Resources

The layers behind the word—infrastructure, data, automation, and governance—so leadership teams stop planning against three different definitions at once.

A six-stage data cycle—and why preservation, access, and re-use, not analysis skill, are what usually block recommender engines and in-silico screening.

Notes from building a tool that populates databases automatically and extracts usable data from documents that were not built for it.

What it actually takes to turn inconsistent, narrative lab records into usable, structured data—worked through on five real examples.

Why digitised notes still fail FAIR, and how LLMs can structure historical “dark data” after the fact—so teams stop repeating experiments they have already run.

A live walkthrough of Augmend extracting structured results from shampoo-lab write-ups and pilot records—equipment IDs, material usage, and variable mapping into Excel.

How Augmend extracts parts, materials, and product specs from unstructured documents and loads them into your PLM—so the system becomes usable without the re-keying overhead.

The three-step logic underneath data-driven product development—and where teams often lose the connection between activity and impact.

MAPI research with BMNP Strategies and Decodexis on how advanced analytics is reshaping manufacturing innovation—and why most companies still under-resource it.

Connecting R&D process development with continuous improvement to turn insight into practice.

The case for prediction over brute-force computation—and when it's worth trading raw speed for a smarter model.

Five decisions about your own lab—ambition, orientation, compliance, vendor hygiene, and deferrable add-ons—before you talk to ELN vendors.

Three ways an ELN can store your data after you hit save—and the one demo question that reveals which model you are actually buying.

Why so many PLM systems stay underused—critical data locked in documents—and what changes once extraction into structured fields is automated.

Why governance—not another tool—is what makes data trustworthy enough to build on.

What FAIR actually requires beyond digitisation, shown through two experiments that only reveal their optimum once the data is harmonised.

People, process, and systems failures that create bad data—and the three highest-leverage fixes to start with.

Catching and fixing data problems at the source instead of downstream—and why that's cheaper every time it's skipped.

Why so much R&D evidence still lives in spreadsheets and slides—and what useful standardisation actually requires.

A blunt, honest look at what "trust the data" actually costs when nobody's been checking it.

Four buckets—input, change, output, and business outcome—and four to six collectible KPIs that show whether an initiative actually worked.

McKinsey research on how much R&D budget top innovators actually reallocate each year—and why both inertia and panic moves miss the mark.

Roadmaps, hub and spoke, domain-plus-digital people, data engineering, on-the-job support, and few big bets—what keeps programmes delivering after launch.

Three effort patterns after a digital change—below normal, back to normal, or permanently above—and why recognizing which one you are in changes what you promise.

The framework behind scope-and-strategy work—setting direction before committing budget to tools.

A contrarian take on where digital strategy actually needs to start—and why the obvious starting point usually isn't it.

A sequencing framework for data investment—use what you have, improve what you have, then create what you don't have yet.