A growing number of companies have embraced big data, and data science now plays a significant role in many organisations. A 2017 study found that 62% of companies use data science on a weekly basis (with 31% using it daily), and that 89% of enterprises have at least one data scientist responsible for applying data science to the business…
The Analytics Suite 2 was unveiled at this year’s Digital Analytics Forum. This brand-new version of AT Internet’s solution is reinventing the daily routines of analysts, thanks to two main principles: data quality to ensure reliable measurement, and democratisation of analytics to generate insights for all job roles across the company.
Like exercise and grocery shopping, some things in life can be painful and time-consuming, but critical nonetheless. Analytics data quality control often falls into that bucket.
More than 55% of companies use data to make decisions, but only 33% of CEOs say they really trust their data (according to studies from Oxford Economics, SAP & KPMG).
Though data has become an increasingly strategic element for companies, its quality remains a significant challenge for a majority of groups.
Lionel Cherpin, founder of Empirik agency, was kind enough to partake in our interview. Discover his expert opinions on digital analytics.
Above all else, it’s the quality of your data that matters most. If your data is lacking in quality, your analyses will be skewed. When starting a new analytics project, focus first and foremost on accurate implementation.
The implementation phase is a critical step in a web analytics project. We regularly see web analytics implementations that were disregarded until the very end of a website project and were not adequately tested.
Today, we’ll share 10 tips to help you succeed in digital analytics, each paired with an image to help you comprehend and remember this advice. We hope it will help you go further with your analytics activities!
Measurement and bias are often found together. Ignoring or denying this is a risky bet. But analysts always know how to draw useful learnings from this fact.
When working with numbers, errors can happen easily. Incorrect mathematical formulas, erroneous filters and faulty addition all represent potential problems.