It all boils down to privacy. Data has the potential to support the discovery of new medical treatments. It could transform healthcare for the better — and it is hard to find anyone who would not be in favour of that. But at what price? Regulators seem to have decided that in some cases the price is too high.
Data is, of course, the lifeblood of AI.
Recently, David West, CEO of Proscia told Information Age that “AI-powered digital pathology, changes the game and helps fill the shortage of pathologists, who are needed to accurately diagnose millions of cancer cases every year.” And West sees this trend gaining ground as “as health care organisations upgrade to electronic health record systems, which potentially support more data accessibility and interoperability–two technology trends necessary for successful AI adoption.”
Then there is its importance to the economy. According to McKinsey, if data is allowed to flow freely, then global GDP grows at between $250bn and $450bn more rapidly. According to PwC, AI could boost global GDP by $15 trillion by the year 2030.
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But if the price we have to pay is to live in an Orwellian world, maybe that price is too high.
The EU’s GDPR and other privacy regulations being rolled out across the world in countries like Canada, Japan and Brazil are an attempt to ensure we get the benefits of data without the penalty of lack of privacy.
But GDPR does not always work. How often do you throw your hands up in frustration because you have to read and agree/disagree with privacy policies and opt-in requests, just to get a tiny piece of information? It sometimes takes longer to read the disclaimers and other compliance inspired literature, than get the actual information you need.
According to Sarah Burnett, Executive Vice President and Distinguished Analyst at Everest Group: “Organisations are confusing their ability to share data internally between departments.” She told Information Age that a “lack of understanding of regulations means organisations are limiting what they do with chatbots, for example, or the finance department may not be sharing data with customer contact centre.”
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Does this put countries with less prescriptive privacy regimes at an advantage? The US has a hotchpotch of privacy regulations, but even the rules being introduced in liberal California fall way short of the EU’s GDPR. In the race to master AI, does this put the US at an advantage over Europe?
China takes a lack of concern over privacy to another level. There is its social credit system, for example, gaining points for being a good citizen, including, one suspects saying nice things about the government. Maybe, one day, thinking nice things about the government.
But ‘resistance is futile,’ the collective is strong: and China seems to be winning the AI race, leaving the likes of the UK, the land of Alan Turing, and DeepMind’s AlphaGo, hoping it can find a niche as the land that specialises in ethical AI — kind of data for good.
Then again, as privacy lawyer, Abigail Dubiniecki once told Information Age, garbage in, garbage out. If people don’t think they can trust processors of their data, maybe they will distort the truth, or even lie about the information they reveal. If that is so, if data is inaccurate, what’s the point?
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Automation and data
One of the advantages of automation technologies such as RPA is accuracy of the data, or so says Sarah Burnett. She said: “If a person enters a bit of data, they could so easily switch some numbers, and fixing that error, the further down the process it travels, the more expensive it becomes. You can have people, whose time is very expensive, chasing this error and trying to fix it a long way down the process swim lane. When robots are developed and tested well, they can be 100% accurate with data.”
But the field of data science grows. Analysts are applying their skills to gain insights from data.
Some say that data and automation will take jobs, but in an interview with Information Age, Jeremy Achin, CEO of DataRobot said that there could never be enough data scientists. “If everyone on the planet became data scientists there still wouldn’t be enough.” Data Robot’s solution is to automate the role of the data scientist, but and Achin says that even then, the thirst for data scientists will be unquenchable.
Return of silos
Sarah Burnett also feels that the sheer volume of data that you have to manage and store effectively is a major issue. “Sometimes,” she says “it is easier to keep the silos, and not integrate the data, but then you might miss out on potential, not spotting patterns that you might have spotted before. ”
Add to the equation, agile.
Donald Feinberg, vice president and distinguished research analyst at Gartner said: “The continued survival of any business will depend upon an agile, data-centric architecture that responds to the constant rate of change.”
- Augmented analytics
- Augmented data management
- Continuous intelligence
- Explainable AI
- Graph analysis
- Data fabric
- Conversational analytics
- Commercial AI and machine learning
- Persistent memory servers
It is clear that both the challenge and opportunity of data is immense. This month, Information Age will be looking at all these topics in more detail.