When historians look back at the early 2020s, will they talk about this period as a data renaissance or something darker? The Data Leadership Collaborative recently asked 12 data leaders to consider that question, the future of AI, and the best real-world applications of AI. Here’s what we heard.

A renaissance is a period of revived interest in something, typically framed within a new model. The most famous renaissance blossomed in Europe from the late 14th to the 17th centuries, driven by a rediscovery of ancient Greek and Roman texts (especially Cicero) as well as increased interaction between cultures thanks to trade routes that brought luxury goods to Italy and then across Europe. The impact of the renaissance in literature, philosophy, politics, religion, architecture, art, and science resonated for centuries and led to the Enlightenment. Rigidity began to give way to openness to new ideas, views, and beliefs. 

Are we in a data renaissance? 

Based on this definition, Adam Mico, Principal, Data Visualization and Enablement at Moderna, believes we are living through a data renaissance, and that AI lies at the core. “If you’re not considering AI and applying tools like GPT4, you’re going to be left behind,” he said. “And that’s going to happen very quickly as we are pushing to this new age of data. There will be room for a lot of other things that require human intervention and are not related to AI – design and creating bespoke visualizations, for example – but the problem is that without using AI to clean the data that we’re getting from so many different places, you’re going to be slower and get pushed to the background.”

Some thought the term renaissance might not accurately describe where we are in our data history. Mark Palmer, an advisory board member at several data companies and former CEO of streaming analytics company StreamBase, argued, “I don’t think there’s anything renaissance-like in the data; only that generative AI uses more of it. To me, the real renaissance is happening in how AI is being democratized with new prompt-based interfaces that make it as easy to use as Google.” 

Benny Benford, former Chief Data Officer at Jaguar Land Rover, prefers to define our age as more of a no-code renaissance. “Whether you like it or not, now a prompt is a form of code because it’s the ability to instruct a computer to undertake a series of complex tasks,” he explained. “And, with AutoGPT coming you’ll be able to create programs from prompts.” 

Colm O’Grada, Director of Data and Analytics at Tines, sees parallels between data in 2023 and events from the European Renaissance. “If you think about the political changes, democratization, and the dissemination of information with things like the printing press, then compare that with the last ten years and the challenges we’ve had around data literacy and incremental improvements in platforms for storing, accessing, surfacing information, and doing data analysis, I think we’re at the cusp of a big step change,” he said. “Soon anyone will be able to ask a question of data and not have to worry about writing code or statistics. AI can abstract away that complexity, and even respond visually. A user will be able to get half-decent results by simply asking ‘visualize this data for me’.“

Part of this evolving renaissance will have an impact on data analysts as well as users. “I think it will be both scary and transformative for data,” O’Grada explained, “in that the intrinsic technical skillsets that you need to extract value from data will become less valuable and the business and domain knowledge that you need to make good decisions from data will become more valuable. That feels like part of this big leap.”

Kevin O’Callaghan, Head of Data Analytics at Teamwork, views our current moment as both a renaissance and a reevaluation of our entire data culture. “We look at generative AI being produced just to grab attention, like what does the perfect woman and man look like, and then it makes headlines in the papers,” he said. “It’s making us reevaluate things, looking at information, querying it, and being a lot more data savvy. So I don’t necessarily see it as a renaissance in terms of ‘Yes, something happened before, we’re now taking this perspective.’ But I definitely see a reevaluation of looking at where you have an explosion of new thinking, creativity, and approaches, with businesses certainly wanting to get on board with this. So I think it’s more of a reevaluation of where we are and where we want to go.”

Why is AI generating so much excitement? 

Aisha Quaintance, VP at RelationalAI and Chairman, Executive Data Forum @ Women In Data, feels that the term renaissance is more aligned with the energy she feels about the topic as she hosts conversations about it with Chief Data Officers. At a recent dinner for 50 CDOs in finance, she noted that “the panel was supposed to be on knowledge graphs. But as soon as we invited ChatGPT into the conversation, all anybody wanted to talk about was ‘How do I get on board?’ So I felt like there’s a rebirth of energy or motivation to get on board faster, to do more with data and to do more AI with data. One head of AI at a major vendor reported that he had done 50 executive briefings on AI in the last 30 days. I definitely feel a rebirth of excitement about data going on right now.”

Kat LaSota, Director of Data and Analytics at Embrace Pet Insurance, sees a growing trend of companies embracing data and AI training as a policy as a renaissance of our time. She envisions a future where everyone, regardless of their role, will need data and AI literacy to work more efficiently. Kat also notes the enthusiasm surrounding the integration of data into daily operations and emphasizes the importance of involving everyone in the organization to build a data culture.

“I’ve noticed that renaissance rebirth of excitement as well,” LaSota continued. “How does data tie into what we do every day? How do we use it to make profitable decisions, create better relationships, to tell that story, but in a way that everyone understands and is involved? By using things like natural language to interact with data, a new data culture emerges. This will promote the democratization of data and enable individuals to interact with data to receive meaningful responses that drive informed decision-making and uncover valuable insights.”

Allen Hillery, Program Manager at CUNY’s Black, Race, and Ethnic Studies Initiative and Adjunct Lecturer at CUNY’s Colin Powell School for Civic and Global Leadership, added that AI automation is also creating excitement as people discover what they can now do with data. “In addition to cleaning and visualizing data, people are trying to find ways to automate it. So AI in itself is a topic, but how it can be leveraged is also sparking a data renaissance.”

What other data issues are at play today? 

Data leaders focused on several issues that must be considered in new uses of data. 

Glenn Exton, Head of Customer Business Analytics at NatWest Group, noted that his organization has a strong, purpose-led focus around the use of data for good; in his specific case, good customer outcomes. “There needs to be a rationale on why you would use some of these advanced technologies,” he says, “and to have that embedded into your company. It has to be part of the fabric of the organization and it needs to be in the fabric of a purpose-led strategy.” Setting this tone from the top of the organization is essential. Exton noted that in the UK, regulators are stepping up their role as consumer watchdogs in the financial services sector to enforce more ethical practices as data forms the core of more products. 

Lena Winfree, a technology fellow at Fisk University who also owns an IT solutions company and a workforce development company called LocalTek, shared the educator’s perspective about AI. “In the universities especially, they’re panicking,” she said. “They’re panicking about ChatGPT, they’re panicking because they’re unsure to how assign a paper when it can be done with AI. They’re trying to figure out how they can utilize the technology but not disrupt the learning environment.”

Winfree added that some universities are still behind the curve when it comes to data and data science. “We’ve had different lectures on what to do with it and what the students need to understand. It’s a distractor for some but an asset for others. I think answering the call and understanding how we can apply AI in academia would be very resourceful for a lot of people.”

As for her corporate clients, Winfree noted that “I feel like it’s our job to explain and show what’s possible utilizing data. AI is completely irrelevant when people do not know what to do or how to even search for the answers they need.”

Laura Madsen, Co-Founder and Chief Blow-Stuff-Up Officer of Moxy Analytics and an expert in data governance, noted that a precursor to enjoying the many benefits and uses of AI in many companies is organizing data more effectively. “I’m in the guts of a lot of these programs and the data is a mess. Programmatically these companies can’t manage their data very well, there are often no concepts of governance, so people can’t ask solid questions about their data. I see a big gap between immaturity in using data and people being able to ask any question of their data and get visualizations and statistics. How do we help our clients fill that gap as quickly as possible so that they can really get to all of these things?”

Kat LaSota of Embrace agreed. “One thing you still need throughout all of this is that data maturity level. You need governance built in no matter what, then you can start adding these generative AI things on top of that. And I think that that’s where some companies fall short. You also want to ask yourself, ‘Is this ChatGPT or this generative AI model effective? What is it doing for my bottom line? How is it affecting my loss ratio?’ You still need that base data to guide you, to show you where you’re going and how you’re doing.”

For Teamwork’s O’Callaghan, the hype of AI often clouds intent or even sound strategy. “Too often people just want to associate their name with a GPT project because it looks like they’re cool and modern and know what they’re doing. But people aren’t quite taking a step back and saying, ‘Okay, what are we going to do with this? What does it matter for my goals or my company’s ambitions? What is it really going to be?’ As leaders, we need to help articulate our end destination. Without that strategy for our data, our governance, and an effective use of our tools, it’s still just noise.”

What are the best applications of AI? 

Mark Palmer noted that a common, mistaken presumption about AI is that it will deliver definitive answers and therefore stands ready to replace human effort – and by extension, jobs. “I agree with Kevin Kelly, the founder of Wired, who compares generative AI to the universal intern. What AI comes back with is often the least common denominator of the answer.”

For Palmer, the collaboration between AI and humans is at the heart of the data renaissance. “I think AI becomes very liberating and renaissance-relevant when it is used as a collaborative tool, not some sort of foe or evil force we compete with,” he said. “It’s not magically fixing data quality or magically coming up with answers that are bachelor’s degree worthy. It’s just an intern that’s giving you an average answer. Being able to leverage it still requires human thought and creativity.”

Benny Benford noted that new AI products look at legacy applications and build data models in minutes that might take a team months to produce. “These products might tip how we think about everything to do with data governance and data quality purely because of AI’s ability to touch so many areas,” he said. “I think we’ll see AI go everywhere because it has instantly become a commodity, and once that happens lots of things become frictionless.”

With this near ubiquity as backdrop, Benford noted that “the biggest question now for organizations is, do you actually understand where your value comes from? Because if you do, it’s really going to aid your decisions when you’re looking at automation. If automation only reduces cost in an area, does that actually increase the amount of value your organization creates? Plenty of things can be automated and make no difference whatsoever to a company's performance. Those who succeed will identify where to automate and use AI to create more value for their customers.”

For Allen Hillery, data leaders need to help their organizations move past AI as a shiny new object to find its best use cases. “I think we all agree that this is cyclical,” he said. “A new technology comes out and we have decision-makers who are not the most expert in data pushing for their organizations to want to leverage the technology, when they haven’t leveraged the last two or three iterations correctly. If renaissance is about self-discovery, companies need to assess their data maturity. And I think one of the concerns I have, as technology gets more and more advanced, is that the impact happens much faster. So I am very concerned about data quality, data governance, and how algorithms are going to be leveraged.”

Are we finally listening to our data leaders?

“I don’t know if excitement is the word, or more like hysteria, but I guess how I feel is that our colleagues and clients are finally listening,” Aisha Quaintance said. “I feel like we’ve been pounding this into their heads that they need to do more with their data and get it ready to be able to take advantage of AI. And now there’s this rebirth because a lot of people I’m talking to don’t know what to do, but they have to get back to their executive team or their board immediately with how they’re implementing it. I see a rebirth of calling all the consulting firms to fix their data. It’s almost like a forced renaissance.”

SAVVI AI Founder Maya Mikhailov suggested that data leaders under pressure to deliver AI gains combine a short-term and longer-term approach. “I think it’s our job as data leaders to not only point out how data is central to AI, but also to help prevent organizations from getting overwhelmed. A lot of companies we’re speaking to just don’t have the patience for data transformation. They don’t want to wait 24 or 36 months for a data transformation project before they can catch up with their peers who are executing with AI now. I think we have to walk a fine line between encouraging the type of data transformation project that will give them long-term success, but also helps them achieve short-term wins. If you don’t, it’s going to make the data community seem oddly pedantic about: ‘First, data, then anything else you want to do.’ I would say give them some wins, show them what they’ve invested in data can give them some wins today, then put them on the path for a better data tomorrow.”

What is the future of AI?

Glenn Exton of NatWest is already looking to the next horizon of AI. In a recent department-wide session he found that his data analysts in particular were thinking about the future of AI, how the technology will persist and where it will go from here. “As a leader of a team, it’s very top of mind how I take an organization forward where there are genuine concerns on what’s going to happen in their future roles,” he said. “They realize that they may have to go through a rebirth themselves to shape and co-create the future together with our customers. A big part of what I do is creating a great place to work for my team where they can bring their best to work knowing that I champion their ideas, belonging, judgment, impact and future.”

“I see AI as a way to free up additional resources as well as the opportunity to be more creative, and to come up with new solutions that you would not otherwise have the opportunity to consider,” Moderna’s Mico said. “You can also look at ways to enable AI to push things along much more fluidly than before. So there’s a lot of things that it can do. It could be positive, it can be negative, and even in the same company it can be positive and negative depending on how they use it.”

Maya Mikhailov sees more creativity than flattening in AI’s future. “I think people who have real creativity and think outside the box will use AI as a paintbrush in their tool set, but they will not use it as the entire tool set. What I see with generative AI, especially in creativity, is that normal is now leveling up. When Photoshop and Canva appeared, suddenly everything prior looked so tacky and horrible, the new normal they created got a lot better as a result. And that’s the same thing that’s happening with generative AI.”

Colm O’Grada of Tines puts AI in the same category as a powerful tool, but just a tool. “When AI is used well, when it’s put in some clever product wrapper, it reduces the technical overhead to do certain things, be it code or learning skills you need. To follow on Maya’s point, you don’t need to learn how to use the paintbrush anymore, you can just use AI to do that for you, but you still need to have the creative vision of where you want to get to. I don’t think it’s going to wipe out data analysts or data scientists completely. I think what it will do is force people to put value on different aspects of their roles.”

O’Grada explained that what may be more valuable in the age of AI is being able to have meaningful business conversations. “Think about the kind of analysis you need to do, and the decisions you need to be able to influence,” he said. “Those are things that AI is not going to rapidly displace. If people took a more rational and calm approach to it, they’d see it’s just another tool that reduces the technical overhead you need to get certain things done, and potentially increases efficiency. Beyond that, it doesn’t dramatically change the utility of having an analyst or a team. I think you still need those things, but if you’re smart about using AI, you’re going to get a lot more out of those existing resources.” 

Mark Palmer sees the implications of AI as transformational. “It’s not just chat, it’s not just text, it’s not just art. There are multiple levels of implications of generative AI. Kevin Kelly likened it to the period photography started in the 1800s. Painters revolted and dismissed it as a tool. Yes, it raises the value of a really good data analyst or somebody that really understands data, but it also dramatically changes the utility or the ability they have to be creative and do their job. We don’t just have better painters because of photography; we also have master photographers. To me AI has only just begun.”

Benny Benford saw a similar connection, since creativity and art often flourish in the wake of scientific breakthroughs. “Look at what AI has done already,” he said. “Biology is leaping forward decades. Engineers are discovering new materials. As Steve Jobs said, creativity is just connecting things. AI is making new connections itself and supporting people to make new connections at an astonishing rate.”

“AI is also going to free people up,” he said. “There are a lot of people in data who have incredibly monotonous jobs where there are processes that could have been automated a long time ago. Now they’ll be able to spend time on other things based on having more capacity. And that’s got to be good for creativity, whether that’s enabling more variety when you’re making products or adding personalization. We’re already seeing a boom in creativity, but it doesn’t mean the AI is necessarily going to be doing the creativity. It helped me a lot in a post I wrote recently, in part because I don’t know many people with a working knowledge of both Japanese history and the UK Industrial Revolution.” Benford added, “Of course, I had to check the references.”

Is AI a force for democratization?

Although commentators have claimed that AI creates a less equal playing field for some workers, the DLC panelists ended on an upbeat note – that AI is a force for democratization, much as Gutenberg’s printing press democratized print and drove higher literacy over the course of the Renaissance. But as in any conversation about technology, their optimism came with caveats.

“The good news is that it’s becoming very clear that the cost of entry with AI is lower than people expected,” Kevin O’Callaghan explained. “The Googles of the world are finding themselves on the back foot compared to open source alternatives, where anyone who can get a few AWS credits can put something useful together in terms of the platform space. People don’t have any moats around their products here, so anyone can get involved. The costs are low and largely born by the platform owners, who seem to be happy to take a loss for the time being. So from a democratization point of view, there don’t seem to be the same barriers we’ve seen with other technologies in the past.

“The difference,” O’Callaghan continued, “is that this time the challenges will be cultural, based on a lot of the models and the data that AI is being trained on. Hopefully there’ll be innovation around that to balance global and local perspectives and how we’re able to use them as a result.” 

Mark Palmer concurred. “I think it’s just the beginning of the democratization of the interface. Again, I’d caution all of us not to conflate generative AI with ChatGPT. That’s just text and that’s just the general corpus of the internet it’s basically trained on. It’s the wisdom of the crowds writ large. I also work with a database company that’s adding a prompt-based interface to democratize access to their proprietary database. Neural nets and a lot of the algorithms for GPT have been around for a while, but what’s new is the interface. What we’re going through now is like the birth of iPhones or personal computers. Conversational prompt-based interfaces are also part of that revolution.”

“If you think of the Renaissance in terms of democratization of information, I think you’ll see that in new prompt-based interfaces on a lot of things,” Palmer concluded. “Which to me says we’re not even in the first pitch of a nine inning ball game on that score. There’s a lot more to be done way beyond just internet text. That’s all I would say about democratization and AI in general. I think it’s only just begun.”