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Guess Who? Netflix & Data Anonymisation
Data anonymisation is the process of protecting sensitive or private information by erasing or encrypting identifying information about an individual in data. Some of the data this can relate to might include names or locations. Subjective information such as…
Butterfly Data Automates Forecast Process from 5 Days to 2
The credit union consolidation rate has held steady at about 3.5% a year for the past 40 years. The most common merger scenario has larger entities acquiring small and midsize credit unions. Analysts are speculating that pressures on the biggest credit unions will force many to consider merger strategies in the months to come.
Butterfly Data Helps Insurers Drive Double-Digit Improvement in Customer Retention
In the first eight months of 2021, publicly announced mergers and acquisitions (M&A) were valued at more than $3.6 trillion globally and $1.8 trillion in the US according to Dealogic. Both numbers are the highest since 1995, when the data provider began its tracking.
An Introduction to Fuzzy Matching
Fuzzy Matching, also known as Approximate String Matching, is a technique to identify whether two strings are similar but not the same. Some everyday uses of fuzzy matching include: spell check, auto-correct, spam filtering, record linkage, and address matching.
Using Machine Learning to Detect Fake News
For my final year University project, I was tasked with using Artificial Intelligence to solve a real-world problem. The misinformation I was seeing every day sprawled over social media about such things as the American election and, more recently, the Covid-19 pandemic, sprang to mind…
SAS Step Star James Lancashire
Here at Butterfly, we love to celebrate our team and particularly today we want to recognise the achievements and unique entrance to our team of James Lancashire
What are the most common scores in Cricket?
At the start of this year, Kane Williamson scored 238 in New Zealand’s first innings of their second Test match against Pakistan in Christchurch. Remarkably, this was the first time…