Data forensics powered by AI
Data forensics powered by AI

Looking at data in the correct way is crucial in solving a myriad of business and non-business matters. Insight and the uncovering of hidden data was used to solve a critical issue that affected the outcome of World War II.

During aerial conflict, fighter planes would come back from battle with bullet holes. The Allies found the areas that were most commonly hit by enemy fire. They sought to strengthen the most commonly damaged parts of the planes to reduce the number that was shot down.

A mathematician, Abraham Wald, pointed out that perhaps there was another way to look at the data. Perhaps the reason certain areas of the planes weren't covered in bullet holes was that planes that were shot in those areas did not return. This insight led to the armor being re-enforced on the parts of the plane where there were no bullet holes.

The story behind the data is arguably more important than the data itself. Or more precisely, the reason behind why we are missing certain pieces of data may be more meaningful than the data we have.

Data drives businesses today. Businesses need DATA FORENSICS to run efficiently and optimally.

DATA FORENSICS (data analysis, data mining ("DM"), and artificial intelligence ("AI"):


Ø  Analyzes large amounts of raw data

Ø  Turns raw data into actionable insight to optimize business decisions and performance

Ø  Uses multi-dimensional hierarchical reduction to eliminate irrelevant material from the discovery process

Ø  Allows for the discovery of relationships, patterns, and trends across a dataset to be able to craft a strategic direction or solve a specific problem

Ø  Can be used to uncover HIDDEN DATA (reveals answers to questions and insights that managers did not know to ask)

Ø Enables you to find a "needle in a haystack"

Examples of the use of DM combined with AI

Healthcare (hidden relationship): DM+AI is used to assess the relationship between symptoms and a specific disease; how often is that symptom mentioned in relationship to a disease. A high correlation between the symptom and mentions of the disease could help identify the best drug to treat that disease.

Healthcare (discoverability): If you were looking for a link between carcinogens from tobacco and a specific type of cancer such as lung cancer. A 'traditional' search could provide data that mentions carcinogens, tobacco and/or lung cancer. Using DM+AI techniques, you could retrieve information where only specific carcinogens have an effect on the lungs.

Healthcare (pattern discernment): can be used to match the biochemical properties of molecules to a viral protein's properties in order to identify a molecule likely to bind to the virus.

ALBA Consultants have used their DM+AI technology to identify at-risk patients (two patents on this algorithm) for a large managed care organization, predicted future hall of fame sports figures, and uncovered a "needle in a haystack" effect from the usage of a certain drug. They have also uncovered an oddity at a hospital: why a disproportionate number of patients were older than 100 years.

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