Companies have a myriad of software development strategies to choose from, but it wasn’t the completion of a recent AI-focused project in half the expected time that convinced the chief data officer of Woodside Energy that they had found the right approach.

On the contrary, explained data manager Shelley Kalms, that validation came when another project was not delivering the expected value – and the project was stopped in its tracks.

“We launched the initiative and it got the approval and the funding, and the team stood up,” Kalms told the recent IBM Think conference, “and they started working on a hypothesis. But what they found was that it wasn’t delivering the value they had expected – and so they quit.

“You could say it fails, but it’s also learning in this place – and the team has learned that ‘we will go fast, we will determine if we are providing value?’ And if not, we stop.

The ‘quick failure“ The mantra is nothing new to startups who live or die on it often, but in large organizations it can always be a surprise to see it actually happen – hence Kalm’s delight in seeing the philosophy. adopted so effectively.

After all, shutting down a poorly performing project allowed Woodside to redirect its valuable data analysts, developers, AI specialists, and other experts to other, more beneficial projects – like a new platform based on AI which has proven to be particularly effective in predicting when the resources of the giant’s machines need to be maintained.

The development of this system was originally initiated with the expectation of a 12 week implementation to develop a minimum viable product (MVP) – but “they did it in 6 weeks,” Kalms said, “and they started to realize the value that went to the P&L [profit and loss statement]. “

“For me, it was real,” she added. “We work at the speed of a startup, but we evolve for a company.”

Learning to fail

Woodside’s commitment to digital innovation dates back to at least 2014, and the company is often cited as one of Australia’s most progressive due to its full commitment to data-driven transformation and exploitation. .

Of the society partnership with IBM, in particular, has fueled a range of projects, including an AI-based, risk-based maintenance system and a integration tool which was run as a test of evolution IBM Garage collaborative development process.

Helping businesses operate like a startup – and yes, that includes fail quickly – is a core mission of IBM Garage, a self-proclaimed “transformation accelerator” that leverages virtual collaboration to bring together enterprise developers and IBM specialists in AI, blockchain, data science and other critical areas.

Despite speaking the language of startups, IBM’s collaborative approach targets a chronic problem: Large companies are still not very good at handling data.

A recent MIT Technology Review Insights survey showed just how poorly managed data is in most businesses, with just 13% of companies achieving their data-driven goals and many more floundering.

Successful data projects require more than tools, with successful enterprise data users reaffirming the importance of a data-driven culture as well as tools and data itself.

Garage is as focused on building this culture as it is on improving access to tools.

This fit well with Woodside’s pioneering agenda, with collaborative teams coming together and working together regularly to remove internal friction – ensuring the project has access to the right skills, when they need them.

“Culture is absolutely essential to transformation,” Kalms explained, noting that the Garage model “takes this human-centered approach in all its aspects, when we look from start to finish.”

“He takes the research and takes it in the context in which it takes place. The data then directs the solution to the opportunity or problem you’re trying to solve. “

There is always a trap

It’s not always a home run: In many companies, Gartner recently observed, a greater reliance on agile development and remote collaboration has been mixed.

While 92% of agile teams increased their code production by 10% during the pandemic, Gartner noted, two-thirds of software teams release code less frequently – and it’s 64% larger, on average.

“This is not the ideal situation for agile and newly remote teams,” notes the analyst firm.

Part of the challenge of an effort like IBM Garage, therefore, is not only making collaboration easier, but also preventing it from taking on a life of its own.

For Woodside, Kalms said, widespread membership has been key to keeping his plans from swelling out of control.

“People get there from day one,” she said, “and they look at the concept, the ideation, the involvement and the race, and they see it through to the end,” even though this failure means terminating a project prematurely.

“What’s different is it’s not done to us,” Kalms said. “It is our people who are developing an extremely engaging and empowering solution, and taking it to a level that I have never seen before.”