## Metadata - Author: [[Olga Berezovsky]] - Full Title:: Anticipating 2026: Top Trends in Analytics - Issue 298 - Category:: #🗞️Articles - URL:: https://dataanalysis.substack.com/p/anticipating-2026-top-trends-in-analytics?utm_source=post-banner&utm_medium=web&utm_campaign=posts-open-in-app&triedRedirect=true - Read date:: [[2026-01-08]] ## Highlights > By now, we should have learned the hard lesson: ***more insights don’t matter unless you can act on them***. In 2026 and beyond, data teams will win or lose on integrations and on their ability to maintain metadata, semantics, context, and quality guardrails so systems can operate without breaking trust. ([View Highlight](https://read.readwise.io/read/01ked1ak6weyeb35kevnq59269)) > The role of the analyst is shifting from ***producing and optimizing reports to curating context***. Analysts are becoming “librarians” of the data stack ([View Highlight](https://read.readwise.io/read/01ked1fah7javfhd6gcn09fgzm)) > we’ve always defined and maintained context: what “*active*” means, how we define a billing period, what “significant” means for our data, what’s expected threshold for alerts, etc. The difference is that it’s no longer just analysts relying on this context. ([View Highlight](https://read.readwise.io/read/01ked1fvrmgph1xkbmkwfqfw24)) > **Benchmarks and guardrails -** Give systems standards to compare against - they can’t tell “right” from “wrong” unless you define and guardrail it. For example: > • Don’t use cohorts for modeling with fewer than 1,000 users. > • Use these expected baselines for key metrics… > • If a metric moves >10%, escalate or require review. > • Reject analyses that violate known data quality constraints ([View Highlight](https://read.readwise.io/read/01ked1jyr24pwhemre4n6ydgd7))