Why do some companies succeed while others fail?

This challenging question is at the very basis of my research interests and agenda as a Doctoral Candidate, at Cardiff Business School, previously a masters student at Cambridge University 

My areas of research include

Organizational Performance

Technology Adoption and Impact

My MAIN Research Question Therefore is:

Can the performance of a single organization be predicted using ML-based predictive diagnostics?

Sub Research Questions include



Such a large area of research clearly requires scoping to make it far more manageable. I have decided to scope the organizational type to public companies in the Financial Services industry, a domain I have worked in now for close to two decades.  These two constraints greatly reduce the population to a more reasonable scope, and should allow me to establish a strong basis upon which other domains can be explored.


Factor and KPI Research, Quantification for Analysis

The research methodology I have selected starts with the development of measures for analysis of a wide array of factors which I hypothesize have an impact on company performance. This is derived from academic and industry literature as well as my personal experience of 25 years as an industry executive. Alongside this, I am developing the KPIs, the outcomes which are indicative of success and failure, against which the factors can be evaluated.

Many of the factors have traditionally been very hard to quantify and measure. As an example, how do you quantify the talent of an organization? The literature is very sparse in this realm, and is referenced as such. So, how does one measure the unmeasurable? This is part of the work I am undertaking, initial results of which can be seen on the Talent Analytics page.

Data Gathering

The data sources for this research are comprised, first of all, of publicly available information.

Brightdata.com through their BrightData.org research support program, is providing the necessary public information to support the analysis.  

Other data includes public company performance information from Wharton Research Data Services.

Data Analysis

Data Analysis is being performed using a variety of tools, including those from Qlik.com, Dataiku.com, Google Cloud Platform and more. The analytical techniques being used vary based on the needs, and include various forms of regressions, Natural Language Processing (NLP), k-means  and KNN for clustering, Random Forest and many other supervised and unsupervised methods.

Use Cases

As a practicing academic, my interests lie in the application of my research to organizations and their ecosystem. Some key use cases resulting directly from my research include:

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