set the standard high and lead by example, emphasising integrity in every decision In the private sector, compromised integrity can lead to financial losses, reputational damage, and legal liabilities. Imagine the chaos that would ensue if a bank's transaction records were altered or corrupted. In the public sector, the stakes are even higher. Inaccurate or tampered data could lead to miscarriages of justice, compromised national security, or erosion of public trust.  #IntegrityMatters #CIATriad #InfoSecEssentials

If data quality is everyone's job, it's no one's priority. When a business misses its revenue targets or botches a product launch, the root cause is often easy to spot: unclear goals, flawed strategy, or poor execution. But when data goes wrong—when reports are unreliable, customer insights are murky, or machine-learning models misfire—the culprit is usually harder to pin down. Everyone was supposed to care about data quality, but no one really did. This is the hidden cost of "data quality is everyone's responsibility"—a mantra that sounds wise but often means data is no one's priority in day-to-day business. When employees are busy tackling urgent tasks—closing deals, shipping products, fixing bugs—they don't prioritize data quality. After all, data quality issues rarely explode in real time. Like technical debt, they erode progress slowly, invisibly, until major initiatives stall, and the company is left wondering why its data-driven transformation never took off. Some businesses respond by pointing to their Chief Data Officer (CDO), expecting one powerful executive to fix the company's data problems. But this approach is only part of the solution. Data is created, used, and maintained everywhere across the business. A single executive, no matter how capable, can't overhaul a company's data culture from the top down. The real work of data integrity happens on the ground, within the teams that generate and use data daily. The real solution is to treat data like other critical business assets—finances, customer relationships, or brand reputation—and make senior leaders directly accountable for the data produced in their domains. Just as the CFO ensures accurate financial reporting and the head of sales owns customer satisfaction, department heads must be responsible for the quality, accessibility, and usability of their data. Their performance evaluations should reflect it. Data health metrics—like data accuracy, completeness, and cross-functional usability—should be tracked just as rigorously as sales targets or cost controls. When senior leaders know that part of their bonuses, promotions, and reputations hinge on clean, useful data, data responsibility moves from being a side project to core business work. Real progress begins when we stop treating data quality as a collective aspiration and start treating it as what it truly is: a core business function that demands clear ownership. When leaders stake their reputations on it, clean and reliable data becomes not just a technical requirement, but a fundamental measure of business success.

The only way to prevent data quality issues is by helping data consumers and producers communicate effectively BEFORE breaking changes are deployed. To do that, we must first acknowledge the reality of modern software engineering: 1. Data producers don’t know who is using their data and for what 2. Data producers don’t want to cause damage to others through their changes 3. Data producers do not want to be slowed down unnecessarily Next, we must acknowledge the reality of modern data engineering: 1. Data engineers can’t be a part of every conversation for every feature (there are too many) 2. Not every change is a breaking change 3. A significant number of data quality issues CAN be prevented if data engineers are involved in the conversation What these six points imply is the following: If data producers, data consumers, and data engineers are all made aware that something will break before a change has deployed, it can resolve data quality through better communication without slowing anyone down while also building more awareness across the engineering organization. We are not talking about more meaningless alerts. The most essential piece of this puzzle is CONTEXT, communicated at the right time and place. Data producers: Should understand when they are making a breaking change, who they are impacting, and the cost to the business Data engineers: Should understand when a contract is about to be violated, the offending pull request, and the data producer making the change Data consumers: Should understand that their asset is about to be broken, how to plan for the change, or escalate if necessary The data contract is the technical mechanism to provide this context to each stakeholder in the data supply chain, facilitated through checks in the CI/CD workflow of source systems. These checks can be created by data engineers and data platform teams, just as security teams create similar checks to ensure Eng teams follow best practices! Data consumers can subscribe to contracts, just as software engineers can subscribe to GitHub repositories in order to be informed if something changes. But instead of being alerted on an arbitrary code change in a language they don’t know, they are alerted on breaking changes to the metadata which can be easily understood by all data practitioners. Data quality CAN be solved, but it won’t happen through better data pipelines or computationally efficient storage. It will happen by aligning the incentives of data producers and consumers through more effective communication. Good luck! #dataengineering

We need to fix the root causes of data quality issues, not just clean incorrect data. The cost of reappearing data quality issues will accumulate over time. If a data engineer needs to spend one day reviewing and fixing the same or a very similar data quality issue every month, that will result in 12 days of work after a year and two months after five years. Those recurring issues only indicate that there is a process issue in data collection or data management. If we identify the root cause of these issues, we can enhance the data platform's reliability and gain user trust. Some tasks cannot be automated, such as no tool can compel data stakeholders to engage in the process. However, we can present them with reports to confirm that the issue is real and feasible to be fixed. The root cause process for DQ issues is straightforward: 🔸Identify the problem 🔸Engage experts who can confirm it 🔸Collect all information that will describe the problem in detail, such as data samples 🔸Discuss the possible causes and pick the most reasonable one 🔸Implement a solution 🔸Confirm that the issue is solved by triggering data quality checks The most crucial step is the first one - you need to identify the problem enough to show it to the business users. They will be willing to invest in data quality if they see a value. #dataquality #datagovernance #dataengineering

🔍 Data Integrity (DI) Remediation & Validation in Biomanufacturing: Compliance is Non-Negotiable! In cGMP biomanufacturing, data integrity (DI) is the backbone of compliance. Without robust DI controls, the risk of regulatory scrutiny, product recalls, and patient safety issues escalates. Yet, many facilities still struggle with DI gaps, leading to FDA 483s, Warning Letters, and even Consent Decrees. So, how should organizations approach DI remediation and validation effectively? ⚠️ Common DI Pitfalls in Biomanufacturing ❌ Incomplete or altered records – Missing or manipulated batch records, audit trails, and electronic data raise red flags. ❌ Lack of ALCOA+ principles – Data must be Attributable, Legible, Contemporaneous, Original, and Accurate, plus Complete, Consistent, Enduring, and Available. ❌ Inadequate system controls – Poorly configured manufacturing execution systems (MES), laboratory information management systems (LIMS), and electronic batch records (EBRs) can compromise DI. ❌ Unvalidated data systems – Failure to validate computerized systems leads to unreliable data and regulatory noncompliance. 🔄 DI Remediation: A Risk-Based Approach A reactive approach to DI remediation is not enough. A well-structured DI remediation plan should include: ✅ Gap Assessment & Risk Prioritization – Identify DI gaps across paper-based and electronic systems. Prioritize remediation based on product impact and regulatory risk. ✅ Governance & Training – Establish DI policies, SOPs, and cross-functional training programs to embed a culture of DI compliance. ✅ Data Lifecycle Management – Implement controls for data generation, processing, storage, and retrieval to ensure compliance throughout the product lifecycle. ✅ Audit Trail Reviews & Exception Handling – Routine monitoring of electronic data trails to detect and correct DI issues before inspections. ✅ Periodic DI Assessments – Continuous review of DI controls through internal audits and self-inspections to maintain readiness. 📊 DI Validation: Ensuring Trustworthy Data Validation of GxP computerized systems ensures that data is reliable, accurate, and compliant. Key steps include: 🔹 System Risk Assessment – Categorize systems based on DI risk to determine validation effort. 🔹 21 CFR Part 11 Compliance – Ensure electronic signatures, access controls, and audit trails meet regulatory expectations. 🔹 IQ, OQ, PQ Execution – Verify system installation, operation, and performance meets DI requirements. 🔹 Periodic Review & Revalidation – Validate updates, patches, and system changes to maintain DI compliance over time. 🏆 DI Excellence = Compliance + Business Success A proactive DI strategy strengthens compliance, minimizes regulatory risk, and improves manufacturing efficiency. Organizations that invest in DI remediation and validation today will be the ones achieving inspection readiness and long-term success in biologics and cell & gene therapy manufacturing. #DataIntegrity #GMPCompliance