Outliers, Inliers, and Other Surprises that Fly from your Data

Data can fly beyond the bounds of our models and our expectations in surprising and interesting ways. When data fly in these ways, we often find new insights and new value about the people, products, and processes that our data sources are tracking. Here are 4 simple examples of surprises that can fly from our data:

(1) Outliers — when data points are several standard deviations from the mean of your data distribution, these are traditional data outliers. These may signal at least 3 possible causes: (a) a data measurement problem (in the sensor); (b) a data processing problem (in the data pipeline); or (c) an amazing unexpected discovery about your data items. The first two causes are data quality issues that must be addressed and repaired. The latter case (when your data fly outside the bounds of your expectations) is golden and worthy of deeper exploration.

(2) Inliers — sometimes your data have constraints (business rules) that are inviolable (e.g., Fraction of customers that are Male + Fraction of customers that are Female = 1). A simple business example would be: Profit = Revenue minus Costs. Suppose an analyst examines these 3 numbers (Profit, Revenue, Costs) for many different entries in his business database, and he finds a data entry that is near the mean of the distribution for each of those 3 numbers. It appears (at first glance) that this entry is perfectly normal (an inlier, not an outlier), but in fact it might violate the above business rule. In that case, there is definitely a problem with these numbers — they have “flown” outside the bounds of the business rule.

(3) Nonlinear correlations — fitting a curve y=F(x) through data for the purpose of estimating values of y for new values of x is called regression. This is also an example of Predictive Analytics (we can predict future values based upon a function that was learned from the historical training data). When using higher-order functions for F(x) (especially polynomial functions), we must remember that the curves often diverge (to extreme values) beyond the range of the known data points that were used to learn the function. Such an extrapolation of the regression curve could lead to predictive outcomes that make no sense, because they fly far beyond reasonable values of our data parameters.

(4) Uplift — when two events occur together more frequently than you would expect from random chance, then their mutual dependence causes uplift. Statistical lift is simply measured by: P(X,Y)/[P(X)P(Y)]. The numerator P(X,Y) represents the joint frequency of two events X and Y co-occurring simultaneously. The denominator represents the probability that the two events X and Y will co-occur (at the same time) at random. If X and Y are completely independent events, then the numerator will equal the denominator – in that case (mutual independence), the uplift equals 1 (i.e., no lift). Conversely, if there is a higher than random co-occurrence of X and Y, then the statistical lift flies to values that are greater than 1 — that’s uplift! And that’s interesting. Cases with significant uplift can be marketing gold for your organization: in customer recommendation engines, in fraud detection, in targeted marketing campaigns, in community detection within social networks, or in mining electronic health records for adverse drug interactions and side effects.

These and other such instances of high-flying data are increasingly challenging to identify in the era of big data: high volume and high variety produce big computational challenges in searching for data that fly in interesting directions (especially in complex high-dimensional data sets). To achieve efficient and effective discovery in these cases, fast automatic statistical modeling can help. For this purpose, I recommend that you check out the analytics solutions from the fast automatic modeling folks at http://soft10ware.com/.

The Soft10 software package is trained to report automatically the most significant, informative and interesting dependencies in your data, no matter which way the data fly.

(Read the full blog, with more details for the 4 cases listed above, at: https://www.linkedin.com/pulse/when-data-fly-kirk-borne)

Follow Kirk Borne on Twitter @KirkDBorne

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