As we have stepped into a new year, we decided to take a look back at all the data science stories that shaped 2017. It emerged as a big year for data science. Industries across domains realized the importance and potential of data and implemented it vigorously into their operations. New functionalities, new trends and constructive conversations about big data, artificial intelligence and machine learning defined data science in 2017.
Here’s a review of data science’s journey in 2017:
Conversations about Artificial Intelligence went mainstream
This year, one of the most debated topics was Artificial Intelligence. Remarkable incidents such as Facebook’s Artificial Intelligence bots developing their own language sparked opinions and dialogues about AI across platforms. Tech titans Elon Musk and Mark Zuckerberg were an important part of this debate. While Zuckerberg believes that AI has the potential to greatly further humankind, Musk keeps sounding the alarm about AI and the adverse effects it can potentially have on humanity.
Despite the warring opinions on the effect of artificial intelligence, the forefront it took this year has led to major industries incorporating it into their operations. Chatbots, for instance, have been implemented into almost every industry’s customer journey. Though most AI projects in 2017 have been at a base level, the center stage that artificial intelligence has taken shows immense potential for growth and newer developments.
A year of hacks and breaches
2017 was also a year of unfortunate data breaches. Ransomware to data hack, industries across domains suffered loss/breach of sensitive data. Cyberattacks on big names across domains such as Equifax, Uber, and Zomato brought the topic of data governance to the limelight. The hack into Equifax, one of the largest consumer credit reporting agencies, threatened the security of its customers and indicated the larger flaws in securing sensitive data.
It wasn’t just security breaches and stolen user information, 2017 saw one of the biggest ransomware attack – WannaCry. Spanning over 150 countries, this ransomware leveraged some of the leaked NSA tools. It targeted businesses running outdated Windows software and locked down computer systems. The hackers behind WannaCry demanded money to unlock files. More than 300,000 machines were hit across numerous industries, including healthcare and car companies.
Such high-level threats to information that didn’t even spare industry giants sparked the conversation about the necessity for better security. Regulatory bodies such as the General Data Protection Regulation (GDPR) vowed to strengthen the levels of defense for data.
Traditional industries adopted data science
Conservative industries such as finance and healthcare took brave steps towards data science this year. These industries are known for their old school processes that stem from various regulations in handling people’s personal data. The banking and finance sectors have taken serious steps towards a more digital and data-dependent functionality. The positive effect of customer analytics, social network analytics and big data on customer relations and the proportionate growth in business is one of the main reasons that has turned major banking firms towards data science.
With healthcare, the use of data science is multifold. About 30% of the world’s stored data comes from the healthcare industry. Predictive analysis and AI have expanded the possibilities for advanced healthcare. The global clinical intelligence, which includes clinical decision support tools and population health management systems, is set to see a 12.5% CAGR between 2017 and 2023. The ability to diagnose and treat better by optimizing data has helped the industry step up its game.
2017 proved to be quite an eventful year for data science. 2018 will certainly see new technologies throughout the different parts of the big data ecosystem. New startups will prop up and leverage the developments in the field, forcing the bigger fishes to step up their game.
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What innovations do you expect in the field of data science in 2018? Tell us in the comments section!