AI With No Data, Data Transformation, ESG & Data, Politics Of Data, Customer Data, Computational Storage

This week, we begin with an essay describing the different approaches to AI while explaining that it can solve various problems even without the availability of raw data. Next, is an article sketching the importance of data transformation while migrating data and contradicting some related myths. Then, we have a piece focusing on ESG strategies adopted by businesses to benefit them in supporting their goals with sustainability &social responsibility. Following that, we have a story on the leading trend of data-centric trade agreements with emphasis on data & data sharing among nations. Next, is an analysis of the need for customer data platform (CDP) that might compromise data control, privacy & flexibility. Lastly, we have a TechRadar interview with Richard New, VP of Research at Western Digital, to discuss the opportunities of combining data storage and processing known as computational storage.

Artificial Intelligence: Approaches To AI To Solve Complex Problems Even Without Data

The term Artificial Intelligence (AI) has recently become a hot topic, however, there are currently some misunderstandings about this term, for instance, it has been used as a synonym for Machine Learning (ML), however, ML is only a part of the whole AI. There are two main reasons to explain this, the first one is the fact that ML is the best known of all techniques, and the second one is because of the similarities between learning and “intelligent behaviour”.

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Debunking The Myths Of Data Transformation For A Better Data Migration Strategy

Data transformation can be a key component of a successful data migration strategy. To get it right, however, it’s important to really understand the process and what it entails. There are many myths and misunderstandings on this topic, but when it’s all done correctly, it’s a significant business enabler. In short, every major business transformation requires data transformation. Data migration is a loaded term; it can mean different things depending on what you’re trying to accomplish. 

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Why A Successful ESG Strategy Hinges On Strong Data Practices

The marketing technology industry is inextricably linked to huge amounts of data, so it is important that the development of technologies both maximize data security and keep cloud computing resources to a minimum. The truth is that the environmental, social and governance (ESG) issues associated with user data have been snowballing for quite some time. It seems now though that companies willing to adapt and put a major focus on ESG strategy tend to benefit in the long run. 

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The Politics Of Data In Government

The UK-Singapore Digital Economy Agreement (DEA), which was published in March, is described by officials as the world’s “most innovative trade agreement”. The trade deal – with a strong emphasis on data and data sharing in a modern digital world – follows similar agreements with Japan and Australia with reports of other agreements in the pipeline. On its own, it may not have been enough to knock some other geopolitical events off the front pages.

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4 Signs A Customer Data Platform Might Not Be A Fit For Your Brand

The “whatever works” mindset serves early-stage customer acquisition and growth hacking well, but it’s tough to scale. As a company’s growth accelerates, the marketing team hits a point where “Whatever works” no longer works like it used to, especially as its customer data reaches a critical mass. That’s naturally when the marketing team also begins to get serious about its martech tools – and these days, there aren’t many marketing technologies hotter than the customer data platform (CDP). 

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Why Bringing Processing To Storage Devices Could Be The Answer To The Data Dilemma

Companies today are more reliant on data than ever. But the smartest business plan, the most brilliant ad campaign, the most discerning sales projections aren’t worth anything if they’re based on bad data. When a company makes decisions based on inaccurate, incomplete, or inconsistent data, it doesn’t matter how finely tuned its business strategy is. You can’t build a great house on a bad foundation. Say a company is in the middle of a major computer-system upgrade: fancy new infrastructure, the latest data-analytics applications, all with the goal of becoming a data powerhouse. 

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