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Having the ability to harness and operationalise data at scale is fundamental to building AI applications. However with the vast amount of organised and unorganised data available, teams need solutions to enable the underlying data to successfully support A/IS use cases. Insufficient training data, lack of a data strategy, data quality, poor data governance, and misalignment to business priorities are among the most common reasons why AI projects fail. To combat these challenges and help our clients leverage the power of A/IS, we used our extensive data and A/IS experience and developed our proprietary AI Data Readiness Assessment tools.
Our tools help organisations accelerate data acquisition and processing of the data needed to develop effective prototypes and algorithms; ensure rules and processes are established detailing how the data is sourced, managed, accessed, and used across the business; develop a balanced dataset that has the required characteristics to address the use case, while minimising bias, and producing training data labelled with a high degree of accuracy; understand the origin of your data and the functions through which it passes, and structure your data appropriately to support access, speed, resilience and compliance objectives; optimise the accessibility of data, implement safeguards, and store and structure data optimally so it can be reused and shared; implement robust data management and security processes, and address data quality, ethics, privacy, and ownership requirements.
Evaluate contextual data available and retrievable about each behaviour being analysed/ feature being predicted.
Identify strengths and weaknesses and exploitable vulnerabilities within the data.
Develop datasets that are reproducible, interpretable, traceable, fair, and complete.
Understand the provenance of the data for effective use, and optimise accessibility
Address data quality, ethics, privacy, and ownership.
How to process the data needed to develop effective prototypes and algorithms.
Ensure appropriate use of data.
Establish an AI Data Strategy.
Complete AI Data Readiness Assessment using our proprietary AI Assessment Tools.
Accelerate the acquisition of data, and understand data generation methods.
Develop a high quality, labelled dataset, minimising bias and increasing accuracy.
Set access controls that set limitations around who can access the data, how data is accessed and how it might be used.
Establish internal security precautions as well as external safeguards to protect sensitive data.
Implement secure and robust data management procedures.
Establish a process for removing data which does not fit the required criteria of PII or meets data regulation requirements.
Establish diversity in the data/ project team, and identify any shortcomings in the data collection methodology.
During a Client engagement our Consultants pass through six key phases that form the anatomy of how we manage a typical consulting project. We appreciate every assignment is different, so we have flexible processes in place to bring our experts onboard quickly and effectively without disrupting existing work programmes. We are happy to provide our services at any stage of the lifecycle.
AI can be used by organisations to provide new goods and services, boost productivity, enhance competitiveness, ultimately leading to economic growth and better quality of life.
Learn more about how we can help you achieve your critical priorities: