Your Chief Virtual Learning Architect

Learning to Performance

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Custom Design & Delivery

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Predictive Evaluation

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Enterprise Learning Strategy

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Dave Basarab Consulting has elevated the training experience, offering an end-to-end comprehensive approach that includes training strategy, instructional design, development, delivery , post program training transfer, and evaluation (via the unique Predictive Evaluation methodology). Virtual Chief Architect Dave Basarab has combined all of these individual training elements with his user-friendly, comprehensive Learning to Performance approach, which significantly increases companies' training ROI.

Why we are different

Innovation: Our primary focus is creating learning programs using a Learning to Performance approach.
Expertise: Dave offers the depth and breadth of his experience, including working internally at prestigious companies (Motorola, Ingersoll Rand and Pitney Bowes) as well as his knowledge and expertise as a highly-respected, sought-after consultant.
Partnership: Basarab collaborates with companies, serving as their own Chief Learning Officer whenever they need to plan, strategize, or implement training initiatives.

Training Services

Enterprise Learning Strategy

Enterprise Learning Strategy

At Dave Basarab Consulting, we're experts in creating Enterprise Learning Plans. We work with you to develop your company's learning strategy and direction.

Custom Design, Development, & Delivery

Custom Design, Development, & Delivery

Custom training is an effective way of developing the capability required to execute your strategy. We are a custom design house that creates programs specific to your business.

Learning Burst Development

Learning Burst Development

We can create and deliver your courses via our unique Learning Burst Method - keeping your employees at their jobs while receiving world-class training.

Predictive Evaluation

Predictive Evaluation

We predict the ROI for your courses and establish success gates. We then evaluate the course against the success gates to show value realized and continually improve results.

Leadership Development

Leadership Development

Turn-key virtual custom leadership development program that combines world-class leadership speakers/educators with post-event personalized coaching to provide you with a cadre of highly skilled leaders.

Predictive Evaluation

Ethical Predictive Analytics for Better Employee Learning Decisions

Predictive analytics can help organisations decide which learning investments are likely to improve capability, performance and business outcomes. Used carefully, it can identify participation risks, anticipate skill gaps and show whether a programme is being adopted after delivery.

The ethical question is how these forecasts are made and applied. In Australia, employers must balance useful workforce insights with privacy obligations, employee trust, cultural considerations and fair treatment across a diverse labour market. A prediction should support a learning decision, not quietly become a judgement about a person’s value or future.

Approach	Productive use	Main ethical risk	Safeguard
Participation forecasting	Identifying teams that may need better access or manager support	Labelling employees as unmotivated	Use results to remove barriers
Skills analysis	Planning capability development	Overlooking informal or culturally different expertise	Combine data with human review
Impact measurement	Connecting learning with business results	Claiming causation from weak evidence	Define measures and limitations
Individual recommendations	Offering relevant learning pathways	Surveillance or unfair profiling	Give employees transparency and choice

Start With A Legitimate Purpose

A responsible analytics project begins with a clear business and learning purpose. Forecasting which locations may need additional coaching is different from predicting which individuals are unlikely to succeed. The first may guide resource allocation; the second can affect opportunity, reputation and promotion.

Organisations should document why data is being collected, what decision it will inform and what decisions are prohibited. A learning consultancy such as learning consultancy can help connect evaluation design with organisational objectives while keeping the boundaries of the analysis visible.

Use Relevant And Proportionate Data

Learning platforms can contain attendance, assessment results, completion rates, survey responses and manager observations. Other systems may add rosters, sales results or customer feedback. Combining these sources can produce useful patterns, yet every additional variable increases the chance of intrusion or misleading interpretation.

Under Australia’s Privacy Act 1988, organisations should consider whether collection is reasonably necessary, explain how personal information is handled and protect it from misuse. The Australian Privacy Principles are especially relevant when data is reused for a purpose employees did not expect. Data minimisation, access controls and retention limits should be designed before modelling begins.

Make Consent And Transparency Practical

Consent is meaningful when employees understand the arrangement in plain language. A policy buried in an intranet page will not adequately explain why a learning platform tracks activity, how long records are retained or whether managers can see individual results.

Useful transparency includes a short privacy notice, an explanation of prediction limits and a clear process for correcting inaccurate information. In a hybrid workforce spanning Sydney, Melbourne, Brisbane and regional locations, people may access training through different devices and schedules. Communication should work for shift workers, remote staff and employees with accessibility needs.

Test For Bias Before Acting

A model can reproduce historical inequality even when protected attributes are excluded. Attendance patterns may reflect caring responsibilities, disability, rostering, unreliable broadband or limited manager support rather than commitment to development. Australian workplaces also need to consider cultural safety for Aboriginal and Torres Strait Islander employees and the effects of assumptions built into assessment design.

Teams should compare prediction errors across relevant groups and inspect whether recommendations systematically favour particular roles, locations or employment types. Fairness testing should continue after deployment because workforce composition, course design and business priorities change.

Practical checks for a learning analytics model include:

Compare completion and recommendation accuracy across teams and employment arrangements
Review whether shift workers and part-time staff have equal access to learning
Examine missing data for signs of exclusion rather than low motivation
Test assessments for language, disability and cultural bias
Record who can challenge or override an automated recommendation

Keep Humans Accountable

Predictive outputs should inform professional judgement rather than replace it. A manager might use a forecast to offer coaching, protected study time or a different learning format. The same forecast should not automatically deny an employee a development opportunity or place them on a performance pathway.

Accountability needs named owners. Learning leaders, people and culture teams, data specialists and line managers should know who approves a model, who monitors it and who responds to complaints. This is consistent with the broader direction of Australian privacy reform and with employee expectations that workplace technology will remain explainable.

A sound governance arrangement defines:

The approved purpose and prohibited uses of each dataset
Roles for data stewardship, model review and employee support
Review intervals for accuracy, fairness and business relevance
Escalation procedures for disputed or harmful outcomes
Retention, deletion and access rules for individual records

Measure Impact Without Overclaiming

Training data can show whether employees attended, completed practice, passed an assessment or applied a skill. Business data may show changes in customer satisfaction, safety incidents, productivity or sales. These measures become more credible when the organisation specifies the expected pathway from learning activity to workplace result.

A methodology such as Predictive Evaluation can help forecast adoption, impact and return on investment, but forecasts remain estimates. Customer-facing teams in Australian retail, banking and hospitality may experience seasonal demand, new technology or staffing changes at the same time as training. Evaluation should therefore use comparison groups, manager evidence and time-series context where practical.

Build Trust Into Everyday Practice

Ethical use is experienced through ordinary actions: a manager explains why a recommendation appeared, an employee can correct a record, and a learning team reports uncertainty instead of presenting a score as fact. These habits matter in a market where employers compete for skilled people and where informal conversations quickly shape confidence in a workplace system.

For customer-facing teams, custom training guide shows why capability decisions should balance commercial aims with service quality. The same principle applies to predictive learning decisions: use evidence to create better support, preserve human agency and keep the final focus on fair opportunities to build capability.

A practical starting point is to write a one-page decision record before collecting data. State the purpose, permitted inputs, affected employees, fairness checks, human review process and success measures. That simple record turns predictive analytics from an opaque scoring exercise into a governed learning tool.

Learning to Performance

Learning to Performance, a complete training approach, gives companies world-class training to drive significant return. This approach includes upfront work (Impact Mapping, design), training (for staff and company executives), and post-training efforts to ensure training transfer. This unique recipe - the key for successful training and adoption - is changing the way companies implement training. This methodology could work with any content for organizations in any industry.