Get data science out of IT departments and into boardrooms
Where do your data scientists sit? Perhaps they occupy a typically gloomy, computer-filled basement, or maybe they have a glassy building all to themselves. Either way, you’ll not always see business decision-makers walking the same corridors. After all, analytics is best left to the experts, isn’t it?
Yet, back in the boardroom, the statistics and insights that the managers have ordered from the data science department make little sense. How can these be turned into profit? The truth is that where data scientists sit in your organisation is vital – strategically, as well as physically. When they’re separated from decision-makers, data analytics won’t line up very easily with business goals.
I recently had the chance to speak with SAS Collaborators and about optimising the role of the data scientist. A catalyst for positive change, the influence of data professionals should be felt throughout the entire organisation.
One analytics strategy for all to participate in
Data is nothing without strategy. While many business decision-makers stress the importance of data, pointed out that often this appears to be a case of “data FOMO”. These business leaders have a fear of missing out on the potential insights their data has to offer… but in reality, they have no idea what insights they’re looking for.
For all analytics use cases there needs to be a clear understanding first of exactly what the business problem is that you’re trying to tackle, and how analytics can help. Only then can the relevant data be used and the right models created, refined and refreshed that will lead to key business insights to help resolve the problem.
Analytics is for all, as we become more data-driven and analytical in our thinking and our work. But, it’s not up to each department to ‘shake the data hard enough and get results’. Companies must strategically , turning the data into insights which are valuable to all who could benefit from them. This means starting with the right high-quality datasets, selected in collaboration with decision-makers. Then, model design and deployment should prioritise the achievement of the agreed business goals. Finally, the findings should be presented comprehensibly, using visual analytics or simple interfaces tailored for those who need to understand and then act upon them.
Speed is of the essence in analytics, and yet so many projects become a “poisoned chalice”, as stated: passed down through teams and taking years to complete. This can be sped up through collaboration at every stage. Take the furlough scheme. Desperately needed to preserve jobs and businesses through COVID-19, the efficient collaboration of hundreds of policy-makers, data scientists and employers helped within three to four weeks, saving countless jobs.
We are all data scientists
As data becomes more and more pivotal in business, it shouldn’t be left up to the tech experts to advocate its value. Asking data scientists to take on the role of a business analyst is only half the solution; the other route to truly data-driven success is to nurture the evolution of new analyst roles outside of the data science department.
- . While working in their own field – say, supply chain, or customer service – citizen data scientists might create models that leverage analytics specifically for their own department. They can rely on the strength of their contextual knowledge and wider understanding of business priorities to tailor the analytics process to produce insights that their team can act on immediately.
- Data translators. With a breadth of knowledge spanning technical data science and top-level business management, a data translator can turn insights into a more “business-friendly framework”, as Jen noted. As intermediaries between data scientists and decision-makers, they guarantee a process that has impact at scale in the organisation. At the same time, data translators are by default teaching and nurturing each group to think more collaboratively.
- Data literate employees across the business. A sensitivity for the raw data generated from customers or operations isn’t just for business analysts. It has the potential to revolutionise business thinking in all departments and among all staff members. Making this is arguably one of the greatest drivers of reliable growth that businesses can commit to today.
What place does data science have in the future?
Companies aiming to be competitive in the modern day must have a greater focus on data, analytics and insights than ever. But the role of the data scientist is beginning to change.
Ultimately, coding could become a smaller part of what data scientists do, as models become more interface-based. This means data scientists will soon be working to fit machine scale data into existing models, and interpreting the results. They will become responsible for the wider process of managing the insights, and they’ll be constantly reviewing what they mean for the end-user.
In the boardroom, data scientists are invaluable. As Neil pointed out, their knowledge of reference cases can provide the evidence to convince the C-suite of key decisions, informed by data insights. But data science can also have a huge impact on public perception. Organisational ethics is fast becoming a non-negotiable for customers. Sensitivity for how data science capacity can be leveraged worldwide helps organisations to use , to make a difference on issues from deforestation to reducing injustice.
Data-driven businesses are leading the way in the business landscape, and data science is at the helm. When data scientists are stuck in the engine room, their influence on navigation is wasted. By encouraging collaboration, skill-sharing and a broadening of roles among data scientists and others, organisations can be confident of their bearings as they chart a way towards their goals.
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’.