Accenture: How to optimise IT in your cloud estate
Businesses are being urged to get maximum value from their cloud estates with a new IT paradigm in which operating models can be more dynamic.
According to a new report from Accenture getting value from the cloud means optimising in four key areas: innovation, consumption, cost and performance.
“Whatever type of cloud environment you choose, the responsibility of managing your IT still falls to you, not the cloud provider,” points out Accenture.
Break with tradition to get value from cloud
The report Get the most out of cloud: Optimise your IT, highlights when it comes to managing your cloud estate, traditional "change management" approaches will not be adequate.
“There's no such thing as running and decommissioning physical hardware over multi-year cycles, as is common in traditional IT. Instead, capacity, consumption, cost, performance and business innovation must be continuously optimised.”
“This unprecedented pace of change demands a different way of working: an agile, cloud-optimising approach that Accenture calls Run Different.
Key points from the report include:
- Managing a cloud estate needs a new IT paradigm with dynamic operating models
- Getting value means optimising innovation, consumption, cost and performance
- Focus on optimise data, edge, networking and machine learning alongside the cloud
Accenture recommend that before a business overhauls its cloud estate they should consider the following questions:
- How should the operating model change?
- What new skills do I need?
- How should I use automation?
- How can I enforce governance through code?
- Who is responsible for security?
Optimising for new heights
Getting value in the cloud means optimising in two key areas.
According to Accenture when IT tracks and assesses new hyperscaler services, it not only improves optimisation of the cloud estate it can also push innovation and help drive growth.
“However, there often isn’t time to evaluate the innovation potential of every release. This, too, builds a case for seeking expertise from outside the organisation. It's also a reason to create a Cloud Centre of Excellence (CoE) - a dedicated team that combines business and technical expertise to assess the potential of each cloud release.”
Consumption, cost and performance
“Optimising this involves understanding the complex interplay of cloud consumption and business processes while continuously monitoring the full stack,” says Accenture.
Machine learning can also help. “It can predict how an application’s computer should change over time with user behaviour, positioning you to better predict spikes, optimise your consumption, find the right balance between reserved and dynamic cloud instances, and add capacity when needed.”
Power of FinOps transparency
Workloads are often forgotten in the cloud raking up costs and consumption.
According to Accenture FinOps - the financial management of cloud - can help by building financial transparency into cloud operating models via a chargeback mechanism exposing the true financial cost of cloud.
“When individual application teams take responsibility for their own cloud usage and cloud costs, they’re incentivised to minimise them. The whole organisation then becomes better aligned around the total cost of ownership of the cloud estate.”
The report states that more companies are turning to third parties for their cloud. Accenture research shows that 48% of those using third-party managed services “to a great degree” report achieving the full benefits of cloud (compared with just 35% of those that don’t).
“Build optimisation, innovation and the adoption of new cloud capabilities into your day-to-day operations. That's how you run differently in the cloud—and that’s how you get the most value out of your transformation journey,” concludes Accenture.
Chinese Firm Taigusys Launches Emotion-Recognition System
In a detailed investigative report, the Guardian reported that Chinese tech company Taigusys can now monitor facial expressions. The company claims that it can track fake smiles, chart genuine emotions, and help police curtail security threats. ‘Ordinary people here in China aren’t happy about this technology, but they have no choice. If the police say there have to be cameras in a community, people will just have to live with it’, said Chen Wei, company founder and chairman. ‘There’s always that demand, and we’re here to fulfil it’.
Who Will Use the Data?
As of right now, the emotion-recognition market is supposed to be worth US$36bn by 2023—which hints at rapid global adoption. Taigusys counts Huawei, China Mobile, China Unicom, and PetroChina among its 36 clients, but none of them has yet revealed if they’ve purchased the new AI. In addition, Taigusys will likely implement the technology in Chinese prisons, schools, and nursing homes.
It’s not likely that emotion-recognition AI will stay within the realm of private enterprise. President Xi Jinping has promoted ‘positive energy’ among citizens and intimated that negative expressions are no good for a healthy society. If the Chinese central government continues to gain control over private companies’ tech data, national officials could use emotional data for ideological purposes—and target ‘unhappy’ or ‘suspicious’ citizens.
How Does It Work?
Taigusys’s AI will track facial muscle movements, body motions, and other biometric data to infer how a person is feeling, collecting massive amounts of personal data for machine learning purposes. If an individual displays too much negative emotion, the platform can recommend him or her for what’s termed ‘emotional support’—and what may end up being much worse.
Can We Really Detect Human Emotions?
This is still up for debate, but many critics say no. Psychologists still debate whether human emotions can be separated into basic emotions such as fear, joy, and surprise across cultures or whether something more complex is at stake. Many claim that AI emotion-reading technology is not only unethical but inaccurate since facial expressions don’t necessarily indicate someone’s true emotional state.
In addition, Taigusys’s facial tracking system could promote racial bias. One of the company’s systems classes faces as ‘yellow, white, or black’; another distinguishes between Uyghur and Han Chinese; and sometimes, the technology picks up certain ethnic features better than others.
Is China the Only One?
Not a chance. Other countries have also tried to decode and use emotions. In 2007, the U.S. Transportation Security Administration (TSA) launched a heavily contested training programme (SPOT) that taught airport personnel to monitor passengers for signs of stress, deception, and fear. But China as a nation rarely discusses bias, and as a result, its AI-based discrimination could be more dangerous.
‘That Chinese conceptions of race are going to be built into technology and exported to other parts of the world is troubling, particularly since there isn’t the kind of critical discourse [about racism and ethnicity in China] that we’re having in the United States’, said Shazeda Ahmed, an AI researcher at New York University (NYU).
Taigusys’s founder points out, on the other hand, that its system can help prevent tragic violence, citing a 2020 stabbing of 41 people in Guangxi Province. Yet top academics remain unconvinced. As Sandra Wachter, associate professor and senior research fellow at the University of Oxford’s Internet Institute, said: ‘[If this continues], we will see a clash with fundamental human rights, such as free expression and the right to privacy’.