Intel has flagged its own use of social, mobile, analytics, and cloud technologies – the so-called "SMAC stack" — as a major driver of organisational transformation and productivity.
Speaking at the technology & Innovation — the Future of Banking and Financial Services conference in Sydney, Intel's Australian and New Zealand Financial Services Industry manager Andrew Ridley said the use of the four areas of technology has had a "multiplicative effect" at the company.
"For modern enterprises to transform themselves, they have to embrace [the SMAC stack]," he said. "We have to uncouple tightly coupled industrial-age value chains to create boundary-less and more agile ways of working.
"It is not just putting people in an open-plan office. It is not just allowing them to collaborate in an open space. It is much more than that."
Commenting on Intel's own use of social technologies, Ridley said that the company's use of these technologies began with an internal blogging platform and instant messaging. This has since evolved to softphone and online meeting bridge technologies, which provide "one click to meet", VoIP, unified content sharing, and messaging.
"We have enabled groups to reuse content easily through reference libraries, cutting rework, and time to market," he said. "We can securely share large files using content sync capabilities using enterprise tools, and importantly, we can now easily connect to content experts across the corporation through expert finder capabilities."
Ridley said Intel is also deploying a "compute from any device" capability, virtual workspaces, digital whiteboards, and HD video solutions for better meeting collaboration.
"We are experimenting with ideation, crowdsourcing platforms and processes, gamification, and merit badges for those employees that contribute the most," he said. "It is truly changing the way we work as an organisation."
In the mobile space, Ridley said Intel has accrued around 7 million hours of productivity gains during a three-year period through the use of mobility computing within the organisation. In particular, the organisation is heavily promoting touch-enabled devices.
"We anticipate that we will deploy 14,000 touch-enabled devices by the end of this year. Employees that select touch-enabled devices, such as myself, enjoy greater productivity as touch applications become more prevalent."
To encourage developers to produce high-quality touch-enabled applications, Intel has created a five-star rating program. To obtain a five-star rating, applications must: Protect data and user identities; be intuitive and easy to use; be multi-platform; support new device interactions such as voice, touch, and gesture; and support new devices.
To address the security element of mobile computing, the company also created an Intel Trusted Applications Portal to allow users to more securely access selected Intel web-based applications.
"The application is customised to us based on who you are, your business group, and your type of device — mobility is far more than just the device," he said.
Ridley said the use and benefits of cloud are now well-worn territory, but he did predict that the "personal cloud" — an individual's collection of digital content, services, and apps that are seamlessly accessible across any device — will become an increasing feature of Intel's workplace.
Ridley said Intel began its journey with big data analytics by taking on smaller and more focused projects, resulting in as much as AU$10 million in value being delivered.
One such project, Ridley said, was the deployment of a factory business information (BI) solution at one of Intel's assembly, test, and manufacturing facilities. The deployment assesses unit-level data to improve chip testing through using predictive modelling.
"These models helped optimise individual test cycles per chip, reducing some test operations by as much as 80 percent," he explained. "Saving a few seconds on testing when you are manufacturing hundreds of millions of units adds up to significant time savings. These predictive models also more accurately predicted which units would fail at the end of this process."
The company is using predictive modelling to cut down on the failure rates of more than 100,000 end-user computing devices used at the company.
"We believed failure predictions lay in the client logs, which weren't being monitored — that's hundreds of millions of records daily," he said. "We couldn't monitor that much unstructured data, so we implemented a big data solution... And analysed over 70 million records daily. We found a high correlation — 0.78 — between predicted and actual incidents. Using proactive interventions, we were able to reduce failure by 30 percent, which is about 4,000 incidents a week, with an estimated cost avoidance of AU$4 million over two years
The company has also utilised data mining of external sales data to improve the performance of its sales team, and is now moving to roll out self-service BI for various teams.