This week’s roundup discusses critical mistakes in A/B testing, the insane amounts of data we use every minute, how analytics is helping to close the gender gap in the workplace, and the current state of predictive analytics.

Getting Tricked by Statistics: Where A/B Testing Goes Wrong

by Jon Noronha, Director of Product at Optimizely, featured on LinkedIn

Analyzing an A/B test is deceptively simple. You count up how many conversions happened in each variation, you divide by the number of users exposed to it, and then you compare the conversion rates. Then you run it all through a stats formula online and see if it comes out >95% probability. Rinse and repeat for each variation and metric and presto, you’ve got a scorecard of winners and losers. What could possibly go wrong? A whole lot, it turns out.


The Insane Amounts of Data We’re Using Every Minute (Infographic)

by Rose Leadem, Freelance Writer for

With all the tweets, iMessages, streamed songs and Amazon prime orders, did you ever wonder just how much data is actually being generated every minute? To find out, cloud-based operating system Domo analyzed data usage over the past year, and shared the results in its sixth Data Never Sleeps report. It dives into online consumer behavior, examining the amount of data being generated every minute across popular apps and platforms including Google, Instagram, Amazon, Netflix, Spotify and more.


How Analytics Can Help Boost Gender Equality In The Workplace

by Mustafa Faizani, Chief Executive Officer, UAE and IMETA of Mercer, featured on Forbes Middle East

While countries across the world are placing heavy emphasis on diversity, it is important organizations are also part of this change and seize the opportunity and invest time and effort to help close the gender gap internally. But how can companies do this? Over the years, we have seen technology and analytics becoming one of the key drivers in organizations in accelerating the advancement of women.


SAS Survey: 93 Percent of Businesses Cannot Use Analytics to Predict Individual Customers’ Needs

by Wanda Rich, Editor in Chief for Global Banking & Finance Review

Despite a wealth of good intentions, including a big push towards using artificial intelligence (AI) to improve the customer journey, too many customers are being left in their own ‘digital shadows’. They are being served with communications and offers using incomplete data or data that is no longer relevant to their current interests or lifestyles. Even where the data is relevant, often backward-looking analysis is carried out meaning the organization is not establishing the ‘next best action’ for that customer.



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