Brochure

Square

COMPUTATIONAL DATANOTES

Finding the Value in YOUR Data

Your company has a huge repository of data – market segment statistics, products, sales history, client and prospect profiles, client service reports, financial reports – and much of it is different, not comparable and on incompatible databases. This asset is often unrecognised for its value to YOUR business. These data structures are complex, multi faceted, unstructured multi-modal, from different sources and different formats.

For many businesses, data is an untapped resource; an undervalued company asset. Computational DataNotes is the key to unlocking the value of YOUR data.

Computational DataNotes computes and uncovers the hidden nascent structures of the data. Our novel algorithms extract data pattern structures that have meaningful value for the business. Patterns that have usefulness for customized business planning and strategy.

Data incomplete? Missing Values?   CDN can help with that

We resolve this data barrier for your business. Our algorithms learn from your data and Synthesize Missing Values by Learning Distribution from existing data. Example: Boston Housing appraisal Excel datasheets missing values for some rows. Learn Distribution learns the distribution for particular Columns: computes and replaces the missing data values
Certain rows missing data
Missing computed

DataNotes?  Better Visualization of Data

A single independent unit of computation, which renders the intricate and hidden aspects of your data with the most advanced Scientific Visualization. Datanotes structure computational elements that are deployed as easily accessible web objects.
Wind Turbine Analytics
Example: A utility company’s turbine generates electricity, yet the most worrisome parameter of the gigantic turbine is the Gearbox temperatures to avoid permanent damage to the machine.The DataNote incorporates Adaptive Learning Prediction functions as the core of its Alarm to warn management of Temperature anomalies.
K-NN Nearest Neighbours Algorithm
Example: Interactive algorithms e.g. K-NN empowers management to review the Similarity of their current data with past data.  Algorithms look for past reference points to understand the present behaviour of the systems by learning from the past behaviour. (Note: There are a few seconds of delay for the interactive to load) DataNotes are periodically computed according to management’s specifications.

Unlock the Wealth in Your Data. Evaluate the Potential 

Machine Learning (ML): is a method of data analysis that automates analytical model-building.  It is a branch of artificial intelligence based on the construct that systems can learn from data, identify patterns and make decisions with minimal human supervision. Example: 2D x-ray or optical or radio images of pipes and welding are classified by advanced ensemble of learning algorithms to aid the management of a major oil refinery company. Example: 2D images of damaged eroded or corroded surfaces are transformed into 3D landscapes endued with peaks and valleys by means of an advanced Neural Network learning algorithm that learns from landscape images most familiar to human visual processing. This allows management of a refinery to have a more natural view of pipes and welding surfaces and better language for technical discourse.

Definition of Artificial Intelligence and Machine Learning:

Artificial Intelligence AI: Mimics some behaviour of humans. For example, an element or component of AI is Natural Language Processing that writes analysis as if it was written by a human. Machine Learning (ML):  Adaptive learning from examples e.g. we do not know how a dynamical system works and want to determine the specifics of this dynamical system.  ML learns from samples and provides insights and inferences.  Systems can learn from data, identify patterns and make decisions with minimal human intervention.

How to get started with Computational DataNotes

CDN has the expertise and experience analyzing different types of data.  Working collaboratively with clients, the first step is to see if there is a healthy fit for both parties.  A preliminary exploration and discussion is vital to explore the sources of data you have and the types of questions you would like the data to solve.

Also we want to make sure you understand our strict Code of Ethics (please read below) as there are professional and ethical standards we CDN adheres to.  We are highly selective about the clients we choose to work with – there are some business sectors CDN will not engage with.

Code of Ethics

  • Ownership of data remains with the client.
  • CDN retains the confidentiality and proprietary nature of client data.
  • Client data is not sold to or otherwise utilised by third parties.
  • CDN provides accurate analysis of client data and does not alter or otherwise change the analysis of client data.
  • CDN provides reports and analysis based on client data; not legal or financial services based on the data. CDN does not advise.
  • Based on the data provided, CDN’s predict functions provide a percentage level estimate of accuracy for each individual project.
  • AI and ML Algorithm are not perfect they are limited in their functions.   Accuracy and efficiency rates vary based on the amount and quality of the data being analyzed and the types of questions that are being asked from the data.
  • All work and output are AS IS and CDN only reports the results of the computations and guarantees no outcomes.