When data earns the right to steer decisions
Why this matters now
On 29 June 2026, India commemorated the 20th Statistics Day under the theme “Unlocking the Potential of Administrative Data” [1]. The date honors Professor Prasanta Chandra Mahalanobis, born on 29 June 1893, whose work connected statistical method with public planning [2]. For R&D leaders, this is more than a public-statistics observance. It is a reminder that research portfolios now depend on the same discipline: data must be traceable, comparable, current and fit for use before it shapes investment, technology transfer, patentability, regulatory plans or market launch.
A data pipeline is like a laboratory balance. It is useful only after calibration, context and limits are understood.
What changes when records become evidence
MoSPI’s 2026 release framed administrative data as information generated through public delivery systems, regulatory bodies and sectoral databases. It also stressed statistical integrity, credibility, privacy, trust, auditability, explainability and data provenance when using artificial intelligence [1]. Corporate R&D teams face the same test. Operational records, ELN exports, assay results, customer feedback, patent documents, regulatory correspondence and supplier data can all inform strategy. None of them should be treated as decision-ready by default.
| Evidence source | Main strength | Main risk | R&D and IP use |
| Experimental or survey data | Designed for a question | Cost and time | Claims, validation, regulatory support |
| Administrative or operational data | Timely and granular | Not collected for research | Safety signals, demand, process learning |
| Patent and literature data | Public technical record | Legal and semantic complexity | Prior art, patent landscape, FTO |
Discovery starts with source selection. Data curation turns raw material into usable evidence. Data Management protects lineage, access and version control. Data Analytics and Artificial Intelligence can then identify patterns, anomalies and relationships worth testing.
Verified case module: method to institution
Mahalanobis founded the Indian Statistical Institute in Kolkata in 1931 [2]. The National Sample Survey was established in 1950 to collect socio-economic data through sample surveys across India for policy and national income estimation [3]. His statistical work also shaped economic planning: MoSPI’s statistical yearbook notes that the Second Five-Year Plan used two-sector and four-sector models prepared by Professor P. C. Mahalanobis and focused on rapid industrialization and heavy and basic industries [5].
His 1936 paper, “On the Generalized Distance in Statistics,” introduced the statistical distance later known as Mahalanobis distance [4]. Today, the idea remains useful because it evaluates how far a point sits from a distribution while accounting for relationships among variables [2]. The business lesson is direct: a defensible method scales only when it is paired with institutions, repeatable workflows and trained review.
The hard lesson for modern teams
The contrarian point is simple: more records do not automatically mean better strategy. The United Nations’ 2025 module on administrative and other data sources notes that administrative data is created primarily for administrative purposes, not statistical needs, and that statistical agencies may have limited control over how such data is produced [7]. It also says the use of such sources needs quality assessment, cooperation with data providers, processing controls and metadata [7]. The UN National Quality Assurance Framework treats statistical quality management as a system-wide requirement for trust in official statistics [6].
R&D organizations should read this as a warning. A large data lake can still hide bias, missing values, inconsistent definitions or outdated classifications. AI can speed review, but it cannot repair unclear provenance by itself. For regulatory strategy, this affects whether evidence can support claims. For Technology Transfer, it affects whether an invention package is strong enough for licensing or collaboration. For IP protection, it affects what teams disclose, when they file and how confidently they map risks.
From research record to protectable asset
A practical innovation workflow links four tasks. First, discovery teams define the technical problem and collect evidence from experiments, literature, standards, markets and patents. Second, curation teams normalize names, units, classifications, assignees, inventor data, chemical entities and metadata. Third, analytics teams use visual models, clustering, anomaly detection and AI-assisted review to find patterns that experts can test. Fourth, IP and commercialization teams translate evidence into patentability analysis, prior art search, freedom-to-operate support, patent drafting and filing support, and IP protection strategy.
WIPO states that patentability depends on conditions such as novelty, inventive step, industrial applicability and sufficient disclosure, and that prior art searches help compare an invention with existing knowledge [8]. WIPO’s FTO guidance adds a different question: whether a product or service can be used as planned without infringing enforceable third-party IP rights in relevant countries and time periods [9]. Patentability and FTO therefore serve different business decisions. One asks whether protection may be available. The other asks whether commercialization may be blocked.
How Saturo Global supports the workflow
Saturo Global supports research, data and IP teams that need evidence they can defend. Our services include Data Curation & Management, Indexing & Abstracting, Strategic Patent Support and Data Visualization. We support prior art search, freedom-to-operate support, patent landscape support, patent drafting and filing support, and IP protection strategy.
For patent intelligence workflows, Saturo Global works with tools such as PatBase, Origin and Chemical Explorer to help teams organize patent families, technical concepts, chemical information, competitive activity and review outputs [10]. The goal is disciplined evidence handling: cleaner data, clearer review trails and better decision support for R&D investment, Technology Transfer, commercialization and regulatory planning.
Schedule a demo for a walkthrough of Minesoft PatBase and Origin.
References
[1] Press Information Bureau, Government of India. 2026. “MoSPI Celebrated 20th Statistics Day to commemorate the 133rd Birth Anniversary of Prof. P. C. Mahalanobis with the theme ‘Unlocking the Potential of Administrative Data.’” Ministry of Statistics & Programme Implementation. Link.
[2] Indian Statistical Institute. 2025. “Our Founder.” Indian Statistical Institute, Kolkata. Link.
[3] C. R. Rao. 1973. “Prasanta Chandra Mahalanobis (1893–1972): Mahalanobis Era in Statistics.” Biographical Memoirs of Fellows of the Indian National Science Academy. Link.
[4] P. C. Mahalanobis. 1936. “On the Generalized Distance in Statistics.” Proceedings of the National Institute of Sciences of India. Link.
[5] Ministry of Statistics & Programme Implementation. Statistical Year Book India, Chapter 7: “Five Year Plans.” Government of India. Link.
[6] United Nations Statistics Division. 2019. “UN National Quality Assurance Frameworks Manual for Official Statistics.” UNSD Methodology. Link.
[7] United Nations Statistics Division. 2025. “Module for Quality Assurance when using Administrative and Other Data Sources to produce Official Statistics.” UN Statistical Commission Background Document. Link.
[8] World Intellectual Property Organization. n.d. “How to Protect Inventions through Patents.” WIPO. Link.
[9] World Intellectual Property Organization. 2024. “Tool 5: Freedom to Operate.” WIPO Toolkit: Using Inventions in the Public Domain. Link.
[10] Minesoft. 2015–2026. “PatBase,” “Minesoft Origin,” and “Chemical Explorer.” Minesoft product materials. Links.
