Prediction as a Service (PraaS)
Harness the power of AI and ML to drive predictive outcomes and propel your business.
• Lower the barrier of entry to AI
• Reduce the time to value
• Proveable Performance
• No Need for in-house Data Science
Problems we help solve
Customer Churn
Product Demand Forecast
Workforce Demand Forecast
Financial Forecast
AI Feasibility Studies
Understanding the predictive capabilities of your data
Artificial Intelligence (AI) Feasibility Studies are designed to allow businesses to quantify the performance they can achieve from implementing Machine Learning to aid a business use case.
Data gathering
& Analysis
Data
Engineering
Business
Performance
Production
Feasibility
AI Models require high quality data to perform, and these studies put an organisation’s data to the test, both in terms of quality and availability.
We follow a set of steps that take us from initially translating a business challenge into a predictive use case, to an end result where an organisation can see how implementing the solution can improve business outcomes. The key is to identify what would give your business an advantage if known earlier, to be able to define a ‘prediction target’.
The steps are:
1. Data Gathering & Analysis
Combining the various data sources and transforming them in a way which maximises the information available for the model, with Model Training & Analysis, which is where a range of AI models are trained and optimised to maximise performance. The ‘trust’ in each model is assessed using various performance metrics like model ‘bias’ (level of optimism / pessimism) and the degree of accuracy / inaccuracy.
2. Data Engineering
Within the organisation, users will have varying ability to read, understand, interpret and communicate data effectively.
3. Relating Model Performance to business outcomes
Further analysis is done on the best models at the AI training stage, checking their performance against business objectives.
4. Production Feasibility: How to make the prototype a reality
The steps taken to create the models are reviewed, so that automation can be implemented; loading data sources, transforming data, determining how and when the AI model will automatically be retrained, and how the predictions are delivered to end users in a meaningful way.
Through this assessment, we answer the fundamental question of whether the organisation’s data can prove an asset for predictive outcomes. It provides a roadmap for a productionised AI solution, with the confidence that the model is behaving correctly. In addition, the study highlights areas where the overall data strategy can be refined, improving data quality and availability, and with it improving predictive outcomes.
Choosing the right Data
When choosing the right datasources, both business subjectmatter experts and data scientistsplay a vital role.
Handling different Data Types
AI relies heavily on dataengineering quality and data feedsthat translate to meaningfulnumerics.
Driving Metrics
The required business outcomesneed to be the main driver ofmetrics.
Case Study
Companies today face a dilemma, realising the need for AI adoption to stay relevant in the long term, while being presented with heavy investment decisions to implement prior to proving value.
Whether heavy investment in a platform or Data Science team, the time and financial cost of entry is often huge.
This high barrier to entry is causing companies to stall the development of this critical capability, and the costs down the road will be huge.
With Data Technology's Prediction as a Service, we remove the need for in-house Data Science teams, and through our feasibility studies we give our clients the confidence of success before committing to the development and management of a production model.