This article is more than 1 year old
Tired: Data scientists. Wired: Data artists
Asking really good questions about what data can describe matters more than collecting more info
Data scientists are important, but what the world needs now is data artists, according to analysts at Gartner's Data and Analytics Summit in Sydney, Australia.
Analysts Sally Parker and Peter Krensky explained that data artists are people who ask wider – even perhaps tangential – questions about data, and what it might reveal once probed by data scientists.
To illustrate the concept they shared a case study of a public transport operator in Belgium that used data science in an attempt to learn why some of its vehicles broke down. Mindful of the potential for project sprawl, an analytics team developed a 20-day project plan with half the time spent preparing data and the rest dedicated to developing a model.
That effort did not produce a useful insight – but it did bring the data team in contact with the operations team. Once the latter saw the data about the broken-down buses, they quickly demonstrated they were being used on hilly routes and therefore encountered more stresses.
Rotating buses across different routes spread the load and saved tens of millions.
The analysts didn't offer any insight into how to develop data artists. Rather, they suggested that cross-disciplinary collaboration, and being careful not to assume data contains hoped-for insights, are useful steps.
The pair shared the story of 16th century astronomer Tyco Brahe, who conducted extensive observations to prove his belief that Sun orbits the Earth. Brahe's collaborator Johannes Kepler used the same data to prove that the Earth orbits Sol.
"Sometimes all we need to succeed is to change perspective," Krensky said.
- Oracle and Huawei clouds the big movers on Gartner's conjured quadrilateral
- Foldable smartphones crawl to one percent of global market share
- Hybrid work not working? Try building an 'intraverse' to fix it, says Gartner
- Surprise! The metaverse is going to suck for privacy
The pair also recommended that organizations collect less data, because collecting bulk data creates security risks. The pair cited the example of a hotel chain that analyzes customers using just two data points: whether they use the gym, and if they choose healthy food. Those two nuggets are enough to tailor offers and are of far less value to criminals than other items of information.
Both analysts also advocated the use of synthetic data, because it is cheaper to collect and creates fewer privacy challenges.
Synthetic data can also offer the chance to simulate events that are hard to observe. The analysts said Alphabet's self-driving car tech outfit Waymo uses it to simulate making way for ambulances weaving their way through traffic at speed, and to do more tests of self-driving cars than is possible on actual roads.
The keynote also offered a recommendation for IT teams to become "data concierges" who invest in metadata so that business teams can quickly identify and wield the data they need, rather than "data plumbers" who focus on infrastructure. ®