• Written by Azhar Aliyar, Director

A research manager reviews the test results for a new product, trying to understand why it failed to meet the required standards. She uses AsscherAi to query the enterprise data, but the results are unclear because the field names in the database are ambiguous. This is a common problem in research and development, where the data foundations are not well established, leading to confusion and misinterpretation of results.

Field-level meaning and why column names are not documentation

The problem starts with the field names in the database. Column names such as "test_result" or "product_specification" are not sufficient to convey the meaning of the data. They may be clear to the person who created the database, but to others, they are ambiguous. For example, what does "test_result" mean? Is it the result of a specific test, or a summary of all test results? Without clear documentation, it is impossible to know for sure.

This is why column names are not documentation. Documentation should provide a clear and concise explanation of what each field means, including the units of measurement, the data type, and any relevant context. Without this documentation, the data is essentially meaningless, and any analysis or query results will be unreliable.

The systems that get left out of scope and the partial answers that follow

Another problem is that some systems get left out of scope when designing the data foundations for research and development. For example, the enterprise resource planning system may be included, but the product lifecycle management system may not. This means that the data from these systems is not available for analysis, leading to partial answers and a lack of understanding of the complete picture.

This can lead to incorrect conclusions and decisions, as the data is incomplete and may not reflect the true situation. It is essential to include all relevant systems in the scope of the data foundations to ensure that the data is comprehensive and accurate.

The research and development data this depends on: test results and failure analyses, design review records, timesheets and allocation, supplier assessments

The data foundations for research and development depend on a range of data sources, including test results and failure analyses, design review records, timesheets and allocation, and supplier assessments. This data provides a complete picture of the research and development process, from the initial design concept to the final product testing.

For example, the test results and failure analyses provide information on the performance of the product, while the design review records provide information on the design decisions and trade-offs made during the development process. The timesheets and allocation data provide information on the resources used and the effort required to complete each task, while the supplier assessments provide information on the quality and reliability of the suppliers.

By including all these data sources in the data foundations, researchers and developers can gain a deeper understanding of the research and development process and make more informed decisions. For more information on how AsscherAi can support research and development, see our research and development page.

What it changes about: failures rediscovered across programmes and effort invisible against project stage

The data foundations for research and development change the way failures are handled and effort is tracked. With a complete and accurate picture of the data, researchers and developers can identify failures and areas for improvement across multiple programmes, rather than just within a single project.

This means that effort is no longer invisible against the project stage, and resources can be allocated more effectively. By understanding where effort is being wasted or duplicated, researchers and developers can optimize the research and development process and reduce the time and cost required to bring a product to market.

Measured with: failures found before validation, effort against stage, estimate revision count

The effectiveness of the data foundations for research and development can be measured using a range of metrics, including failures found before validation, effort against stage, and estimate revision count. These metrics provide a clear picture of the impact of the data foundations on the research and development process.

For example, the number of failures found before validation provides information on the effectiveness of the testing and quality assurance processes, while the effort against stage provides information on the resources required to complete each task. The estimate revision count provides information on the accuracy of the initial estimates and the amount of rework required.

What this does not do, or when it is the wrong choice

The data foundations for research and development are not a solution for all data-related problems. They are specifically designed to support the research and development process, and may not be suitable for other areas of the organization.

For example, the data foundations may not be suitable for financial reporting or compliance, where different data sources and systems are required. In these cases, a different approach may be needed, and the data foundations for research and development may not be the best choice. If you are unsure about the best approach for your organization, please contact us for more information.