*“Variety is the spice of life,”* they say. And variety is the spice of data also: adding rich texture and flavor to otherwise dull numbers. Variety ranks among the most exciting, interesting, and challenging aspects of big data. **Variety** is one of the original “**3 V’s of Big Data**” and is frequently mentioned in Big Data discussions, which focus too much attention on **Volume**.

A short conversation with many “old school technologists” these days too often involves them making the declaration: *“We’ve always done big data.”* That statement really irks me… for lots of reasons. I summarize in the following article some of those reasons: “Today’s Big Data is Not Yesterday’s Big Data.” In a nutshell, those statements focus almost entirely on Volume, which is really missing the whole point of big data (in my humble opinion)… here comes the Internet of Things… hold onto your bits!

The greatest challenges and the most interesting aspects of big data appear in high-**Velocity** Big Data (requiring fast real-time analytics) and high-**Variety** Big Data (enabling the discovery of interesting patterns, trends, correlations, and features in high-dimensional spaces). Maybe because of my training as an astrophysicist, or maybe because scientific curiosity is a natural human characteristic, I love exploring features in multi-dimensional parameter spaces for interesting discoveries, and so should you!

Dimension reduction is a critical component of any solution dealing with high-variety (high-dimensional) data. Being able to sift through a mountain of data efficiently in order to find the key predictive, descriptive, and indicative features of the collection is a fundamental required data science capability for coping with Big Data.

Identifying the most interesting dimensions of the data is especially valuable when visualizing high-dimensional data. There is a *“good news, bad news”* perspective here. First, the *bad news*: the human capacity for seeing multiple dimensions is very limited: 3 or 4 dimensions are manageable; and 5 or 6 dimensions are possible; but more dimensions are difficult-to-impossible to assimilate. Now for the *good news*: the human cognitive ability to detect patterns, anomalies, changes, or other “features” in a large complex “scene” surpasses most computer algorithms for speed and effectiveness. In this case, a “scene” refers to any small-n projection of a larger-N parameter space of variables.

In data visualization, a systematic ordered parameter sweep through an ensemble of small-n projections (scenes) is often referred to as a “grand tour”, which allows a human viewer of the visualization sequence to see quickly any patterns or trends or anomalies in the large-N parameter space. Even such “grand tours” can miss salient (explanatory) features of the data, especially when the ratio N/n is large. Consequently, a data analytics approach that combines the best of both worlds (machine vision algorithms and human perception) will enable efficient and effective exploration of large high-dimensional data.

One such approach is to use statistical and machine learning techniques to develop “interestingness metrics” for high-variety data sets. As such algorithms are applied to the data (in parameter sweeps or grand tours), they can discover and then present to the data end-user the most interesting and informative features (or combinations of features) in high-dimensional data: “Numbers are powerful, especially in interesting combinations.”

The outcomes of such exploratory data analyses are even more enhanced when the analytics tool ranks the output models (*e.g.,* the data’s “most interesting parameters”) in order of significance and explanatory power (*i.e.,* their ability to “explain” the complex high-dimensional patterns in the data). Soft10’s “automatic statistician” Dr. Mo is a fast predictive analytics software package for exploring complex high-dimensional (high-variety) data. Dr. Mo’s proprietary modeling and analytics techniques have been applied across many application domains, including medicine and health, finance, customer analytics, target marketing, nonprofits, membership services, and more. Check out Dr. Mo at http://soft10ware.com/ and read more here: http://soft10ware.com/big-data-complexity-requires-fast-modeling-technology/

Kirk Borne is a member of the Soft10, Inc. Board of Advisors.

Follow Kirk Borne on Twitter @KirkDBorne