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Gartner Data & Analytics Summit: Unleash innovation on emergence from pandemic
Analysts at the Gartner Data & Analytics Summit in London urged D&A leaders to unleash innovation as their organisations emerge from the shadow of the pandemic
Gartner analysts exhorted data and analytics leaders to unleash innovation at the firm’s EMEA data summit, now that their organisations are emerging from the Covid-19 pandemic.
While knowledge workers coped during the great work-from-home experiment of 2020-22, it is now time to prioritise creativity and innovation, said the analysts at this week’s Gartner Data & Analytics conference in London, held in person for the first time since 2019.
Alys Woodward, senior director in the emerging technologies and trends group at the firm, drew a distinction between creativity and innovation in the opening keynote that she and colleague, Erick Brethenoux, key initiative leader in Gartner’s artificial intelligence (AI) team, gave.
“We need innovation which goes beyond creativity that is about having a great idea; innovation is about using that idea to drive change,” she said. “The companies that can handle uncertainty the best navigate the future more successfully.”
Noting the conference strapline of “unleash innovation, transform”, Brethenoux added: “You’ve told us your biggest challenge is getting your organisations to use your analysis to drive change.”
The duo explored topics such as how data governance helps or hinders innovation, and how “big data thinking” has been frustrating the extraction of business value from data. The cloud has not helped, they said, by encouraging the bad habit of accumulating as much data as possible. Data is, first, a liability, said Woodward. “‘Give me all the data’ is not a strategy,” she said. “We want data that makes us smarter.”
They stressed the value of metadata (data that describes data) to which machine learning has been applied, so that organisations better understand what data they have. They also vaunted the value of what they called the “data fabric”, by which they mean a data management architecture that brings decentralised data assets together.
Small data
In the keynote, they also promoted the idea of “small data”, on a minimal viable model. Brethenoux asked: “How innovative can we be in figuring out the smallest amount of data that we need to know?”
Woodward took their argument on to synthetic data, artificially created data that has similar attributes to the data it mimics. The value of that, she said, is that it reduces the liability involved with the storage of sensitive data. She gave an example of Nationwide Building Society, which uses transaction data to generate synthetic datasets to share with third-party model developers without risking the privacy of its customers.
Synthetic data also enables organisations to “move faster and fill in the gaps in their actual data”, she said, which is important for building machine learning models. Gartner estimates that by 2030, synthetic data will almost completely replace real data in AI models. She gave Amazon as an example, which is using synthetic data from Alexa to speed up support for languages such as Brazilian Portuguese.
Innovation requires combustion
Stephen Brobst, chief technology officer at data warehousing supplier Teradata, was a speaker at this week’s summit. He talked to Computer Weekly about his view of the opening keynote.
“It was interesting to me that the opening keynote was all about innovation,” he said. “What I observed is that during Covid, most organisations’ productivity increased, mostly because [people] learned how to work from home, they didn’t have two-hour commutes every day and they had nothing else to do but work.
“I would argue that, although there was more ‘getting stuff out the door’ and getting the job done, innovation was suffering. That’s why I thought the keynote was interesting, because now we’re getting back together, you need that combustion that takes place in front of a whiteboard, when engineers are fighting passionately about how to approach something. You just can’t do that on a two-dimensional screen.
“People say the millennials do it. I haven’t observed that. I think the tools will get better, and so on, but I think humans are social animals, and that innovation and togetherness actually creates ideation, innovation and collaboration.”
Four societal scenarios for businesses
In the closing keynote, Frank Buytendijk, Gartner fellow in its data and analytics group, and Pieter den Hamer, senior research director at the firm, also highlighted the ineluctably social nature of the context in which businesses operate. They pointed to a current big picture of social fragmentation and the risks to businesses represented by that.
“Social fractures occur when we disengage with each other, when we engage only in a polarised way, or when we act negatively towards the common good,” said Buytendijk.
They said data and analytics leaders need to be cognisant of four possible futures for business organisations in society, summed up as: get with the programme; play to your purpose; mind your own business; and wait for it.
The first, “get with the programme”, said Den Hamer, is the possibility that societies will become more cohesive again, and know and express what they want. In this scenario, data professionals will come to emphasise collaboration and the governance that undergirds that. Responsible AI and environmental, social and governance (ESG) initiatives will become more important, in response to stronger regulation – for example, from the European Union.
The second scenario, “play to your purpose”, said Buytendijk, is well illustrated by the phenomenon of the division of business along religious lines, as in Utrecht having two of everything for Catholic and Protestant communities in the 1950s.
Parallel societies can also emerge today, he said, and that entails, for companies, having different messaging for different communities. For data professionals, this increases the salience of data sovereignty in respect of different communities, whether geographically separate or different in some other way. It also elevates the importance of techniques such as sentiment analytics.
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The third scenario, said Den Hamer, of “mind your own business”, takes us back to a world of commercial companies simply ignoring the voice of society. Here, weariness of all talk of wider social purpose gives way to pure shareholder capitalism. Business, in this mode, should focus on products and services, and leave broader talk to others.
The final scenario, “wait for it”, said Buytendijk, is “the most bizarre because society knows what it wants and then nothing happens”.
A huge economic crisis could be the context here. He gave the example of Shell wanting to be carbon neutral by 2050 but in step with the rest of society. For data and analytics professionals, this scenario means investing in social media analytics.
In conclusion, Buytendijk said companies should take action on social issues, no matter which scenario applies most closely to them.
For data and analytics leaders, said Den Hamer, the “getting with the programme” scenario means they should use their expertise for the good of society; “play to your purpose” adherents should use analytics to know their customers better; “mind your own business” neo-liberals, the goal is simply to use data to maximise profit; and for the fourth scenario, data and analytics plays an early warning role.
Buytendijk concluded by citing the pandemic as a phenomenon that was unforeseen, but yet organisations coped with it partly thanks to past attempts to do scenario planning.
As to which of the four scenarios will come to pass, the duo said they did not know, because no one does.
