MQnomix® – Hotspots

Why do you need Hotspots MQnomics® ?

That’s a simple one! You need it because our solution provides Data Intelligence that translates information into lower risk and more predictable cash-flows for your business.


But how Hotspots MQnomics® defines Data Intelligence ?

In simple terms, it is about real time data that lets you know where and how your business’ landscape is changing and by whom.

Moreover, it is

reliableaccurate and can be retrieved in a predictable and consistent way;

relevant – provides the right type of timely data for superior business decisions making;

intuitive – the information contained in the report is easy to understand, including the causality effects it generates.

Not only this, but there is also a significant difference that real time data can make in regard to Decision Modeling and, as we all know by now, superior decision-making is a must-have strategic capability!

One thing is for sure. All over the world, the era of digital economy generates a profound impact upon the type and speed of changes in business models. And for you to be ahead of the curve, having access to real-time data that, subsequently, are used for decision modeling, is paramount.

A reminder of how important are timely and relevant data in the decision-making process is a high-profile firm’s quest for a new site selection . The first thing to observe here is that the most important variable considered was the local workforce.

Savvy real estate investors, lenders and underwriters were quick to realize the significance of this variable and, in particular, the workforce’s real time characteristics like:



and pay-scale (weighted-average or percentile values, where available).

As a result, when deciding to invest in a real estate project, where to build a new office, or for the purpose of assessing the credit risk of a new commercial/ residential real estate loan, professionals explicitly need these data.

The above-mentioned proxies for business viability, in conjunction with Monte Carlo simulation variability and uncertainty modeling , help business entities to define objective data-based probability and uncertainty distributions for investment’s financial and operational ratios .

How do we make it happen?

Our proprietary data collection algorithms are able to read scores of publicly available data, encompassing active industries and trades, over a customizable period of time.

Then, unsupervised learning algorithms generate the most representative Clusters, that is, areas with the highest density of firms that hire (Fig.1).

Fig.1_ Clusters Map Stats

Simply put, these Clusters bring to light highly relevant areas within larger regions (i.e., major cities, counties, etc.), where the economic activity is the most dynamic. Technically speaking, the final Clusters’ solution is based upon hundreds of initial starting ‘seeds’, whereby the minimum RMSE (root mean square error) value is chosen from all possible solutions/scenarios.

What’s in it for you?

It is about Knowledge that makes the difference. Actually, this is the place where things are getting really interesting… for you!

It consists of an optimized  Clusters Map for the region of your choice (Fig.2) and its corresponding statistics.

Fig.2 Clusters Map & Shape

An alternative to Clusters Map is Clusters Heat Map (Fig.3).

Fig.3 Heat Map

The most important stats and facts covered in this report are:

for each Cluster, the Area and Number of business entities that contribute to the job creation process. Optionally, the business entities names and trades can be added,

the Density of business entities for each Cluster within the region of your interest. The higher the density, the more appealing the Cluster should be,

for each Cluster, the closest neighbor Cluster to it is determined. The information reveals the potential competitor/alternative Cluster to your initial preference,

to see which Cluster is more homogenous and, hence, with more business potential, the RMSSTD is computed. That is, based upon the business entities assigned by the algorithm to each Cluster, the typical distance from the Cluster’s center is computed.

In our example from Fig.1, based on the combination of values from DENSITY and RMSSTD columns, the most important Clusters are 2 and 9.
Whether it is for

site selection,


credit risk insurance,

real estate loans analysis,

all of the above stats, together with ZIP codes, are important. So, based on these criteria, but also on the diversification, depth and pay-scale (where available) of the workforce, you can decide which Clusters can better fit your business interests.

Contact us to start making data intelligence-based decisions!

Share This