Consumer concerns about data collection and more
Verdict lists five of the top big data tweets in Q1 2022 based on data from GlobalData’s tech influencer platform.
Top Tweets are based on the total number of engagements (likes and retweets) received on tweets from over 463 big data experts tracked by GlobalData’s Technology Influencer Platform during the first quarter (Q1) of 2022.
The most popular tweets on big data in Q1 2022: Top 5
1. Antonio Grasso’s tweet on vsconsumer concerns about data collection
Antonio Grasso, CEO of Digital Business Innovation Srl, a consulting firm, tweeted about how consumers have become wary and hesitant to share their personal information with organizations. Grasso shared an infographic detailing how consumers are wary of companies that sell their personal data to third parties or share their personal data with third parties without consent. As a result, consumers have increasingly reported being bombarded with spam, raising concerns about their personal information being misused.
The infographic lists some of the top UK consumer concerns about data collection, some key data industry statistics and how to regain consumer trust. Some of the top consumer concerns about data collection included selling personal data to third parties and without consent, data hacking, data leaks and breaches, data misuse, spam, companies using personal data for their own interests, marketing and advertising issues, unreliability. , and others. The infographic further highlighted that around 45% of organizations in the UK have collaborated with a third party to share first party data. Additionally, 47% of UK businesses experienced a cyberattack at least once a month throughout 2020.
The analysis also revealed that around 36% of marketers said they use customer data most of the time in their decision-making process. Meanwhile, 122.3 billion spam messages were emailed to customers worldwide daily, implying that 85% of daily email traffic worldwide is spam. It was also found that 80% of tech company Alphabet’s revenue came from Google ads, which amounted to $147 billion in ad revenue in 2020.
Username: Antonio Grasso
Twitter username: @antgrasso
2. Ronald Van Loon’s tweet about data science fighting climate change
Ronald Van Loon, CEO of Intelligent World, an influencer network that connects businesses and experts to the public, shared an article about the role of data science in the fight against climate change. In March 2021, the US Department of Energy announced that it would provide $34.5 million for advanced research tools focused on climate and clean energy efforts. Therefore, funding to support data science and compute-driven approaches, such as artificial intelligence (AI) and machine learning (ML) to improve energy efficiency, advance clean energy and forecast anomalies in weather and climate conditions, the detailed article.
Data science is particularly useful in addressing the climate change crisis, as the large amounts of data generated can be applied to the study of climate change and can be used for new developments in the field. For example, the cloud can run ML algorithms to identify different patterns and derive crucial insights from climate data, such as polar ice levels or sea temperatures, the paper notes.
Additionally, climate issues are data-intensive and include complex datasets specifically suited for big data. In addition, high-performance computing (HPC) reduces the amount of environmental data, such as satellite images, allowing analytics to better manage it, the article points out. Other data science use cases for climate change include ML algorithms reducing traffic congestion that deteriorates air quality, and AI-, cloud- and computer-driven intelligence. Internet of Things (IoT) bringing more energy efficiency to building heating and cooling.
Username: Ronald Van Loon
Twitter username: @Ronald_vanLoon
3. Linda Grasso’s tweet about industrial companies are leveraging data to improve their operations
Linda Grasso, CEO of technology, information and media company DeltalogiX, shared an article about how industrial companies are using AI to boost their factory operations. AI in turn leverages Big Data, which offers insights that are derived when ML is applied to complex and variable data sets, the article details. Therefore, experts believe that to be successful, companies will need to transfer their data with the help of specialists in the field. According to research, more than 75% of industrial companies have been able to pilot some form of AI; however, about 15% have not yet understood its potential. Experts believe the latter’s analytics teams typically take an external approach to AI and ML that can work but yields unrealistic results, the article notes.
Therefore, to be successful, companies must deploy an automation framework with accurate historical data. Then big data will have to adapt to AI, often with fewer variables but with smart engineering called smart data. This expert-driven approach will lead to better predictive accuracy, enabling root-cause analysis, the paper further points out. There are five steps to creating smart data, which include defining the process, enriching the data, minimizing dimensionality, applying ML, and implementing and confirming models.
Username: Linda Grasso
Twitter username: @LindaGrass0
4. David Holm’s tweet about more organizations engaging in data collaboration
David Holm, technology strategist and start-up investor, shared an infographic on how more and more organizations are engaging in data collaboration to tackle common problems without compromising privacy. Some six privacy-preserving approaches to sharing data include fully homomorphic encryption, where data is encrypted before it is shared. Second, differential privacy, where noise is added to the data set so that it is difficult to reverse engineer the original inputs. Third, functional encryption, which allows certain users to view certain parts of the encrypted text.
Fourth, federated analysis, where the parties share the ideas of their analysis but not the data. Fifth, zero-knowledge proofs, where users can prove that they are value-aware but not value-aware, and sixth, secure multi-party computing technique, where data analysis is distributed across all parties but no party can see the full set of contributions.
Username: David Holm
Twitter username: @cloudpreacher
5. Andreas Staub’s tweet describing big data
Andreas Staub, Head of Business Development and Digital Transformation at Raiffeisen Banking Group, shared an article about the definition of big data and how it has garnered so much attention over the past few years. Although the exact origin of big data is vague, some researchers claim that it was beginning to emerge due to the increased availability of more affordable and robust computing technologies in the 1990s. Big data has been defined by researchers as those consisting of large sets of data that require manipulation, storage and analysis.
However, the exact size when the data gets big has yet to be established, although there are some general guidelines or the three Vs that describe big in big data, the article notes. These are volume, which includes massive amounts of data, speed, which is built in real time, and variety, which is the type or nature of data, i.e. structured, semi-structured or unstructured.
Big data insights are beneficial for both businesses and individuals, including its application in health services such as the FitBit watch that tracks all health data in real time, the article details. Big data is also being used in business and finance to increase efficiency in pricing, financial fraud detection, customer experience optimization and to perform more actuarial calculations for insurance. On the other hand, big data also comes with inherent risks and challenges, such as misuse of data that can involve collection errors, discrimination, inappropriate profiling, data breaches, cyberattacks or even political manipulation. and social, the article points out.
Username: Andreas Staub
Twitter username: @andi_staub
Likes: 41 Retweets: 46