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DataScan: Issue #56

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Forrester's 2018 predictions. The two types of data strategies every company needs. How to avoid "cre
 

DataScan

November 19 · Issue #56 · View online
Curated digest on the world of data.

Forrester’s 2018 predictions. The two types of data strategies every company needs. How to avoid “creepy” personalisation. Why and how the Cryptobubble will burst.
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Forrester released their predictions for 2018. 💯 The report covers thirteen key topics, from customer experience, to algorithms, to open banking. Interestingly, it shows that “80% of blockchain initiatives failed to meet expectations” in 2017, as:
Marketers oversold the technology, blockchain testing was not comprehensive enough, and blockchain solutions were applied to problems that could have been solved with other solutions.
The report also shows that some companies are weighing up the risks and implications of being fully or only partially compliant with the GDPR:
We predict that 80% of firms affected by GDPR will not comply with the regulation by May 2018. Of those noncompliant firms, 50% will intentionally not comply — meaning they have weighed the cost and risk and are taking a path that presents the best position for their firms. The other 50% are trying to comply but will fail.
–> Even though 79% of consumers say that they are unlikely to share data with companies they do not trust. 💸
What’s your data strategy? Writing for Harvard Business Review, Leandro DalleMule and Thomas H. Davenport explain their framework which “requires companies to make considered trade-offs between “defensive” and “offensive” uses of data and between control and flexibility in its use”. 📈 An interesting take on data management:
How to avoid “creepy” personalisation. Paul Henninger of FTI Consulting explains the “Creepiness Factor” - a way to measure whether your company has the right balance between “respecting privacy” and “making the most of data through customer analytics”. ⚖  Crucially, businesses need to focus on “understanding how customers make decisions” rather than individually profiling customers: 
The key is to adopt a smarter data strategy that reduces the reliance on clearly private data to understand and model customer behaviour.

Such a strategy balances, and promotes, the two apparently conflicting objectives of, on the one hand, reducing the cost of near-term data protection compliance, and on the other hand, creating a sustainable lead in the race to better understand and anticipate customer needs and wants.
Similarly - Janet Balis, a partner and leader of EY’s global advisory media and entertainment group - states that data should be used to “enhance” advertising rather than drive it entirely. 🌟
Nice article by Justin Bachman discussing how airlines are funnelling their vasts amount of customer data to “hyper-personalise” services to encourage loyalty - without crossing the creepy line:
You’ll get an apology if your flight last week was delayed, for example. Or a thank you if you just hit 200,000 miles for the year. Or, say, a flight attendant spills some coffee on your skirt: The tools will allow him to award you some frequent flier miles or a future travel voucher on the spot.
But, as Bachman points out, “do we want to feel like we’re under the microscope every time we fly?” 🕵 🎁 
Miscellaneous
✅  Quick intro to statistics - power your stories with data. 
😅  How to write a cryptocurrency white paper. –> Why and how the Cryptobubble will burst. 
💯  Ethereum Devcon3 - an outsider’s perspective. 
🙊  How Firefox got fast again. 
🔮  Nearly all of Wikipedia is written by just 1% of its editors.
🏆  Why video games are the new IQ tests.
🔍  [x] Everywhere is a “visual, arguably insane way to explore cities”:
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