Learn how data science can evaluate, predict, automate, or even influence different tasks or phenomena in SEO

Have you ever looked at SERPs, keyword trends, and behavior on your website, and wondered if you couldn’t predict how adding new pages would impact how often organic visitors from your website are converted to leads?

Have you ever wondered if you could pick up on industry trends in SERPs before your competitors?

Have you taken a good look at your server logs in and asked yourself what Googlebot browsing patterns reveal about your website’s structure, pertinence, and technologies?

These questions, which bring together different types of data used in SEO strategy, are all part of data SEO. Data SEO is the rising field that brings data science methods of analysis to SEO. Data science uses scientific principles, statistics, and machine learning to bring together and analyze digital data for the purpose of decision making.

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Mason Grimshaw grew up on the Rosebud Sioux Indian Reservation in South Dakota but moved to Rapid City during high school to pursue a better education. When it came time to apply to college, he hopped online, typed “best engineering schools” into Google, and applied to two places: MIT and his father’s alma mater, the South Dakota School of Mines and Technology. He was admitted to both, but when he got into the Institute, his father insisted that he go.

It wasn’t an easy decision, however. Grimshaw felt guilt about leaving his community, where he says that everyone helps each other get by. The move to Rapid City had been difficult enough for him, given that 90 percent of his family lived back at the reservation. Coming to Cambridge was an even bigger step, but his family encouraged him to take the opportunity.

“I didn’t really want to leave home, because that is such a strong community for me. I thought if I did leave, it was only going to be worth it if I could get the best education possible,” he says.

Now a graduate student at the MIT Sloan School of Management working toward a Master of Business Analytics (MBAn) degree, Grimshaw hopes to eventually bring the skills and knowledge he acquires at MIT back home to the reservation.

Looking at the big picture, Grimshaw has aspirations to bring programming to Rosebud. The ultimate dream would be to open a software or web development consulting firm where he could teach community members computer science skills that they could, in turn, teach others. He hopes that through this business, he can equip people in the community with enough technical skills to be able to sustain the company on their own without his help. It’s a long-term goal, but Grimshaw aims high.

Discovering data

After earning his bachelor’s in business analytics at MIT, Grimshaw saw the MBAn as a natural next step. The program teaches students to apply the techniques of data science, programming, machine learning, and optimization to come up with business solutions.

“Because I did it as an undergrad, I thought this stuff was so cool. You can kind of predict the future and help anyone make a better decision. If I was going to be that person to help people make decisions that are important and change people’s lives, I wanted to make sure that I was as prepared as possible,” Grimshaw says.

Surprisingly, Grimshaw did not touch a line of code before coming to MIT. In fact, he entered college intending to study mechanical engineering. But in his first year, his friend was having issues with an assignment for a computer science class, so he decided to help him take a crack at the problem.

The work was fun, Grimshaw says, and coding came naturally for him. Eventually, he dropped his mechanical engineering pursuits and started studying computer science. He later switched majors and applied his computer science education to business analytics.

As a part of his MBAn program, he must complete an analytics capstone project, in which students work with a sponsor organization to create data-driven solutions to specific problems. Grimshaw, along with his program partner Amal Rar, will be working with the Massachusetts Bay Transportation Authority (MBTA) this summer to make The Ride, MBTA’s door-to-door paratransit service, more efficient.

Bringing business to invisible places

Grimshaw is also currently assisting MIT Sloan Senior Lecturer Anjali Sastry in writing a case study for South African nonprofit RLabs. RLabs seeks to inspire hope by providing business training and consulting to underprivileged South African communities. Grimshaw liked the organization’s mission, and he hopes that working on the RLabs case could give him some ideas about how to bring hope and innovation to his own community back home.

The nonprofit has, in part, inspired some of Grimshaw’s future aspirations for Rosebud. It has also gotten him to think about alternative ways to invest in or give back to communities that don’t necessarily focus on money. Some people, he says, need a place to stay or food more immediately than they need money.

Evaluating those circumstances and developing business models that address those more immediate needs as a form of payment can be a unique alternative to traditional compensation. Grimshaw stresses that monetary compensation is still important, but that being responsive to the specific areas of need within a community also has value.

“There’s a fine line. You can’t just say, ‘These people have nothing so they should just be happy to have a roof over their heads.’ I’m certainly not trying to do that, but there’s a difference in values and in what people place value on. Using that to make your business a little more sustainable is interesting,” Grimshaw says.

The reservation that Grimshaw is from lies within Todd County, an area that was previously listed as one of the poorest in America. He hopes to demonstrate to businesses that it is possible and worthwhile to invest in overlooked areas. He says that a lot of case studies in his field don’t feature stories from the emerging world or rural areas. He wants to show that through creative thinking and problem-solving, companies can work in these places, create jobs, and help lift people out of poverty.

Family forward

Outside of his studies, Grimshaw mostly spends time with his wife and 5-month-old son, Augustine. His face lights up as he speaks about them.

His wife, Julia, also has a passion for helping people and works as the assistant activities director at Hale House, an assisted senior living facility in Boston. The two of them grew up together and hope to move their family closer to home after Grimshaw finishes his MBAn. For now, their favorite things to do in Boston are going to the Public Gardens (Augustine loves the grass, Grimshaw says), getting a bite at Tasty Burger in Fenway, and watching the “Great British Bake Off” at home.

He also continues to participate in the American Indian Science and Engineering Society (AISES), which he joined as an undergraduate. There were very few members when he arrived at MIT in 2014, and while the number is still small, Grimshaw is enthusiastic about its growth.

“It was pretty cool because when I came here there were four, and on a good day five, of us. I still go to meetings. As I go now, there’s always 10 people, sometimes up to 12 or 15, and it’s awesome to see how much it’s growing,” he says.

While most people going into his field may opt for Silicon Valley or somewhere else on the coasts, Grimshaw would rather take his skill set closer to home. He won’t necessarily move back to Rosebud itself; somewhere within a reasonable driving-distance is more likely. He’s thinking about Denver, with its up-and-coming tech scene, but nothing is set in stone. Wherever he ends up, if a company is interested in helping others through data, Mason Grimshaw is here to help.

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• Machine learning and deep neural networks have spurred significant advances in artificial intelligence.
• Major AI applications in healthcare include diagnostics, robotic surgeries and virtual nursing assistants.
• Healthcare AI is projected to reach $6.6 billion in value by 2021.
• Adoption of AI could save the U.S. healthcare industry $150 billion annually by 2026.

In Star Wars: The Empire Strikes Back, Luke Skywalker is rescued from the frozen wastes of Hoth after a near-fatal encounter, luckily to be returned to a medical facility filled with advanced robotics and futuristic technology that treat his wounds and quickly bring him back to health. Of course, that’s the stuff of science fiction … for now.

The healthcare industry could be headed toward yet another high-tech makeover (even as it continues to adapt to the advent of electronic health records systems and other healthcare IT products) as artificial intelligence (AI) improves. Could AI applications become the new normal across virtually every sector of the healthcare industry? Many experts believe it is inevitable and coming sooner than you might expect.

What is artificial intelligence?

AI could be simply defined as computers and computer software that are capable of intelligent behavior, such as analysis and learning. It is a broad category at the cutting edge of technological development, growing and changing every day.

Machine learning and neural networks

Machine learning is the foundation of modern AI and is essentially an algorithm that allows computers to learn independently without following any explicit programming. As machine learning algorithms encounter more data, the algorithms’ performance improves.

Deep learning is a subset of machine learning that functions in a similar way with a slight twist. Deep learning goes a step further, making inferences based on the data it has encountered before. In other words, deep learning enables an AI application to draw its own conclusions. It works through an artificial neural network, which is a set of machine learning algorithms that work in tandem. A neural network loosely resembles the human brain, with a series of “neurons” that “fire” when certain stimuli (in this case, data) are present.

“Conventional machine learning solutions aren’t cognitive; they are trained from data but lack the ability to leap beyond missing or broken data and build a hypothesis about potential actions,” said AJ Abdallat, CEO of Beyond Limits. “Machine learning can be effective in detecting something anticipated, but it fails when confronted by the unexpected.”

To take artificial intelligence to the next level, Abdallat said, developers must emphasize both deductive and inductive reasoning, and emulate those cognitive patterns in the machines they design. A benefit of dynamic, deep learning solutions, he added, is that they can explain their reasoning and conclusions, a major benefit for complex decision-making.

How is AI used in healthcare?

AI is still a relatively new technology, especially in the healthcare industry where adoption remains in its infancy. As AI and machine learning tools become more sophisticated, their use cases have expanded; however, adoption of AI remains low, according to John Frownfelter, chief medical information officer at Jvion.

“We’re still in the hype phase where many organizations are trying to understand how it fits into an overall strategy,” said Frownfelter. “Early AI was seen … with more of an emphasis on pattern recognition for billing processes. It has evolved to a much more sophisticated use of deep machine learning and leveraging the power of big data.”

Modern AI applications include wide-ranging use cases, from cybersecurity to radiographic imaging, Frownfelter said. As AI applications continue to improve, the entire healthcare industry could undergo a shift. Here are some of the major ways AI is expected to shape healthcare in the coming years.

Diagnostics

AI excels at categorizing data, especially once it has been exposed to large amounts of data on the subject. That creates great promise for AI when it comes to diagnostics – medical imaging analysis and patient medical records, genetics, and more can all be combined to improve diagnostic outcomes. Moreover, AI tools can use similar information to craft unique treatment approaches and offer recommendations to doctors.

“The really interesting developments are in the clinical arena,” said Frownfelter. “Clinical prescriptive analytics is probably the closest AI is getting to support direct patient care in 2019.”

Robot-assisted surgery

Robotic surgeries allow surgeons to use smaller tools and make more precise incisions. Surgeons (and patients) could also benefit from AI by combining medical records with real-time data during operations, as well as drawing on data from previous successful surgeries of the same type. Accenture, a technology consulting firm, estimates that AI-enabled, robot-assisted surgery could save the U.S. healthcare industry $40 billion annually by 2026.

Virtual nursing assistants

Think of virtual nursing assistants like an Alexa for your hospital bedside. These virtual assistants replicate the typical behavior of a nurse by assisting patients with their daily routines, reminding them to take medications or go to appointments, helping answer medical questions and more. Accenture estimates that virtual nursing assistants could be the second-largest source of annual savings for the U.S. healthcare industry, cutting as much as $20 billion in costs.

Administrative workflow assistance

Naturally, medical practices, hospitals and other points of care result in a great deal of paperwork. In fact, it was consolidating and digitizing these records that led to the industry-wide adoption of electronic health records systems. AI has already started to make its way into these systems and can be used to streamline administrative functions as well. Accenture estimates that new efficiencies in administrative workflow due to emerging AI technologies could result in $18 billion in annual savings.

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