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Making better decisions with big data personas

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Making better decisions with big data personas


A persona is an imaginary figure representing a segment of real people, and it is a communicative design technique aimed at enhanced user understanding. Through several decades of use, personas were data structures, static frameworks user attributes with no interactivity. A persona was a means to organize data about the imaginary person and to present information to the decision-makers. This wasn’t really actionable for most situations.

How personas and data work together

With increasing analytics data, personas can now be generated using big data and algorithmic approaches. This integration of personas and analytics offers impactful opportunities to shift personas from flat files of data presentation to interactive interfaces for analytics systems. These personas analytics systems provide both the empathic connection of personas and the rational insights of analytics. With persona analytics systems, the persona is no longer a static, flat file. Instead, they are operational modes of accessing user data. Combining personas and analytics also makes the user data less challenging to employ for those lacking the skills or desire to work with complex analytics. Another advantage of persona analytics systems is that one can create hundreds of data-driven personas to reflect the various behavioral and demographic nuances in the underlying user population.

A “personas as interfaces” approach offers the benefits of both personas and analytics systems and addresses each’s shortcomings. Transforming both the persona and analytics creation process, personas as interfaces provide both theoretical and practical implications for design, marketing, advertising, health care, and human resources, among other domains.

This persona as interface approach is the foundation of the persona analytics system, Automatic Persona Generation (APG). In pushing advancements of both persona and analytics conceptualization, development, and use, APG presents a multi-layered full-stack integration affording three levels of user data presentation, which are (a) the conceptual persona, (b) the analytical metrics, and (c) the foundational data.

APG generates casts of personas representing the user population, with each segment having a persona. Relying on regular data collection intervals, data-driven personas enrich the traditional persona with additional elements, such as user loyalty, sentiment analysis, and topics of interest, which are features requested by APG customers.

Leveraging intelligence system design concepts, APG identifies unique behavioral patterns of user interactions with products (i.e., these can be products, services, content, interface features, etc.) and then associates these unique patterns to demographic groups based on the strength of association to the unique pattern. After obtaining a grouped interaction matrix, we apply matrix factorization or other algorithms for identifying latent user interaction. Matrix factorization and related algorithms are particularly suited for reducing the dimensionality of large datasets by discerning latent factors.

How APG data-driven personas work

APG enriches the user segments produced by algorithms via adding an appropriate name, picture, social media comments, and related demographic attributes (e.g., marital status, educational level, occupation, etc.) via querying the audience profiles of prominent social media platforms. APG has an internal meta-tagged database of thousand of purchased copyright photos that are age, gender, and ethnically appropriate. The system also has an internal database of hundreds of thousands of names that are also age, gender, and ethnically appropriate. For example, for a persona of an Indian female in her twenties, APG automatically selects a popular name for females twenty years ago in India. The APG data-driven personas are then displayed to the users from the organization via the interactive online system.

APG employs the foundational user data that the system algorithms act upon, transforming this data into information about users. This algorithmic processing outcome is actionable metrics and measures about the user population (i.e., percentages, probabilities, weights, etc.) of the type that one would typically see in industry-standard analytics packages. Employing these actionable metrics is the next level of abstraction taken by APG. The result is a persona analytics system capable of presenting user insights at different granularity levels, with levels both integrated and appropriate to the task.

For example, C-level executives may want a high-level view of the users for which personas would be applicable. Operational managers may want a probabilistic view for which the analytics would appropriate. The implementers need to take direct user action, such as for a marketing campaign, for which the individual user data is more suitable.

Each level of the APG can be broken down as follows:

Conceptual level, personas. The highest level of abstraction, the conceptual level, is the set of personas that APG generates from the data using the method described above, with a default of ten personas. However, APG theoretically can generate as many personas as needed. The persona has nearly all the typical attributes that one finds in traditional flat-file persona profiles. However, in APG, personas as interfaces allow for dramatically increased interactivity in leveraging personas within organizations. Interactivity is provided such that the decision-maker can alter the default number to generate more or fewer personas, with the system currently set for between five and 15 personas. The system can allow for searching a set of personas or leveraging analytics to predict persona interests.

Analytics level: percentages, probabilities, and weights. At the analytics level, APG personas act as interfaces to the underlying information and data used to create the personas. The specific information may vary somewhat by the data source. Still, the analytics level will reflect the metrics and measures generated from the foundational user data and create the personas. In APG, the personas provide affordance to the various analytics information via clickable icons on the persona interface. For example, APG displays the percentage of the entire user population that a particular persona is representing. This analytic insight is valuable for decision-makers to determine the importance of designing or developing for a specific persona and helps address the issue of the persona’s validity in representing actual users.

User level: individual data. Leveraging the demographic metadata from the underlying factorization algorithm, decision-makers can access the specific user level (i.e., individual or aggregate) directly within APG. The numerical user data (in various forms) are the foundation of the personas and analytics.

The implications of data-driven personas

The conceptual shift of personas from flat files to personas as interfaces for enhanced user understanding opens new possibilities for interaction among decision-makers, personas, and analytics. Using data-driven personas embedded as the interfaces to analytics systems, decision-makers can, for example, imbue analysis systems with the benefit of personas to form a psychological bond, via empathy, between stakeholders and user data and still have access to the practical user numbers. There are several practical implications for managers and practitioners. Namely, personas are now actionable, as the personas accurately reflect the underlying user data. This full-stack implementation aspect has not been available with either personas or analytics previously.

APG is a fully functional system deployed with real client organizations. Please visit https://persona.qcri.org to see a demo.

This content was written by Qatar Computing Research Institute, Hamad Bin Khalifa University, a member of Qatar Foundation. It was not written by MIT Technology Review’s editorial staff.

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How Twitter’s “Teacher Li” became the central hub of China protest information

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How Twitter’s “Teacher Li” became the central hub of China protest information


It’s hard to describe the feeling that came after. It’s like everyone is coming to you and all kinds of information from all over the world is converging toward you and [people are] telling you: Hey, what’s happening here; hey, what’s happening there; do you know, this is what’s happening in Guangzhou; I’m in Wuhan, Wuhan is doing this; I’m in Beijing, and I’m following the big group and walking together. Suddenly all the real-time information is being submitted to me, and I don’t know how to describe that feeling. But there was also no time to think about it. 

My heart was beating very fast, and my hands and my brain were constantly switching between several software programs—because you know, you can’t save a video with Twitter’s web version. So I was constantly switching software, editing the video, exporting it, and then posting it on Twitter. [Editor’s note: Li adds subtitles, blocks out account information, and compiles shorter videos into one.] By the end, there was no time to edit the videos anymore. If someone shot and sent over a 12-second WeChat video, I would just use it as is. That’s it. 

I got the largest amount of [private messages] around 6:00 p.m. on Sunday night. At that time, there were many people on the street in five major cities in China: Beijing, Shanghai, Chengdu, Wuhan, and Guangzhou. So I basically was receiving a dozen private messages every second. In the end, I couldn’t even screen the information anymore. I saw it, I clicked on it, and if it was worth posting, I posted it.

People all over the country are telling me about their real-time situations. In order for more people not to be in danger, they went to the [protest] sites themselves and sent me what was going on there. Like, some followers were riding bikes near the presidential palace in Nanjing, taking pictures, and telling me about the situation in the city. And then they asked me to inform everyone to be cautious. I think that’s a really moving thing.

It’s like I have gradually become an anchor sitting in a TV studio, getting endless information from reporters on the scene all over the country. For example, on Monday in Hangzhou, there were five or six people updating me on the latest news simultaneously. But there was a break because all of them were fleeing when the police cleared the venue. 

On the importance of staying objective 

There are a lot of tweets that embellish the truth. From their point of view, they think it’s the right thing to do. They think you have to maximize the outrage so that there can be a revolt. But for me, I think we need reliable information. We need to know what’s really going on, and that’s the most important thing. If we were doing it for the emotion, then in the end I really would have been part of the “foreign influence,” right? 

But if there is a news account outside China that can record what’s happening objectively, in real time, and accurately, then people inside the Great Firewall won’t have doubts anymore. At this moment, in this quite extreme situation of a continuous news blackout, to be able to have an account that can keep posting news from all over the country at a speed of almost one tweet every few seconds is actually a morale boost for everyone. 

Chinese people grow up with patriotism, so they become shy or don’t dare to say something directly or oppose something directly. That’s why the crowd was singing the national anthem and waving the red flag, the national flag [during protests]. You have to understand that the Chinese people are patriotic. Even when they are demanding things [from the government], they do it with that sentiment. 

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Your microbiome ages as you do—and that’s a problem

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Your microbiome ages as you do—and that’s a problem


These ecosystems appear to change as we age—and these changes can potentially put us at increased risk of age-related diseases. So how can we best look after them as we get old? And could an A-grade ecosystem help fend off diseases and help us lead longer, healthier lives?

It’s a question I’ve been pondering this week, partly because I know a few people who have been put on antibiotics for winter infections. These drugs—lifesaving though they can be—can cause mass destruction of gut microbes, wiping out the good along with the bad. How might people who take them best restore a healthy ecosystem afterwards?

I also came across a recent study in which scientists looked at thousands of samples of people’s gut microbe populations to see how they change with age. The standard approach to working out what microbes are living in a person’s gut is to look at feces. The idea is that when we have a bowel movement, we shed plenty of gut bacteria. Scientists can find out which species and strains of bacteria are present to get an estimate of what’s in your intestines.

In this study, a team based at University College Cork in Ireland analyzed data that had already been collected from 21,000 samples of human feces. These had come from people all over the world, including Europe, North and South America, Asia, and Africa. Nineteen nationalities were represented. The samples were all from adults between 18 and 100. 

The authors of this study wanted to get a better handle on what makes for a “good” microbiome, especially as we get older. It has been difficult for microbiologists to work this out. We do know that some bacteria can produce compounds that are good for our guts. Some seem to aid digestion, for example, while others lower inflammation.
 
But when it comes to the ecosystem as a whole, things get more complicated. At the moment, the accepted wisdom is that variety seems to be a good thing—the more microbial diversity, the better. Some scientists believe that unique microbiomes also have benefits, and that a collection of microbes that differs from the norm can keep you healthy.
 
The team looked at how the microbiomes of younger people compared with those of older people, and how they appeared to change with age. The scientists also looked at how the microbial ecosystems varied with signs of unhealthy aging, such as cognitive decline, frailty, and inflammation.
 
They found that the microbiome does seem to change with age, and that, on the whole, the ecosystems in our guts do tend to become more unique—it looks as though we lose aspects of a general “core” microbiome and stray toward a more individual one.
 
But this isn’t necessarily a good thing. In fact, this uniqueness seems to be linked to unhealthy aging and the development of those age-related symptoms listed above, which we’d all rather stave off for as long as possible. And measuring diversity alone doesn’t tell us much about whether the bugs in our guts are helpful or not in this regard.
 
The findings back up what these researchers and others have seen before, challenging the notion that uniqueness is a good thing. Another team has come up with a good analogy, which is known as the Anna Karenina principle of the microbiome: “All happy microbiomes look alike; each unhappy microbiome is unhappy in its own way.”
 
Of course, the big question is: What can we do to maintain a happy microbiome? And will it actually help us stave off age-related diseases?
 
There’s plenty of evidence to suggest that, on the whole, a diet with plenty of fruit, vegetables, and fiber is good for the gut. A couple of years ago, researchers found that after 12 months on a Mediterranean diet—one rich in olive oil, nuts, legumes, and fish, as well as fruit and veg—older people saw changes in their microbiomes that might benefit their health. These changes have been linked to a lowered risk of developing frailty and cognitive decline.
 
But at the individual level, we can’t really be sure of the impact that changes to our diets will have. Probiotics are a good example; you can chug down millions of microbes, but that doesn’t mean that they’ll survive the journey to your gut. Even if they do get there, we don’t know if they’ll be able to form niches in the existing ecosystem, or if they might cause some kind of unwelcome disruption. Some microbial ecosystems might respond really well to fermented foods like sauerkraut and kimchi, while others might not.
 
I personally love kimchi and sauerkraut. If they do turn out to support my microbiome in a way that protects me against age-related diseases, then that’s just the icing on the less-microbiome-friendly cake.

To read more, check out these stories from the Tech Review archive:
 
At-home microbiome tests can tell you which bugs are in your poo, but not much more than that, as Emily Mullin found.
 
Industrial-scale fermentation is one of the technologies transforming the way we produce and prepare our food, according to these experts.
 
Can restricting your calorie intake help you live longer? It seems to work for monkeys, as Katherine Bourzac wrote in 2009. 
 
Adam Piore bravely tried caloric restriction himself to find out if it might help people, too. Teaser: even if you live longer on the diet, you will be miserable doing so. 

From around the web:

Would you pay $15,000 to save your cat’s life? More people are turning to expensive surgery to extend the lives of their pets. (The Atlantic)
 
The World Health Organization will now start using the term “mpox” in place of “monkeypox,” which will be phased out over the next year. (WHO)
 
After three years in prison, He Jiankui—the scientist behind the infamous “CRISPR babies”—is attempting a comeback. (STAT)
 
Tech that allows scientists to listen in on the natural world is revealing some truly amazing discoveries. Who knew that Amazonian sea turtles make more than 200 distinct sounds? And that they start making sounds before they even hatch? (The Guardian)
 
These recordings provide plenty of inspiration for musicians. Whale song is particularly popular. (The New Yorker)
 
Scientists are using tiny worms to diagnose pancreatic cancer. The test, launched in Japan, could be available in the US next year. (Reuters)

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The Download: circumventing China’s firewall, and using AI to invent new drugs

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The Download: circumventing China’s firewall, and using AI to invent new drugs


As protests against rigid covid control measures in China engulfed social media in the past week, one Twitter account has emerged as the central source of information: @李老师不是你老师 (“Teacher Li Is Not Your Teacher”). 

People everywhere in China have sent protest footage and real-time updates to the account through private messages, and it has posted them, with the sender’s identity hidden, on their behalf.

The man behind the account, Li, is a Chinese painter based in Italy, who requested to be identified only by his last name in light of the security risks. He’s been tirelessly posting footage around the clock to help people within China get information, and also to inform the wider world.

The work has been taking its toll—he’s received death threats, and police have visited his family back in China. But it also comes with a sense of liberation, Li told Zeyi Yang, our China reporter. Read the full story.

Biotech labs are using AI inspired by DALL-E to invent new drugs

The news: Text-to-image AI models like OpenAI’s DALL-E 2—programs trained to generate pictures of almost anything you ask for—have sent ripples through the creative industries. Now, two biotech labs are using this type of generative AI, known as a diffusion model, to conjure up designs for new types of protein never seen in nature.

Why it matters: Proteins are the fundamental building blocks of living systems. These protein generators can be directed to produce designs for proteins with specific properties, such as shape or size or function. In effect, this makes it possible to come up with new proteins to do particular jobs on demand. Researchers hope that this will eventually lead to the development of new and more effective drugs. Read the full story.

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