In this context, effective data management is one of the foundations of a data-driven organization. But managing data in an enterprise is highly complex. As new data technologies come on stream, the burden of legacy systems and data silos grows, unless they can be integrated or ring-fenced.
Fragmentation of architecture is a headache for many a chief data officer (CDO), due not just to silos but also to the variety of on-premise and cloud-based tools many organizations use. Along with poor data quality, these issues combine to deprive organizations’ data platforms—and the machine learning and analytics models they support—of the speed and scale needed to deliver the desired business results.
To understand how data management and the technologies it relies on are evolving amid such challenges, MIT Technology Review Insights surveyed 351 CDOs, chief analytics officers, chief information officers (CIOs), chief technology officers (CTOs), and other senior technology leaders. We also conducted in-depth interviews with several other senior technology leaders. Here are the key findings:
- Just 13% of organizations excel at delivering on their data strategy. This select group of “high-achievers” deliver measurable business results across the enterprise. They are succeeding thanks to their attention to the foundations of sound data management and architecture, which enable them to “democratize” data and derive value from machine learning.
- Technology-enabled collaboration is creating a working data culture. The CDOs interviewed for the study ascribe great importance to democratizing analytics and ML capabilities. Pushing these to the edge with advanced data technologies will help end-users to make more informed business decisions — the hallmarks of a strong data culture.
- ML’s business impact is limited by difficulties managing its end-to-end lifecycle. Scaling ML use cases is exceedingly complex for many organizations. The most significant challenge, according to 55% of respondents, is the lack of a central place to store and discover ML models.
- Enterprises seek cloud-native platforms that support data management, analytics, and machine learning. Organizations’ top data priorities over the next two years fall into three areas, all supported by wider adoption of cloud platforms: improving data management, enhancing data analytics and ML, and expanding the use of all types of enterprise data, including streaming and unstructured data.
- Open standards are the top requirements of future data architecture strategies. If respondents could build a new data architecture for their business, the most critical advantage over the existing architecture would be a greater embrace of open-source standards and open data formats.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
How do I know if egg freezing is for me?
The tool is currently being trialed in a group of research volunteers and is not yet widely available. But I’m hoping it represents a move toward more transparency and openness about the real costs and benefits of egg freezing. Yes, it is a remarkable technology that can help people become parents. But it might not be the best option for everyone.
Read more from Tech Review’s archive
Anna Louie Sussman had her eggs frozen in Italy and Spain because services in New York were too expensive. Luckily, there are specialized couriers ready to take frozen sex cells on international journeys, she wrote.
Michele Harrison was 41 when she froze 21 of her eggs. By the time she wanted to use them, two years later, only one was viable. Although she did have a baby, her case demonstrates that egg freezing is no guarantee of parenthood, wrote Bonnie Rochman.
What happens if someone dies with eggs in storage? Frozen eggs and sperm can still be used to create new life, but it’s tricky to work out who can make the decision, as I wrote in a previous edition of The Checkup.
Meanwhile, the race is on to create lab-made eggs and sperm. These cells, which might be made from a person’s blood or skin cells, could potentially solve a lot of fertility problems—should they ever prove safe, as I wrote in a feature for last year’s magazine issue on gender.
Researchers are also working on ways to mature eggs from transgender men in the lab, which could allow them to store and use their eggs without having to pause gender-affirming medical care or go through other potentially distressing procedures, as I wrote last year.
From around the web
The World Health Organization is set to decide whether covid still represents a “public health emergency of international concern.” It will probably decide to keep this status, because of the current outbreak in China. (STAT)
Researchers want to study the brains, genes, and other biological features of incarcerated people to find ways to stop them from reoffending. Others warn that this approach is based on shoddy science and racist ideas. (Undark)
A watermark for chatbots can expose text written by an AI
For example, since OpenAI’s chatbot ChatGPT was launched in November, students have already started cheating by using it to write essays for them. News website CNET has used ChatGPT to write articles, only to have to issue corrections amid accusations of plagiarism. Building the watermarking approach into such systems before they’re released could help address such problems.
In studies, these watermarks have already been used to identify AI-generated text with near certainty. Researchers at the University of Maryland, for example, were able to spot text created by Meta’s open-source language model, OPT-6.7B, using a detection algorithm they built. The work is described in a paper that’s yet to be peer-reviewed, and the code will be available for free around February 15.
AI language models work by predicting and generating one word at a time. After each word, the watermarking algorithm randomly divides the language model’s vocabulary into words on a “greenlist” and a “redlist” and then prompts the model to choose words on the greenlist.
The more greenlisted words in a passage, the more likely it is that the text was generated by a machine. Text written by a person tends to contain a more random mix of words. For example, for the word “beautiful,” the watermarking algorithm could classify the word “flower” as green and “orchid” as red. The AI model with the watermarking algorithm would be more likely to use the word “flower” than “orchid,” explains Tom Goldstein, an assistant professor at the University of Maryland, who was involved in the research.
The Download: watermarking AI text, and freezing eggs
That’s why the team behind a new decision-making tool hope it will help to clear up some of the misconceptions around the procedure—and give would-be parents a much-needed insight into its real costs, benefits, and potential pitfalls. Read the full story.
This story is from The Checkup, MIT Technology Review’s weekly newsletter giving you the inside track on all things health and biotech. Sign up to receive it in your inbox every Thursday.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Elon Musk held a surprise meeting with US political leaders
Allegedly in the interest of ensuring Twitter is “fair to both parties.” (Insider $)
+ Kanye West’s presidential campaign advisors have been booted off Twitter. (Rolling Stone $)
+ Twitter’s trust and safety head is Musk’s biggest champion. (Bloomberg $)
2 We’re treating covid like flu now
Annual covid shots are the next logical step. (The Atlantic $)
3 The worst thing about Sam Bankman-Fried’s spell in jail?
Being cut off from the internet. (Forbes $)
+ Most crypto criminals use just five exchanges. (Wired $)
+ Collapsed crypto firmFTX has objected to a new investigation request. (Reuters)
4 Israel’s tech sector is rising up against its government
Tech workers fear its hardline policies will harm startups. (FT $)
5 It’s possible to power the world solely using renewable energy
At least, according to Stanford academic Mark Jacobson. (The Guardian)
+ Tech bros love the environment these days. (Slate $)
+ How new versions of solar, wind, and batteries could help the grid. (MIT Technology Review)
6 Generative AI is wildly expensive to run
And that’s why promising startups like OpenAI need to hitch their wagons to the likes of Microsoft. (Bloomberg $)
+ How Microsoft benefits from the ChatGPT hype. (Vox)
+ BuzzFeed is planning to make quizzes supercharged by OpenAI. (WSJ $)
+ Generative AI is changing everything. But what’s left when the hype is gone? (MIT Technology Review)
7 It’s hard not to blame self-driving cars for accidents
Even when it’s not technically their fault. (WSJ $)
8 What it’s like to swap Google for TikTok
It’s great for food suggestions and hacks, but hopeless for anything work-related. (Wired $)
+ The platform really wants to stay operational in the US. (Vox)
+ TikTok is mired in an eyelash controversy. (Rolling Stone $)
9 CRISPR gene editing kits are available to buy online
But there’s no guarantee these experiments will actually work. (Motherboard)
+ Next up for CRISPR: Gene editing for the masses? (MIT Technology Review)
10 Tech workers are livestreaming their layoffs
It’s a candid window into how these notoriously secretive companies treat their staff. (The Information $)