Content strategy, across languages and markets

Terminology’s ROI resists a magic formula. This piece frames the question through three prompts—necessity, budget, and payoff—then argues term validation works like software unit testing: each validated term reduces downstream errors. The honest verdict: terminology management is a long-term bet, but a worthwhile one.

Terminology gets dismissed as a translation-only concern, but IDC and Tekom data show knowledge workers waste hours hunting for information and departments constantly clash over vocabulary. The solution: build termbases before content creation, start with a simple spreadsheet, and let “good enough” guide you rather than perfection.

Not every metric deserves KPI status—only ones tied to real business goals. This piece maps KPIs to translation volume, revenue growth, and reputation, then shows how TMS dashboard metadata (word counts, delivery dates, resource counts) feeds indicators like DIFOT and First Pass Yield.

Data won’t make you rich, but it can sharpen decisions. This overview walks LSPs through turning human-resource, client, and language data into business intelligence—tracking translator turnaround, client profitability, and leveraging rates—setting up the follow-on piece on converting analytics into KPIs.

Using Gartner’s Hype Cycle as a lens, tlet’s separate real threats from noise—blockchain, AI, cryptocurrency—from technologies that matured, like machine translation. The advice for LSPs: skip the Luddite instinct, collaborate with your tech provider, know your data, and automate relentlessly.