Late last month in Denver, data-warehousing giant Teradata launched its analytics cloud architecture and a raft of new tools designed to help organizations get more value from their data. But for some companies, technology is not the problem.

“All these tools are very nice, of course, but people need to use them,” says Jaco Fok, chief of innovation and digitization at OMV Petrom, a Romanian energy company with revenues of around €4.7bn ($5.2bn).

“We find that getting people to use the tools is actually 70% of the effort.”

He is not alone. According to the Harvard Business Review, 72% of the 64 C-level executives it spoke to say they have yet to forge a data culture, despite investment in big-data technologies.

The former national oil company of Romania, OMV Petrom was privatized about 15 years ago and is now part of Austria’s OMV Group.

With operations in eastern Europe and the middle east, in 2017 it began a digital transformation program with an innovation fund that Fok can use to build proof-of-concepts for the application of technologies capable of “changing people’s mindset” in their attitude to digitization.

“I’m on a mission to create a digital democracy. That means moving the tools and the capabilities skillsets into the hands of individuals with data problems. At a department level, we’re still juggling with Excel or on paper. That needs to change,” he told Teradata’s Universe conference.

First, OMV Petrom founded a ‘digital academy’, which, in partnership with Microsoft and LinkedIn, offers around 250 courses designed to help disseminate fundamental IT user skills throughout the organization.

But a digitization program also requires support from the top of the organization to succeed, he says.

“Everybody says if your CTO is not driving the initiative, then you know your whole change program is totally rubbish, and to some extent that is true.”

Fok’s answer was to create simple scorecards for each board member, updating them on the progress of the various digitization programs in their remit.

Although they are an old-style management tool, they provide a quick way of making progress on digitization that seems tangible to the most influential people in the organization. Crucially, the scorecards were introduced a year after projects began.

“You can say, ‘This project of yours is actually quite successful’. That makes board members feel good because if they have to say something about digitization, they have the security of some success,” he says.

Another technique Fok has employed to influence the board is the creation of an ‘innovation council’ made up of individuals who report to the board – two for each board member.

“Everyone on the board has a favorite person who they call when they get a question about a proposal,” he says.

“So, whenever I bring an idea to the board, the people board members call are already part of the design process. They will say: ‘Yeah, this is a great idea’. Culture is something that can be manufactured.”

A management layer down, Fok says he developed a ‘tribe’; a group of around 30 hand-picked individuals who are outgoing and interested in digitization.

He gave them an initial agenda and organized the first three meetings. After that, he let them manage themselves with access to a small budget to pay for events and speakers.

“Sometimes you want to do things that are kind of naughty. You’re not sure if they’re allowed, so you try them. You can just do it and then, if you need to, say sorry afterwards,” Fok says.

“Now they’re responsible for their own activities and have nothing to do with me. Not that I want to completely step away from accountability, but it’s a handy change tool in this sack of solutions.”

As well as using soft power to influence change, Fok has helped introduce data-visualization tools based on the Teradata platform as part of a pilot program.

Designed to support refinery maintenance, the dashboards avoid the problem of managers “bringing their own data” to meetings and spending most of their time arguing about which data is more accurate.

“All those meetings have become highly efficient since we eliminated the old reports,” Fok says. “It took three months for people to really embrace it, but a refinery manager is reporting fantastic results as the team adjusts to the changing language.”

Despite the challenges embedding a culture for data-driven decision making, companies in the HBR survey say they are still investing heavily in big data and analytics. OMV Petrom provides some valuable lessons for those hoping to change hearts and minds.

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Today’s Earth scientists are spending less time standing in fields collecting soil samples, and more time behind a computer screen. Most geoscience data is automatically collected by sensors and satellites. The big challenge is making sense of all that data so that scientists can get back to what they do best: Observing the world, asking questions, conducting experiments, and finding evidence.

Scientists use large, publicly available datasets from government programs such as NASA, NOAA, and USGS (that’s the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and US Geological Survey, in non-acronym speak). Many Earth scientists also have private sources, and combining these public and private datasets is difficult and time-consuming.

If a scientist wants to look at satellite images to gain a better understanding of climate change, for example, they have to spend hours sifting through data and managing several software programs.

“You want to reduce the time that you’re just managing data and get to those real meaty scientific questions,” says Dr. Annie Burgess. She is the lab director at the Earth Science Information Partners (ESIP) Lab, which funded a project led by Dr. Ziheng Sun, Principal Investigator, Center for Spatial Information Science and Systems at George Mason University. He developed Geoweaver, a program that solves the big data challenges that earth scientists face.

Sun developed a web-based system for deep learning on multiple datasets. It provides geoscientists with a system for making sense of public data (such as satellite images from NASA and NOAA) and private data (such as field observations). The project, called Geoweaver, helps earth scientists effectively use machine learning to sift through data so they can understand what’s really going on with our planet.

“ESIP Lab Geoweaver is an online application for scientists to manage their research workflows,” Sun explains. “It could be installed anywhere and accessed from anywhere. It is a life-saving project for people coding in multiple languages, dealing with multiple facilities and multiple datasets to carry out their science workflows.” 

Machine learning isn’t new, but previous versions were too slow to support the real-time data that Earth scientists need. Today’s computational power is much better, so Sun’s program can train on field data in much less time. He says the old, slow versions didn’t work, so geoscientists don’t have faith in machine learning. That’s why he created a program that combines the newest AI techniques with the programs that they already know and trust.

“Geoweaver will accelerate the adoption of artificial intelligence techniques in science,” Sun says. “It allows scientists to combine their legacy programs and datasets with the cutting-edge deep learning algorithms to create AI models which can more accurately and more automatically understand and predict our environment.”

His research group is already using the program in the lab every day for traditional geoscience research, such as studying crop yield prediction, agricultural drought, flooding damage assessment, and air quality prediction.

This research is more important now than ever because traditional models for agricultural markers such as crop yield didn’t factor in the rising global temperatures.

Burgess explains, “In a time of change in climate, really understanding something like crop yield, which affects the economy, affects global food supply.” She adds, “As the climate is more variable, you can’t rely on standard modeling techniques. And so the type of work that Ziheng Sun is doing where you’re using machine learning and satellite imagery, it’s going to prove more robust output for the future in a time of changing climate.”

That’s why her lab (ESIP) is providing small grants to help scientists like Dr. Sun develop prototypes that combine classic science techniques with the advantages of big data.

Geoweaver was designed with geoscientists in mind, but it can also be used by other scientists or even people working with data in completely different disciplines. It’s for people who are managing multiple servers and data sets for a machine learning workflow.

“It can be used by anybody who deals with servers, deals with multiple servers, multiple end features, and multiple operating systems,” Sun says. He is using the program in his lab now, and developing the final version of the open-source software is expected to be available in the next six months. 

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MICROSOFT SAID LAST week that it had achieved a new record for the accuracy of software that transcribes speech. Its system missed just one in 20 words on a standard collection of phone call recordings—matching humans given the same challenge.

The result is the latest in a string of recent findings that some view as proof that advances in artificial intelligence are accelerating, threatening to upend the economy. Some software has proved itself better than people at recognizing objects such as cars or cats in images, and Google’s AlphaGo software has overpowered multiple Go champions—a feat that until recently was considered a decade or more away. Companies are eager to build on this progress; mentions of AI on corporate earnings calls have grown more or less exponentially.

Now some AI observers are trying to develop a more exact picture of how, and how fast, the technology is advancing. By measuring progress—or the lack of it—in different areas, they hope to pierce the fog of hype about AI. The projects aim to give researchers and policymakers a more clear-eyed view of what parts of the field are advancing most quickly and what responses that may require.

“This is something that needs to be done in part because there’s so much craziness out there about where AI is going,” says Ray Perrault, a researcher at nonprofit lab SRI International. He’s one of the leaders of a project called the AI Index, which aims to release a detailed snapshot of the state and rate of progress in the field by the end of the year. The project is backed by the One Hundred Year Study on Artificial Intelligence, established at Stanford in 2015 to examine the effects of AI on society.

Claims of AI advances are everywhere these days, coming even from the marketers of fast food and toothbrushes. Even boasts from solid research teams can be difficult to assess. Microsoft first announced it had matched humans at speech recognition last October. But researchers at IBM and crowdsourcing company Appen subsequently showed humans were more accurate than Microsoft had claimed. The software giant had to cut its error rate a further 12 percent to make its latest claim of human parity.

The Electronic Frontier Foundation, which campaigns to protect civil liberties from digital threats, has started its own effort to measure and contextualize progress in AI. The nonprofit is combing research papers like Microsoft’s to assemble an open source, online repository of data points on AI progress and performance. “We want to know what urgent and longer term policy implications there are of the real version of AI, as opposed to the speculative version that people get overexcited about,” says Peter Eckersley, EFF’s chief computer scientist.

Both projects lean heavily on published research about machine learning and AI. For example, EFF’s repository includes charts showing rapid progress in image recognitionsince 2012—and the gulf between machine and humanperformance on a test that challenges software to understand children’s books. The AI Index project is looking to chart trends in the subfields of AI getting the most attention from researchers.

The AI Index will also try to monitor and measure how AI is being put to work in the real world. Perrault says it could be useful to chart the numbers of engineers working with the technology and the investment dollars flowing to AI-centric companies, for example. The goal is to “find out how much this research is having an impact on commercial products,” he says—although he concedes that companies may not be willing to release the data. The AI Index project is also working on tracking the volume and sentiment of media and public attention to AI.

Perrault says the project should win a broad audience because researchers and funding agencies will be keen to see which areas of AI have the most momentum, or need for support and new ideas. He says banks and consulting companies have already called, seeking a better handle on what’s real in AI. The tech industry’s decades-long love affair with Moore’s Law, which measured and forecast advances in computer processors, suggests charts showing AI progress will find a ready audience in Silicon Valley.

It’s less clear how such measures might help government officials and regulators grappling with the effects of smarter software in areas like privacy. “I’m not sure how useful it’ll be,” says Ryan Calo, a law professor at the University of Washington who recently proposed a detailed roadmap of AI policy issues. He argues that decisionmakers need a high-level grasp of the underlying technology, and a strong sense of values, more than granular measures of progress.

Eckersley of the EFF argues that AI tracking projects will become more useful with time. For example, debate about job losses might be informed by data on how quickly software programs are advancing to automate the core tasks of certain workers. And Eckersley says looking at measures of progress in the field has already helped convince him of the importance of work on how to make AI systems more trustworthy. “The data we’ve collected supports the notion that the safety and security of AI systems is a relevant and perhaps even urgent field of research,” he says.

Researchers in academia and at companies such as Google have recently investigated how to trick or booby-trap AI software and prevent it from misbehaving. As companies rush to put software in control of more common technology such as cars, measurable progress on how to make it reliable and safe could be the most important of all.

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