Data Science, Apple’s Privacy Change, Data Automation, Synthetic Data, Transportation, Trustable Data

  • This week we begin with an article that describes the significantly evolving roles & tasks of data scientists as a result of new technology & additional changes that may eventually completely alter their nature of work.
  • Next, we have a piece explaining how changes by Apple to provide its users with greater choice over privacy settings have made zero-party data, new social platforms, blog interaction, & conventional marketing more useful.
  • Following that we have a story on data automation when integrated with the metaverse, helps businesses maintain accuracy & consistency across a variety of formats & sources and has the potential to completely transform how businesses operate.
  • After that, we have a write-up discussing the unquestionable acceleration of ML/AI-based technologies by synthetic data as they continue to penetrate every industry and sector.
  • Next is a note on data in intelligent transportation & a real-world example of data-driven programs to reform parking fines and enforcement by the city of Chicago with Conduent Transportation’s data science team.
  • Finally, we have an essay about trustable data that comes from specific and trusted sources, its factors & how it is a strategic asset for the organization.
Predictions On The Future Of Data Science

It is known that one of the main tasks usually assigned to data scientists is to “predict” the future. At the same time, the future of data scientists as a profession today is by no means predictable. New technologies are profoundly changing the responsibilities and activities performed by data scientists. This is then compounded by further transformations that may soon totally change the nature of such work. 

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How Apple’s Privacy Changes Force Social Media Marketing To Evolve

Direct-to-consumer businesses that previously relied heavily on Facebook (now Meta) as a way to target and advertise via social media are now starting to realize the perils that resulted from privacy policy changes instituted by Apple. These changes have upended the digital advertising strategy for hundreds of thousands of businesses and forced these companies to find new paths to their coveted customers.

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Automating Business Data Within The Metaverse Workplace

An increasing number of companies are looking to adopt the metaverse as a key element of their future business plans. Uncertainty persists, however, about what form the metaverse will take. Employees can expect to see even more digitized, working versions of themselves in various industries and roles, eliminating the need for a formal workplace and physical presence. 

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The Multi-Billion-Dollar Potential Of Synthetic Data

Synthetic data will be a huge industry in five to 10 years. For instance, Gartner estimates that by 2024, 60% of data for AI applications will be synthetic. This type of data and the tools used to create it have significant untapped investment potential. We are effectively on the cusp of a revolution in how machine learning (ML) and artificial intelligence (AI) can grow and have even more applications across sectors and industries. 

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Real-World Examples Of Data-Driven Transformation In Transportation

By now, you’ve probably already heard plenty about data in intelligent transportation. By their very nature, technologies that support intelligent transportation programs capture massive amounts of data. From the number of passengers entering and exiting transit platforms at rush hour to license plate images of vehicles caught racing through red lights, information comes flowing in through every device — fare gates, enforcement cameras, toll transponders and more. 

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What Is Trustable Data? Why Do You Need It?

The need to use predictive analysis and modeling in forecasting the growth of data has been brought about by how great the volume and variety of data there currently is. According to Gartner, “Data preparation is an iterative and agile process for exploring, combining, cleaning, and transforming raw data into curated datasets for self-service data integration, data science, data discovery, and business intelligence/analytics.”

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