If you want to understand how AI will change the world of work, take a look at the world of data analytics. It's a microcosm of how AI opens up access to disciplines that have previously been a specialist domain. The latest AI capabilities make it possible for many more people across an organization to find answers and insights from analytics, by opening up and automating processes that previously only the specialists could do. But this doesn't mean there's less work for those specialists — the increased use of analytics creates new demand for skilled operators.
Instead of disappearing, the specialists' jobs change, with less time taken up by repetitive tasks and more time spent enabling better use of the technology across the organization. It's a similar process to what we're seeing with the rise of no-code and low-code toolkits in software development, or the emergence of AI-assisted design tools in the field of graphic design. AI-enabled automation makes it possible for ordinary folk to complete their own projects, leading to a higher volume of usage that in turn increases demand for specialized skills.
As a result of these changes, vendors that previously served a specialist community now find their products opening out to a much broader user base. For Tableau, the popular business analytics tool now owned by Salesforce, announcements at this month's Dreamforce conference added fresh momentum to that trend. Salesforce introduced an upgraded Data Cloud, which makes it easier to surface data for analysis, and Einstein CoPilot, which supports users with generative AI, in the case of Tableau helping them to create reports and find insights more easily. These new capabilities add new heft to Tableau Pulse, first announced in May, which delivers AI-enhanced insights to users in the flow of their work, whether inside Salesforce CRM apps or in messaging apps such as Slack and Teams.
Real-time data insights
Instead of focusing on its traditional audience of 'data geeks', these developments continue to broaden Tableau's appeal — a democratization strategy that's already been in place for several years — to reach a much larger cadre of business users. Ryan Aytay, who became President and CEO of Tableau in May, says the goal is to give those users access to the real-time data insights they need:
Who doesn't want to know what's happening in real time? It's not rocket science, but a lot of that stuff hasn't been made possible, because it took too long. The batch process — making a dashboard that comes back, [and then] it's the wrong dashboard. 'No, that's not what I want. Redo it.' That's the old way, from 2000.
The new tools change the process from one in which the data specialist goes away and designs a report or dashboard to order, to become one in which the role of the data specialist is to design ready-made templates and tooling that business users can immediately pick up and use, while standing by to offer assistance when help is needed to plug in new data sources or fine-tune the results. It's much more of a continuous collaboration between specialists and users. Aytay goes on:
If I'm just getting simple amounts of data through, let's say, Pulse, it's helping me with some of the generative capabilities, but then if I want to deep dive into the dataset, I can still open the workbook and I can still ask my analyst a question ...
We're just making it easier, and I think the real-time component of it will help, because if I ask for something in the old world, it takes a few days, and it's irrelevant at some point. But I need it now — I mean who doesn't want anything immediately? So it does accelerate many things.
Changing behaviors and learning new skills
While the technology is the enabler, this new approach also requires some behavioral changes and skills development among both users and specialists. Aytay concedes that Tableau has a role to play here in helping its customers be successful. He says:
AI is one thing that we need to be focused on. But we also need to make sure that people actually understand how to help and leverage the data in these businesses. That's why I keep anchoring on data skills, and this data literacy thing that is so critical. Because what's the one differentiator [that] one company will have over another? It's how they leverage their data, that drove their AI to make the best business decisions, that means this company grew faster and was more profitable than this company, or had a better customer experience than this company.
The way that's going to work, it's not just technology, it's also the people. That is really the journey we're on ... We have to guide our customers in this direction, not just be like, 'Here's the tool.'
For data specialists, one of the most important roles will be preparing the ground so that the data users need is available to them in the form that they need it. This is the significance of Data Cloud, as Aytay explains:
I don't want to oversimplify the fact that just getting the data is very hard. If you start with this chain of, the data is disconnected, we bring it together, we visualize it, we have a recommended action — here's what you should do to take the action is the last mile. That is amazing if you can get there. I think generative accelerates us to that point ...
If I'm an analyst, and someone who's a data professional, I can also focus on the value chain before that, which is collecting and harmonizing the data. That is a big, big problem that I continue to hear about from our customers.
That's why I think Data Cloud is becoming a key component. What we bring to market, with Tableau, is the ability to take action — not just with Pulse, it's to do with Slack or anything in Salesforce. That's that journey that we have to help people with. We can't just go in and be like, 'It's just analytics.'
This journey will inevitably create more demand for skilled data specialists, he adds:
I think in the next 12 to 24 months, we're going to see those data skills accelerate, but where also the community needs to grow. Because there are many people who can move careers and actually go jump into this space and get into large enterprise companies.
They need help. They know the problem. They have the solution in terms of technology, but they still are trying to build this data base.
Extending the impact of analytics
The expansion of the skills base means that, while the centralized model of a single center of excellence or data factory is still important, in most enterprises, data expertise is becoming more distributed out to function-specific data teams. He says:
It's like a hub-and-spoke. The best companies I'm seeing right now are moving from a really central hub-and-spoke, which is owned by IT and they shoot it out to different places, to multiple hubs with less spokes. The finance team has their own Center of Excellence, because it's becoming more accessible, given that we're bringing data to people who have never been analysts.
The impact of AI will also challenge companies to become more agile in how they react to data insights. Generative AI will help analyze trends and understand where changing behavior might make an impact on performance, but the organization must then be flexible enough for teams to then be able to act on those insights. This sets up a cyclical feedback loop when the results of this change come back in the data, surfacing new insights. He explains:
Data we know enriches AI, but AI will enrich the data. If the action is powered by AI, then I can enrich the data. I'll see if I told my other team to make these changes — go to more meetings, to do these phone calls, to implement this sales play — I can quickly see it's working or not working, and I can adjust it. That's enriching the data, ultimately. I may find the result actually wasn't what I thought it was. So a really good data-driven business also has to be agile.
Much of the focus at Dreamforce was on the close integration between Tableau and Salesforce, with the formerly separate CRM analytics product now being folded into Tableau. While enabling other users beyond the CRM function remains important to Tableau, Aytay believes being tightly built-in to Salesforce will continue to expand its market, especially following the announcement of two free Tableau creator licenses for enterprise customers who start using Data Cloud. He adds:
Before Tableau was part of Salesforce, some people ran and added analytics to their CRM experience. But now it just comes prepackaged and deeply integrated. So it's just an expansion of our market. And I think it's going to make the Customer 360 stronger, especially when we're now embedding it inside the product with the unified analytics and Data Cloud.
But Tableau still has much to offer users outside of CRM, he believes:
All functions need data to run ... I think the ability to get a lot of data quickly, and make decisions, it's that speed that I think we're focused on — and unlocking the data, whether it's your customer data, whether it's your finance data, whether it's HR data — being able to do those decisions quickly. That's what we all need. And I think that's where we're going with Tableau.
Connected digital technology, now enhanced with AI, is increasingly opening up and democratizing access to all kinds of specialist capabilities, both within and beyond the enterprise. It's an example of what I've called the XaaS Effect, applied to internal specialist functions rather than external customer engagement:
As functions including HR, finance and IT itself become digitally connected, many routine processes are automated or become self-service, with the ability to track how they're performing for users. Meanwhile, domain specialists find their time is freed up from mundane tasks and instead they become available to offer advice when something unexpected comes up, or if additional help is needed to achieve the desired outcome. Information, know-how and agency becomes more atomically available within a collaborative canvas of cross-functional teamwork.
This is changing the jobs of specialists, forcing them to up-skill in order to provide more enabling capabilities or become online advisors to users within an increasingly powerful digital user experience. Organizations have to adapt too, because to fully realize the benefits of more open access to data analysis, they need to become more agile so that they can respond effectively to the insights it delivers, as Aytay indicates. Being 'data driven' means more than simply reacting to data signals but also understanding how those reactions reflect enterprise values and goals.
The good news is that AI-powered automation is widening access to previously specialist capabilities, making it possible to apply these capabilities across many more use cases. The corollary is that this is disruptive for everyone because new skills are needed. Non-specialists have to learn how to make the most of the tools they're using, while specialists have to take on a more advanced role that emphasizes problem-solving and collaboration. Vendors have to support their existing users and customers through these changes, while learning to support their newly-acquired non-specialist user base. Organizations too have to adapt to a more agile business environment, where individual employees have more autonomy to rapidly respond to data insights.
Putting in the technology is just the first step to achieving success in this new era.