But some government officials and state engineers are hoping there is an alternative: elevation. The $6.8 billion Southwest Coastal Louisiana Project is betting that raising residences by an average of three to five feet and nonresidential buildings by three to six, coupled with extensive work to restore coastal boundary lands, will keep Louisianans in their communities and a local economy that helps power the country’s oil industry running. The project, a collaboration between the US Army Corps of Engineers and the Louisiana Coastal Protection and Restoration Authority (CPRA), is focused on roughly 4,700 square miles of land inthree parishes in the southwestern corner of the state: Cameron, Vermilion, and Calcasieu, where Lake Charles is the parish seat. More than 3,000 homes have been identified as being at risk of imminent flooding, and therefore as candidates for elevation funding.

Ultimately, it’s something of a last-ditch effort to preserve this slice of coastline, even as some locals pick up and move inland and as formal plans for managed retreat—or government funding for community relocation—become more popular in climate-­vulnerable areas across the country and the rest of the world. 

Since 1932, Louisiana has lost some 1.2 million acres of coast to erosion—an area nearly twice the size of Rhode Island.

Now, after eight years of surveys, paperwork, and waiting for cash, the pilot phase of the project is finally moving forward and raising 21 homes. As it does so, project staff and locals alike will be forced to grapple with a looming existential question: Can a region facing some of the nation’s most alarming climate predictions build its way out of an accelerating crisis? 

Darrel Broussard, the project’s senior manager, sees its work as the region’s best chance at reducing damage over the next 50 years and safeguarding the roots residents have put down over generations. “This is Louisiana. This is where everyone lives. This is where we work. This is where the economy comes from,” he says. “There are models out there trying to predict the future. They’re just models. Right now, we currently have communities, neighbors, all living there.”

At the same time, some environmental experts worry that this may be too rosy an outlook, with time and nature conspiring against lasting success. “The sooner we can shift our mindset towards managed retreat, the better,” says Torbjörn Törnqvist, a geology professor at Tulane University. “This is a very tough issue. This is a part of the country that’s just going to disappear.” 

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It didn’t take long for the Bells to feel at home in Lake Charles, the biggest city in what Louisiana officials call the state’s “working coastline.” The economy here thrives on commercial fishing and agriculture, though petroleum services have long been at its heart; roughly 30% of Louisiana’s refining capacity is based in the region, and the state accounts for nearly one-sixth of the country’s refining capacity, according to the US Energy Information Administration. 

But what appealed most to Christa Bell, a public relations professor at McNeese State University, was locals’ hospitality and cuisine—proud reflections of Louisiana’s friendly charm. She loved the warm aesthetic of historic Ryan Street’s red brick buildings, which stand in stark contrast to the city’s casinos and refineries and its single skyscraper, the former Capital One Tower.

The building has sat vacant since a hurricane damaged it nearly four years ago—and over that time it has become a symbol of the strain created by severe weather in an area where waterways flow like veins and where flooding occurs often. 

This report seeks to understand organizations’ objectives for their data modernization projects and how they are implementing such initiatives. To do so, it surveyed senior data and technology executives across industries. The research finds that many have made substantial progress and investment in data modernization. Alignment on data strategy and the goals of modernization appear to be far from complete in many organizations, however, leaving a disconnect between data and technology teams and the rest of the business. Data and technology executives and their teams can still do more to understand their colleagues’ data needs and actively seek their input on how to meet them.

Following are the study’s key findings:

AI isn’t the only reason companies are modernizing the data estate. Better decision-making is the primary aim of data modernization, with nearly half (46%) of executives citing this among their three top drivers. Support for AI models (40%) and for decarbonization (38%) are also major drivers of modernization, as are improving regulatory compliance (33%) and boosting operational efficiency (32%).

Data strategy is too often siloed from business strategy. Nearly all surveyed organizations recognize the importance of taking a strategic approach to data. Only 22% say they lack a fully developed data strategy. When asked if their data strategy is completely aligned with key business objectives, however, only 39% agree. Data teams can also do more to bring other business units and functions into strategy discussions: 42% of respondents say their data strategy was developed exclusively by the data or technology team.

Data strategy paves the road to modernization. It is probably no coincidence that most organizations (71%) that have embarked on data modernization in the past two years have had a data strategy in place for longer than that. Modernization goals require buy-in from the business, and implementation decisions need strategic guidance, lest they lead to added complexity or duplication.

Top data pain points are data quality and timeliness. Executives point to substandard data (cited by 41%) and untimely delivery (33%) as the facets of their data operations most in need of improvement. Incomplete or inaccurate data leads enterprise users to question data trustworthiness. This helps explain why the most common modernization measure taken by our respondents’ organizations in the past two years has been to review and upgrade data governance (cited by 45%).

Cross-functional teams and DataOps are key levers to improve data quality. Modern data engineering practices are taking root in many businesses. Nearly half of organizations (48%) are empowering cross-functional data teams to enforce data quality standards, and 47% are prioritizing implementing DataOps (cited by 47%). These sorts of practices, which echo the agile methodologies and product thinking that have become standard in software engineering, are only starting to make their way into the data realm.

Compliance and security considerations often hinder modernization. Compliance and security concerns are major impediments to modernization, each cited by 44% of the respondents. Regulatory compliance is mentioned particularly frequently by those working in energy, public sector, transport, and financial services organizations. High costs are another oft-cited hurdle (40%), especially among the survey’s smaller organizations.

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.

Stretch weighs about 50 pounds. It has a small mobile base, a stick with a camera dangling off it, and an adjustable arm featuring a gripper with suction cups at the ends. It can be controlled with a console controller. Henry controls Stretch using a laptop, with a tool that that tracks his head movements to move a cursor around. He is able to move his thumb and index finger enough to click a computer mouse. Last summer, Stretch was with the couple for more than a month, and Henry says it gave him a whole new level of autonomy. “It was practical, and I could see using it every day,” he says. 

Using his laptop, he could get the robot to brush his hair and have it hold fruit kebabs for him to snack on. It also opened up Henry’s relationship with his granddaughter Teddie. Before, they barely interacted. “She didn’t hug him at all goodbye. Nothing like that,” Jane says. But “Papa Wheelie” and Teddie used Stretch to play, engaging in relay races, bowling, and magnetic fishing. 

Stretch doesn’t have much in the way of smarts: it comes with some pre­installed software, such as the web interface that Henry uses to control it, and other capabilities such as AI-enabled navigation. The main benefit of Stretch is that people can plug in their own AI models and use them to do experiments. But it offers a glimpse of what a world with useful home robots could look like. Robots that can do many of the things humans do in the home—tasks such as folding laundry, cooking meals, and cleaning—have been a dream of robotics research since the inception of the field in the 1950s. For a long time, it’s been just that: “Robotics is full of dreamers,” says Kemp.

But the field is at an inflection point, says Ken Goldberg, a robotics professor at the University of California, Berkeley. Previous efforts to build a useful home robot, he says, have emphatically failed to meet the expectations set by popular culture—think the robotic maid from The Jetsons. Now things are very different. Thanks to cheap hardware like Stretch, along with efforts to collect and share data and advances in generative AI, robots are getting more competent and helpful faster than ever before. “We’re at a point where we’re very close to getting capability that is really going to be useful,” Goldberg says. 

Folding laundry, cooking shrimp, wiping surfaces, unloading shopping baskets—today’s AI-powered robots are learning to do tasks that for their predecessors would have been extremely difficult. 

Missing pieces

There’s a well-known observation among roboticists: What is hard for humans is easy for machines, and what is easy for humans is hard for machines. Called Moravec’s paradox, it was first articulated in the 1980s by Hans Moravec, thena roboticist at the Robotics Institute of Carnegie Mellon University. A robot can play chess or hold an object still for hours on end with no problem. Tying a shoelace, catching a ball, or having a conversation is another matter. 

There are three reasons for this, says Goldberg. First, robots lack precise control and coordination. Second, their understanding of the surrounding world is limited because they are reliant on cameras and sensors to perceive it. Third, they lack an innate sense of practical physics. 

“Pick up a hammer, and it will probably fall out of your gripper, unless you grab it near the heavy part. But you don’t know that if you just look at it, unless you know how hammers work,” Goldberg says.