Data quality discussions often miss a critical distinction. Yes, there’s plenty of genuinely bad data that needs cleaning and validation. But there’s also a widespread problem of using the wrong data to answer questions it cannot answer - then dismissing entire datasets when that use case fails.

This creates two costly mistakes. First, organizations discard potentially valuable data because one application didn’t work. Second, they fall into the “big data fallacy” - believing more data automatically fixes quality issues, when it can actually amplify existing errors and biases.

The solution isn’t more data or perfect data. It’s understanding that the same dataset can have excellent and terrible use cases depending on the question asked. When you understand the nature and direction of errors across multiple datasets, geography, and time periods, sophisticated models can learn to extract value from what appears to be flawed information.

Understanding Data Characteristics vs Data Applications

Accuracy measures how close data points are to the true value. Precision measures how close repeated measurements are to each other.* This creates the key insight: low precision, high accuracy data varies widely but averages correctly over time - assuming the errors are unbiased and don’t systematically lean in one direction.

For point-in-time benchmarking, this creates problems - you’re benchmarking against noise. But for time-series prediction, the same scattered data becomes valuable because algorithms can learn patterns from thousands of observations.

*Note: In some statistical contexts, “accuracy” is referred to as “trueness,” and overall accuracy is described as a combination of precision and trueness.

The Single-Signal Problem

Early navigators relied on single reference points - the peak of an island on the horizon, a morning star, or a distinctive coastline feature. This worked in ideal conditions but failed catastrophically when fog obscured the reference, currents caused drift, or storms knocked ships off course. No backup meant no way to detect or correct errors.

Business Applications: We do the same thing analytically. We traditionally look at discrete lagging signals - like last quarter’s customer satisfaction scores, same store sales and traffic, marketing spend effectiveness. Even though there are multiple metrics, they’re analyzed independently rather than modeled jointly, and we’re left to manually process potential attribution between them. When one signal shows problems, we often don’t know if it’s noise or a real trend until it’s too late to respond.

The Navigation Solution: Analytical Triangulation

Multi-point navigation was an early advancement that enabled the age of exploration. Celestial navigation - using multiple stars, sun positions, and compass bearings simultaneously - changed the world by enabling reliable ocean travel. Later, satellites with advanced triangulation incorporated Einstein’s theory of relativity to make modern GPS possible.

GPS demonstrates sophisticated triangulation in action. It uses several satellites simultaneously (technically called trilateration, but we’ll use “triangulation” for the broader concept), delivering accurate positioning even when individual signals fail. As satellite geometry changes, the receiver continuously calculates position using different combinations of available satellites, with ground networks providing corrections and integrity checks. The truth is that precision and accuracy are constantly shifting, but we keep using it. Maybe we’re not ready to let GPS automatically land planes or drive cars for us, but we trust it for countless navigation decisions because multiple imperfect signals create reliable positioning.

Business Analytics and Applied Triangulation: The parallel is multivariate models that process many variables in a complex world instead of one signal at a time. Like satellites shifting to land antennae to wifi hubs, models with different techniques, data inputs, weights, and update frequencies can estimate where you are, how you got there, and where you’re likely going next given the economic headwinds, currents, and obstacles ahead.

This approach also helps sort what’s controllable from what’s not - distinguishing between market forces you must navigate around and competitive factors you can influence.

Business analytics should evolve the same way. Instead of single-signal approaches, organizations need triangulation using multiple analytical signals.

At each decision point, triangulate using three signals: predictive (where you’ll end up), explanatory (why you’re moving this direction), and contextual (local conditions affecting this trajectory).

Like GPS satellites, individual signals may be imperfect, but the triangulated position provides sufficient confidence to navigate forward. The key is using multiple imperfect signals rather than relying on one perfect source.

From Triangulation to Simulation

The power of triangulation becomes clear when you move beyond individual waypoints to understanding entire journeys. Once you’ve established multiple decision points through triangulation, simulation systems reconstruct the patterns to understand systemic behavior.

Simulation examines how competitive dynamics affected different locations, how seasonal patterns interacted with economic factors, and how various signals contributed to system behavior over time. This creates a comprehensive model that can test scenarios: “If competitor X launches a promotion in Q2, how will that interact with seasonal patterns at each location?”

Consider mobile device location data for foot traffic analysis. For point-in-time benchmarking, it’s problematic - sample sizes vary, device penetration differs, and distinguishing actual changes from measurement noise is difficult. As a single-signal data point it would be easy to dismiss as unusable.

In a triangulated approach, mobile data becomes one signal among several. Combined with credit card transaction samples and actual sales data, sophisticated models learn the systematic errors in each dataset and triangulate toward more accurate predictions than any single perfect source could provide.

Then simulation systems use this triangulated understanding to model “what if” scenarios across different time periods, locations, and market conditions. The same data that couldn’t reliably count Tuesday’s customers becomes the foundation for sophisticated market share analysis and competitive intelligence.

The Competitive Advantage

The mathematics for triangulation already exists. The limiting factor is organizational ability to adapt to the rapidly changing technology and methods.

The competitive advantage belongs to organizations that triangulate with existing signals, then simulate forward to test strategic scenarios. Most won’t, which creates opportunity for those who embrace navigational sophistication over analytical simplicity.

The additional benefit is that these models aren’t just simulation tools - they’re continuously learning systems. Early adopters will not only gain immediate advantage but those who build systems with enterprise memory will benefit from compounding returns as the models learn from each decision cycle, market response, and outcome. The gap between sophisticated and traditional analytics widens over time as learning systems compound their accuracy advantage.

___

The precision versus accuracy framework is just the beginning. The real insight: the same data can be useless for single-signal analysis and transformative for multi-signal triangulation. The difference lies in analytical sophistication, not data quality. To learn more visit