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BS ISO IEC 29155 โ IT Project Performance Benchmarking Framework
Systems and Software Engineering โ Information Technology Project Performance Benchmarking Framework
๐ The BS ISO/IEC 29155 series provides an internationally harmonised framework for information technology (IT) project performance benchmarking, adopted identically in the UK from ISO/IEC 29155. Targeting the systems and software engineering domain, it helps organisations measure IT project performance in a comparable and reproducible way, identify improvement opportunities, and lay the foundation for industry-level project performance databases. BSI Technical Committee IST/15 (Software and systems engineering) is responsible for UK participation, classified under ICS 35.080. This article presents the series as a whole: its framework structure, part contents, and engineering practice.
๐ก “You cannot improve what you do not measure” โ but inconsistent measurement definitions make data incomparable. The core value of ISO/IEC 29155 is harmonising “what to measure, how to measure, and how to report”, so that performance data from different organisations and periods is genuinely comparable.
1. Series Overview ๐
The ISO/IEC 29155 series is organised in four layers โ framework, requirements, reporting, and data. The parts were published in different years but build progressively on one another:
| Part | Edition | Subject | Main Content |
|---|---|---|---|
| BS ISO/IEC 29155-1 | 2011 | Part 1: Concepts and definitions | Defines the concepts and terminology of the overall framework: core and supporting benchmarking activities, and benchmarking information (aims, contexts, measurements). |
| BS ISO/IEC 29155-2 | 2013 | Part 2: Requirements for benchmarking | Specifies the benchmarking process model and requirements, defining roles, responsibilities, and activity inputs and outputs. |
| BS ISO/IEC 29155-3 | 2015 | Part 3: Guidance for reporting | Provides guidance for producing benchmarking reports and defines their quality requirements and role within the framework. |
| BS ISO/IEC 29155-4 | 2016 | Part 4: Guidance for data collection and data repositories | Guides the collection, storage, and maintenance of benchmarking data to support project performance databases (scan version; written from public domain knowledge). |
โ ๏ธ Part 4 (29155-4:2016) is a scan-only file in the source material; its content is summarised from public domain knowledge. Always refer to the official BSI published version.
2. Framework Structure and Core Activities ๐งฉ
Part 1 (Concepts and definitions) defines IT project performance benchmarking as a systematic process: determine the aims and contexts of benchmarking, collect project measurement data, compare it against benchmark data, analyse gaps, and finally produce improvement recommendations. The framework divides activities into two categories:
- Core benchmarking activities: defining benchmarking aims, selecting and defining measurements, collecting data, analysing and comparing, and reporting with recommendations โ directly producing benchmarking conclusions.
- Supporting activities: data quality assurance, data warehouse and repository management, process and tool support, plus organisational governance and training, keeping the core activities sustainable.
Part 2 (Requirements) provides the benchmarking process model: from preparation and planning, through data collection, analysis and evaluation, to reporting and improvement, with activities, inputs, outputs, and roles (e.g., sponsor, benchmarking practitioner, data analyst) defined for each process. It also stresses requirements for measurement consistency, validity, and traceability.
| Process Phase | Main Activities | Key Outputs |
|---|---|---|
| Planning and preparation | Define aims and contexts, scope, and select measurements | Benchmarking plan |
| Data collection | Collect project attribute data, verify quality | Normalised dataset |
| Analysis and comparison | Benchmark against reference data, identify gaps and causes | Gap analysis conclusions |
| Reporting and improvement | Produce reports, propose and implement improvements | Benchmarking report |
3. Reporting and Data Maintenance Requirements ๐
Part 3 (Guidance for reporting) clarifies the role of reports in the benchmarking framework: a report is the carrier of conclusions and improvement recommendations and evidence of data credibility. The guidance defines general quality requirements for reports โ completeness, accuracy, traceability, consistency, unambiguity, and verifiability โ and specifies the structure and content elements (aims and scope, method, data sources, results, limitations, and recommendations).
Part 4 (Data maintenance) focuses on long-term data asset building: standardising data item definitions, data formats, metadata, data quality rules, and update mechanisms, so that project data from successive rounds accumulates into organisational or industry-level benchmark databases.
๐จ Benchmarking is most at risk of “garbage in, garbage out”. If data definitions, collection conventions, and quality checks are inconsistent, the resulting comparisons will mislead decisions. Manage the full data lifecycle per Part 2 and Part 4 requirements.
4. Engineering Practice ๐ ๏ธ
- ๐ฏ Estimating: use historical and industry benchmark data to calibrate duration, cost, and effort estimates at project initiation.
- ๐ Process improvement: periodically benchmark delivered projects (productivity, defect density, schedule deviation) against baselines to locate improvement points.
- ๐ Supplier evaluation: measure outsourced project performance using a harmonised basis to support supplier tiering and renewal decisions.
- ๐๏ธ Organisational capability: build an organisational project performance database, accumulating data assets and reusable estimation models.
- ๐ Reporting governance: follow the Part 3 reporting guidance to manage internal report production and publication, strengthening management confidence in data.
5. Frequently Asked Questions โ
โ Q: How does ISO/IEC 29155 relate to other software measurement standards such as ISO/IEC 14143 and ISO/IEC 15939?
A: ISO/IEC 15939 defines the measurement process; ISO/IEC 14143 defines functional size measurement (FSM) concepts. ISO/IEC 29155 organises these measurement capabilities into the higher-level “project performance benchmarking” framework, defining how to use measurement data for project benchmarking.
A: ISO/IEC 15939 defines the measurement process; ISO/IEC 14143 defines functional size measurement (FSM) concepts. ISO/IEC 29155 organises these measurement capabilities into the higher-level “project performance benchmarking” framework, defining how to use measurement data for project benchmarking.
โ Q: Is benchmarking only for large organisations?
A: No. The framework allows tailoring by organisation size and objectives. Small and medium organisations can start with the simplest form โ harmonise measurement definitions, collect data for a few projects, then gradually establish the benchmarking process.
A: No. The framework allows tailoring by organisation size and objectives. Small and medium organisations can start with the simplest form โ harmonise measurement definitions, collect data for a few projects, then gradually establish the benchmarking process.
โ Q: Does this series specify particular performance indicators?
A: It does not prescribe specific indicator values. It defines the framework for how to define, collect, analyse, and report indicators; the concrete indicators (e.g., function points per person-month) are chosen by the organisation based on aims and contexts.
A: It does not prescribe specific indicator values. It defines the framework for how to define, collect, analyse, and report indicators; the concrete indicators (e.g., function points per person-month) are chosen by the organisation based on aims and contexts.
โ Q: What is worth noting about data maintenance in Part 4?
A: The key insight is that data assets need long-term stewardship โ standardised definitions, assured quality, and continuous accumulation are needed to form statistically meaningful baselines; one-off, inconsistently defined data cannot support reliable benchmarking.
A: The key insight is that data assets need long-term stewardship โ standardised definitions, assured quality, and continuous accumulation are needed to form statistically meaningful baselines; one-off, inconsistently defined data cannot support reliable benchmarking.