How Tableau Next & Data 360 Turn Business Data into Value | Dub Dub

 by Sarah Burnett & Fiona Crocker | Co-Founders, Dub Dub Data

If you have been in the Tableau or Salesforce world for a while, you have probably felt the tension building. On one side, teams want the freedom, depth, and creative control they have in Tableau Classic. On the other, businesses are demanding something more pragmatic. They want answers where work happens, and they want those answers to translate into action without a handoff, a ticket, and a two week wait.

That is why the Tableau Next conversation matters. It is not just a product update. It is a shift in how analytics gets delivered, who it is built for, and where it lives. Tableau Next is not trying to win a beauty contest in the BI tool market. It is trying to make Salesforce users more effective by putting analytics directly in the operational flow.

This blog unpacks four big ideas from the podcast episode that inspired it: what Tableau Next is really doing inside Salesforce, what Data360 actually is, how connected analytics turns insight into action, and why the semantic layer plus AI is reshaping data roles. If you are responsible for analytics, CRM, marketing ops, sales ops, or data strategy, you will see exactly why this topic is worth your attention right now.

TL;DR

Here at Dub Dub Data, we believe Tableau Next is redefining analytics inside Salesforce by combining Data Cloud, AI, and a strong semantic foundation to turn insight into action. Instead of standalone dashboards, organisations can embed trusted metrics, agentic analytics, and guided decision-making directly into everyday workflows. The result is faster decisions, better data access, and real business value, without replacing Tableau Classic overnight.

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Tableau Next and Salesforce: A New Model for Analytics Integration

For organisations already invested in Salesforce, the emergence of Tableau Next represents more than a product update. It signals a shift in how analytics is expected to operate inside the business. Instead of analytics living in a separate reporting layer, Tableau Next is designed to work directly within Salesforce, aligning insights with action and decision-making.

This tighter relationship between Tableau and Salesforce changes expectations. Analytics is no longer something you check after the fact. It becomes part of the daily workflow, supporting frontline teams as they prioritise work, engage customers, and respond to change. The real value comes from integration that feels native, not bolted on.

Why Tableau Next Exists in the Salesforce Ecosystem

The strong reactions make sense because Tableau users are used to flexibility. Tableau Classic has years of maturity behind it, and many people have built careers on mastering its capabilities. Tableau Next is still early. Feature gaps exist. Visual control is not at parity in all areas. That creates a natural comparison trap where people judge it as a lesser version of what they already know.

But the comparison is flawed. Tableau Next is not trying to solve the exact same problem. Tableau Classic is powerful for exploration, complex builds, and broad enterprise BI use cases across many systems. Tableau Next is oriented towards operational analytics and connected workflows within Salesforce. They can coexist, and in many organisations they should.

From Standalone Dashboards to Embedded Salesforce Analytics

You should pay attention now if you fit into one of these buckets:

• Salesforce heavy organisations where Salesforce is the system of work for revenue, service, or customer ops
  
• Teams with multiple Salesforce orgs that struggle to create a consistent view across regions, brands, or business units
  
• Marketing and sales ops teams who spend too long turning insights into lists, segments, campaigns, tasks, or follow ups
  
• Data leaders trying to reduce dashboard sprawl, improve metric trust, and enable faster decision loops
  
• Tableau teams who want a strategic pathway rather than being surprised by the market shifting around them
  

If you are none of the above, Tableau Next may still be interesting, but it is unlikely to be urgent.

Data Cloud (Data 360): The Foundation for Trusted Business Data

Data360 is often discussed like a buzzword. In reality, it is a practical attempt to solve a very real problem that analytics teams face every day: customer information is fragmented, duplicated, and inconsistent across systems, and traditional data tooling does not fully resolve identity and business logic issues.

What Data Cloud and Data 360 Actually Do Inside Salesforce

Data360 has roots in the customer data platform concept. In plain English, it was designed to consolidate customer profiles across channels so that businesses could recognise the same person even when they appear in different systems or with different identifiers. Think website activity, in store purchases, support interactions, loyalty programs, account sign ups, family accounts, and business accounts. Many organisations have pieces of a customer story scattered everywhere.

The useful idea here is not just centralising data. It is building a unified profile that can link out to multiple underlying records without forcing you to overwrite or destroy the original sources. That matters because the real world is messy. Data is collected for operational reasons, and changing it at the source is often slow, risky, or politically painful.

A unified profile model allows you to keep source records but still make decisions as if you have one customer view. It also allows you to define rules that decide which information takes priority in different contexts. That is the kind of nuance that makes customer analytics usable, not just technically correct.

A purpose built data layer for Salesforce first

Data360 has evolved into something broader than the original CDP framing. It is now positioned as a purpose built data environment that is strongly oriented towards Salesforce objects and Salesforce shaped logic. This is a key point. It is not just a generic lake or warehouse feature. It is shaped for how Salesforce data behaves, how Salesforce is configured, and how Salesforce organisations operate at scale.

It also supports connections to external platforms such as Snowflake or Databricks for organisations that already have broader data estates. For some teams, Data360 becomes a consolidation layer that pulls together Salesforce first, then enriches it with other sources. For other teams, it becomes a bridge that makes Salesforce data more usable and more consistent without requiring a heavy engineering rebuild.

What changes when organisations implement Data360

When organisations introduce Data360, they often shift out of one of two frustrating states:

1. They are blind inside Salesforce because native reporting is limited, and multi object analysis is clunky. They can see isolated slices but struggle to answer end to end questions.
   
   
2. They have data outside Salesforce in a warehouse, but the meaning is hard to recover. The business logic and operational context do not travel neatly into the warehouse, so teams can extract data but cannot reliably interpret it.
   
   

Data360 improves both situations by retaining more of the operational logic and making it easier to unify and relate data in ways that align with how the organisation actually functions. It is not magic, and it is not always automatic, but it creates a more realistic path to usable answers.

From Dashboards to Decisions: Unlocking Business Value with Tableau Next

Why Traditional Dashboards Fail to Drive Action

Traditional analytics often stops at the dashboard. It tells you what happened, but it rarely helps you decide what to do next. Tableau Next challenges that pattern by focusing on analytics that can drive action directly inside Salesforce.

Instead of building more static reporting, teams can focus on the few metrics that actually influence outcomes. Whether that is identifying customers at risk, surfacing high-value opportunities, or prioritising service work, Tableau Next helps turn insight into movement.

This is where organisations begin to unlock real business value. Analytics becomes something teams actively use, not something they passively review in monthly meetings.

How Tableau Next Helps Turn Metrics into Action

Here are examples of connected analytics outcomes that matter, beyond the dashboard:

• Customer reactivation: identify customers with high lifetime value who have not engaged recently, segment them, and launch a campaign without exporting spreadsheets
  
• Service performance: detect emerging case trends and create targeted follow up workflows, coaching tasks, or knowledge base updates
  
• Sales productivity: identify accounts that match ideal customer patterns and surface a prioritised call list directly in the sales workflow
  
• Experimentation at speed: split audiences into test groups, run two approaches, and measure uplift without requiring a complex analytics project
  
• Operational triage: spot anomalies in fulfilment, delivery, or returns and create a case, escalation, or remediation plan directly from the insight
  

In most businesses today, each of these takes days or weeks because analytics and action are separated by process gaps. Connected analytics aims to collapse that gap.

Why semantic modelling is the enablement layer for action

Action requires trust. Trust requires consistent definitions. Consistent definitions require semantic modelling. If a marketer or a sales manager is going to take action from an analytics result, they must trust what the metric means, what the segment includes, and how the logic was applied.

That is why connected analytics is not a UX problem. It is a modelling problem first.

The Semantic Layer: The Key to Scalable Metrics and AI Analytics

One of the most important concepts underpinning this shift is the semantic layer. Without a shared understanding of what data means, no amount of AI or automation will deliver reliable outcomes.

Tableau semantics provides a way to define relationships, calculations, and business logic once, then reuse them across experiences. This reduces confusion, improves trust, and prevents teams from recreating logic in every dashboard or report.

When semantics are handled properly, teams spend less time debating numbers and more time acting on them. It also creates a safer foundation for AI-driven experiences, because the system has context about how data should be interpreted.

What a strong semantic layer includes

A properly built semantic layer typically includes:

• Clean, intentional relationships between customer, account, opportunity, product, service, and time concepts
  
• Standard measures and metrics such as lifetime value, churn risk, conversion rates, service resolution, pipeline coverage, and attainment
  
• Business rules baked in such as inclusion criteria, deduplication logic, priority rules, and context specific definitions
  
• Reusable calculations that stop teams rebuilding the same logic in every dashboard, every workbook, and every report
  
• Field descriptions and context so that humans and AI tools understand what a field represents and how it should be used
  
• Security and visibility logic that ensures people see only what they are meant to see, especially in operational contexts
  

This is not glamorous work, but it is the work that makes everything else scale.

Why dashboards get better when semantics get better

When the semantic layer is robust, dashboards stop being dumping grounds. You do not need fifty filters, twenty tooltips, and endless tabs to cover every question. You can design a cleaner experience that answers the top questions, then allow users to explore or ask follow up questions through AI powered interfaces without breaking definitions.

This is also where Tableau Next begins to look less like “a dashboard tool” and more like an operational analytics layer.

AI, Agentforce and the Rise of Agentic Analytics

AI is rapidly reshaping expectations for analytics, but its real value emerges when it is grounded in structure. Within the Salesforce ecosystem, Agentforce represents a move towards AI-assisted decision-making that operates within defined guardrails.

This is where agentic analytics comes into play. Instead of AI simply answering questions, it can assist users in exploring scenarios, identifying patterns, and recommending next steps based on trusted definitions.

AI does not replace human judgement. It augments it. When paired with strong semantics and integrated data, AI becomes a multiplier rather than a risk. Tableau Next is positioned to leverage these capabilities in a way that feels practical, not gimmicky.

How AI Changes Data Access and Decision-Making

AI can deliver real value when it supports the tedious parts of semantic work and accelerates iteration. Examples include:

• Drafting field descriptions that humans can refine, making documentation less painful
  
• Suggesting relationship patterns or modelling improvements based on observed usage
  
• Allowing users to ask natural language questions against a governed semantic layer
  
• Highlighting where definitions are ambiguous or where the model needs clarification
  
• Supporting faster hypothesis testing by reducing the time from question to first answer
  
  

The highest leverage use is when AI helps teams refine and improve governance, rather than skipping it.

What AI is overhyped for

The most overhyped idea is that AI agents will magically build your semantic models for you without deep business context. That is not realistic in heavily customised Salesforce environments where the meaning of fields and the workflow behind them varies across teams, orgs, and regions.

AI will not know your unwritten rules unless you codify them. That is why semantic work is not going away. It is becoming more valuable.

How Data Roles Will Shift: Consultative, Model First, Outcome Driven

As analytics becomes more embedded in systems like Salesforce, the role of the analyst is also changing. Purely building visual outputs is no longer enough. The most valuable analysts are those who understand business context, define semantics, and enable others to act confidently on data.

This shift rewards professionals who can translate ambiguity into structure and help organisations leverage analytics for real outcomes. Tableau Next supports this evolution by encouraging models that scale across teams, rather than one-off builds.

The roles that will grow

The following roles and capabilities will become more valued as connected analytics expands:

• Semantic modellers who can define relationships, metrics, and business rules that scale across teams
  
• Analytics translators who can turn vague stakeholder needs into clear questions, definitions, and measurable outcomes
  
• Connected analytics designers who understand both the data and the workflow, and can design experiences that drive action
  
• Enablement leaders who upskill the business to self serve safely while maintaining governance and trust
  
• Experimentation partners who help teams run measurable tests and learn faster, rather than delivering static reporting
  

These roles sit closer to business outcomes, and that is why they will be prioritised.

The roles that will be pressured

Roles built primarily around building visuals to spec, without owning the problem, the logic, or the outcome, will feel pressure. That does not mean visual skills are irrelevant. It means visual skills are not enough on their own. If a task can be standardised as best practice, it can eventually be automated.

The safe path is to become the person who understands how the organisation runs and can encode that into models and workflows.

Tableau Next vs Tableau Cloud: Choosing the Right Analytics Approach

A common question is how Tableau Next compares to Tableau Cloud. The reality is that they serve different but complementary purposes. Tableau Cloud remains a powerful option for broad visualisation, exploration, and enterprise reporting across multiple systems.

Tableau Next, by contrast, is optimised for Salesforce-centric use cases where analytics must align tightly with operational workflows. Many organisations will benefit from using both, depending on audience and context.

Thinking in terms of coexistence rather than replacement allows teams to adopt new capabilities without disrupting what already works.

When Tableau Classic is the right answer

Tableau Classic remains strong when you need:

• Rich visual flexibility and mature chart types
  
• Broad enterprise BI across many platforms and datasets
  
• Deep exploratory analysis and ad hoc investigation
  
• Complex builds that require full control over design and interaction patterns
  
• A stable, proven environment that supports existing governance and adoption
  

If Salesforce is not your core system of work, Tableau Classic will likely remain your anchor.

When Tableau Next Is the Better Fit for Salesforce Integration

Tableau Next becomes compelling when:

• A large portion of users work inside Salesforce daily
  
• Your analytics needs to trigger workflows, tasks, campaigns, or operational actions
  
• You want aligned governance with Salesforce admin and security structures
  
• You want to standardise semantic models and scale consistent definitions across operational teams
  
• You want to reduce friction between insight, segmentation, and execution
  
•  

Why coexistence is a smart strategy

For many businesses, the most practical approach is coexistence. Tableau Classic can serve the broader BI and deep analysis needs, while Tableau Next supports operational, in flow analytics within Salesforce. The common thread becomes semantic definitions and governance, not a single tool.

This approach also reduces risk. You can pilot connected analytics use cases without forcing a full platform change.

Conclusion

Tableau Next represents a meaningful step forward in how analytics can work inside Salesforce, not as a standalone reporting tool, but as an integrated analytics platform that supports action. When combined with Data Cloud, Tableau semantics, AI, and emerging capabilities like Agentforce and Tableau Pulse, organisations can move beyond static reporting and start embedding analytics directly into everyday workflow. The real opportunity lies in how teams unify data, define trusted metrics, and leverage agentic analytics to make faster, more confident decisions. For businesses willing to invest in strong foundations and thoughtful integration, Tableau Next does not just show what happened, it helps decide what happens next.

Explore Tableau Next Analytics with Dub Dub Data

If this has sparked ideas about how you could unify your data, empower your teams and move from static reporting to agentic analytics, now is the right time to experiment.

Dub Dub Data works with organisations to:

• Design Tableau Next proofs of concept that are realistic and high impact
  
• Shape semantic layers that reflect real business complexity
  
• Enable teams to use agents safely and effectively
  
• Integrate Tableau Classic, Tableau Next and Salesforce in practical ways
  

If you are ready to see what Tableau Next could do in your organisation, book a consultation with Dub Dub Data. We will work with you to identify a sharp use case, map the data landscape and design a roadmap you can execute with confidence.

D30 Tableau Next & Data360: Why Salesforce Customers Should Pay Attention

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🎙️ Unscripted. Uncensored. Undeniably data.

Summary

In this episode of UnDUBBED, hosts Sarah and Fiona engage with Kirk Munroe, co-founder of Paint with Data, to discuss the evolution of Tableau within the Salesforce ecosystem, focusing on Data360 and Tableau Next. They explore the challenges and opportunities presented by these tools, emphasizing the importance of building semantic models and adapting to the changing landscape of data analytics. Kirk shares insights on the future of analytics, the role of data professionals, and the necessity of embracing new technologies while maintaining a strategic approach to data management.

Key Takeaways from the Podcast

• Kirk Munroe emphasises the importance of embracing change in analytics.
• Data360 aims to unify customer profiles across multiple platforms.
• The transition to Tableau Next is essential for Salesforce users.
• Building semantic models is crucial for effective data analysis.
• Data professionals should focus on understanding business context.
• The future of analytics will be more consultative than ever.
• AI poses risks but also opportunities in data analytics.
• Gathering requirements is key to successful data projects.
• Data Cloud allows for better integration of Salesforce data.
• The potential of connected analytics is significant for organisations. 

Podcast Chapters

00:00 Introduction to Tableau and Kirk Munro

01:53 Understanding Tableau Next and Its Marketing Challenges

04:12 The Evolution of Data360 and Its Importance

06:36 Kirk Munro's Journey in Data and Analytics

09:44 Exploring Data360: Unified Customer Profiles

10:42 The Future of Data Applications and AI

17:42 The Challenges of Legacy Systems in Banking

17:53 Implementing Data360: Transforming Data Accessibility

20:20 Customization in Salesforce: A Double-Edged Sword

22:05 Transitioning to Tableau Next: What You Need to Know

26:04 The Future of Data Roles in the Tableau Ecosystem

30:29 Gathering Requirements: The Key to Effective Data Solutions

32:29 The Importance of Semantic Models in Data Analytics

38:17 Advice for Data Leaders: Improving Data Models

43:58 The Role of Data in Marketing Campaigns

46:14 Building Effective Data Models

48:50 Understanding Causation and Correlation in Data

51:34 The Future of Tableau Next

52:36 Strategic Recommendations for Tableau Next

01:00:35 Rapid Fire Insights on Data Analytics

Links

Kirk Munroe on LinkedIn: https://www.linkedin.com/in/kirkmunroe/

Paint with Data: https://www.paintwithdata.com/

Data Modeling with Tableau: https://www.amazon.com/Data-Modeling-Tableau-practical-building/dp/1803248025

Kirk Munroe: 4 Common Tableau Data Model Problems…and How to Fix Them: https://www.flerlagetwins.com/2023/01/data-.html

Kirk Munroe: So What is Tableau Next? https://www.flerlagetwins.com/2025/07/tableau-next-1.html

Master the Tableau Data Model & Relationships featuring Kirk Munroe: https://www.youtube.com/watch?v=OE25-cK2ZHI

Data Modeling Masterclass with Kirk Munro: https://www.nextleveltableau.com/modeling

Keywords

Tableau, Salesforce, Data360, analytics, Kirk Munroe, data strategy, semantic models, Tableau Next, business intelligence, data visualization