Organizations are investing heavily in analytics and AI, but those systems still inherit the quality of the information underneath them. $12.9 million per year on average is Gartner’s estimate of the cost of poor data quality to organizations. At the same time, 26% of organizational data is considered “untrustworthy” by data and analytics leaders, while 54% of business leaders are not fully confident that the data they need is accessible.
For Nathan Koengeter, those numbers describe both the promise and the constraint of data-driven business. He has worked across enterprise data integration, data quality, analytics, market research and business intelligence. His experience has repeatedly reinforced the same principle: information does not improve a decision merely because it exists. It has to be reliable enough to trust, relevant enough to the question and clear enough for somebody to act on it.
Bad Inputs Create Confident Mistakes
Poor data rarely announces itself. A duplicated record, inconsistent format, missing field or broken feed can travel quietly through a system and appear later in a polished dashboard.
Nathan Koengeter has spent years working at that less-visible end of analytics. His approved professional materials describe Nathan Koengeter parsing and cleaning incoming data files, identifying inconsistencies and working with technical and client teams to correct recurring problems.
Nathan Koengeter summarizes the principle as “garbage in, garbage out.” The phrase predates modern AI, but the consequence is more immediate now. An organization can generate analysis faster than ever. It can also generate a wrong answer faster than ever.
Good Data Starts Before the Dashboard
One of the more important changes in Nathan Koengeter’s data work was moving from repeated cleanup toward improving how information arrived in the first place.
Nathan Koengeter helped standardize inputs, train clients around cleaner submissions, validate information earlier in the workflow and automate repetitive quality-control tasks. The goal was to prevent recurring errors rather than becoming more efficient at repairing the same errors indefinitely.
Gartner’s cost estimate helps explain why Nathan Koengeter treats data quality as an operating issue rather than technical housekeeping. The cost appears through rework, reporting disputes, inaccurate targeting, delayed decisions and missed opportunities.
Trust Changes Decision Speed
Good data affects more than accuracy. Nathan Koengeter also sees data quality as a speed issue. When teams trust a metric, they can debate the decision. When they do not, the meeting begins with reconciliation.
about 25% annually is the rate at which Salesforce says organizational data volumes are growing. More information can create more friction if definitions, governance, validation and integration do not keep pace.
Nathan Koengeter’s work in integration and validation is aimed at closing that trust gap upstream. The less time an organization spends arguing about whose spreadsheet is correct, the more time it can spend deciding what to do.
Clean Data Still Needs Translation
Reliable information is necessary, but Nathan Koengeter does not treat data quality as the finish line. Leaders can be buried under accurate information just as easily as inaccurate information.
A second part of Nathan Koengeter’s career has involved translating complex analytical material into practical business insight. Organizations do not earn a return from storing data. They earn a return when the evidence changes an allocation, process, product or strategy.
According to research fromMcKinsey, companies that make extensive use of customer analytics are 23 times more likely to outperform competitors in customer acquisition and nearly 19 times more likely to achieve above-average profitability than companies that do not. The point is not that analytics automatically creates those outcomes. It is that organizations able to use information well can build a meaningful operating advantage.
Nathan Koengeter’s contribution sits in that connection between data quality and usability. A technically correct analysis that never reaches a decision-maker in a useful form has limited value. So does a beautifully presented recommendation built on unreliable inputs.
More Data Can Make the Problem Harder
Modern organizations generate information continuously through transactions, surveys, customer interactions, marketing platforms, financial systems and connected applications.
Nathan Koengeter sees a compounding risk inside that growth. If definitions, ownership, validation and integration do not scale with volume, more data can produce more inconsistency rather than more clarity. That is why Nathan Koengeter emphasizes structured intake, standardization, repeatable validation and automation.
The Decision Is the Final Quality Test
Nathan Koengeter’s combination of analytics experience and business training also keeps data quality tied to context. A dataset can be accurate and still be too old. It can be complete and still measure the wrong thing. It can be statistically sophisticated and still fail to answer the decision management faces.
For Nathan Koengeter, the strongest analytical process starts with the decision and works backward. What does the organization need to know? How reliable must the answer be? Which sources can support it? What uncertainty remains?
Nathan Koengeter’s career has spanned the chain from direct research and data collection to integration, quality control, analysis and communication. Across those stages, he has returned to the same operating principle: the quality of a business decision cannot be separated from the quality of the information used to make it.
AI may change the tools and dashboards may change how quickly information appears. Nathan Koengeter’s central point remains durable. Reliable inputs create trustworthy analysis. Trustworthy analysis makes choices clearer. Better choices are where good data finally becomes valuable.
