Data Quality vs Data Intelligence

Agencies have more data than ever and see less and less benefit from it. My new blog is on why broad data beats big data: integrate what you already have instead of piling up more of the same. Special thanks to Stanley Young for advising against calling it "fat data versus tall data" 😂. Broader is better. https://lnkd.in/e7ncGVTT

The findings are worthy of consideration, but are based on a small sample and do not align completely with other data → Read full article: https://lnkd.in/eCazZXUB

We’ve got a few ideas in mind.💡   Data is only as valuable as what you do with it. The real impact comes from the actions and insights it enables. What would you fill in the blank with?

Thoroughly enjoyed discussing data-driven decision-making with Lloyd Richards, FIA CERA CStat, for the Dispelling Data Myths series. Making effective decisions is about having the right data at the right time, not just the fastest! At Crowe, we help organisations turn data into decisions they can trust.

well worth listening to Joshua Douglas and Lloyd Richards, FIA CERA CStat from Crowe UK talking about some of the challenges of getting the right data at the right time.

Is faster data always better?⚡ When we talk about real-time data, it's easy to assume that less latency is always the goal. But the reality is more nuanced. Some decisions require information in seconds. ⏱️ Others benefit more from complete, reliable data than speed alone. The key question isn't 'How fast can we make data flow?' but 'What is the right speed of data for the outcome we're trying to achieve?' 🎥 Watch our latest video in the Dispelling Data Myths series that explores data latency, real-time decision-making, and why there is no one-size-fits-all approach here 👇 https://lnkd.in/eFmP6qyK Lloyd Richards, FIA CERA CStat Joshua Douglas #DataStrategy #DataAnalytics #DataLatency #DispellingDataMyths

Data tells us what happened. Context helps explain why it happened. The best decisions rarely come from numbers alone. They come from combining data with an understanding of incentives, constraints, and the environment in which those numbers exist. Looking beyond the dashboard often reveals the bigger story.

What happens before a dataset reaches Local Insight? Before new data reaches Local Insight, our team needs to understand: 🔹 🔹 what the measure actually represents 🔹 how values are calculated 🔹 which geographies and time periods it covers 🔹 how missing or suppressed values should be handled 🔹 what caveats users need to know We then process, check and document the data before making it available through maps, reports and dashboards. A clear chart or map is only as reliable as the decisions, checks and documentation behind it. Which datasets do you find trickiest to work with?

"Research shows that roughly 60% of businesses do not actively measure or track the financial cost of poor-quality data." Source: intheblack.cpaaustralia

How we create dataset with Dark data feel like illegal? Day 216: Complex real-world problems are composed by data and dark data.