Salesforce Einstein Analytics and Tableau Explained

Key Takeaways:

Salesforce Einstein Analytics was renamed, not retired, and is now CRM Analytics.
The product launched as Wave Analytics in 2014, became Einstein Analytics and then Tableau CRM, and has been CRM Analytics since 2022.
Einstein is Salesforce’s overarching AI brand, while CRM Analytics is the analytics product.
Einstein Discovery, the predictive engine inside CRM Analytics, powers AI forecasting with explainable predictions surfaced directly on records.
CRM Analytics, Tableau, and Tableau Next all have active roadmaps, and Salesforce has published no retirement or migration date.
Existing customers should get roadmap commitments in writing at renewal and watch release notes, packaging, and certification changes.

If you searched for Salesforce Einstein Analytics, you are holding a name the product stopped using years ago. The short answer to the question behind your search: the product is alive, and it is now called CRM Analytics.

Nothing has retired. The platform was renamed more than once, and each name shipped with its own documentation, certifications, and training material. Many pages that still rank for the old name describe the product as though that name were current, so admins, RevOps leads, and IT decisionmakers who trained on it land on a results page full of contradictions.

The product also changed in substance, not just in name. From its Wave Analytics beginnings, it became a platform that runs natively on Salesforce, inherits its security model and metadata, and puts insight inside the records where sales and service teams already work. That native depth is a large part of why it has outlasted every name change.

This guide covers the dated naming lineage, what CRM Analytics does in 2026, and how Einstein Discovery delivers AI forecasting. It also explains how the product relates to Tableau and Tableau Next, how to choose between them, and what existing customers should do next.

As a leading Salesforce implementation partner, Achieva works with all three analytics platforms (implementing them but not reselling their licenses), giving businesses implementation support across their analytics environments.

Table of Content

What Happened to Salesforce Einstein Analytics? One Product, Four Names
What Is Salesforce CRM Analytics in 2026?
How Does Einstein Discovery Deliver AI Forecasting in CRM Analytics?
How Do CRM Analytics, Tableau, and Tableau Next Fit Together?
Which Salesforce Analytics Tool Should You Choose When?
What Should Existing CRM Analytics Customers Do Now?
What Does “Einstein” Mean Across Salesforce?
How Does Achieva Help with CRM Analytics, Tableau, and Tableau Next?
Conclusion
Frequently Asked Questions

What Happened to Salesforce Einstein Analytics? One Product, Four Names

The product most people still search for as Salesforce Einstein Analytics has worn four brand identities in eight years. Each rename brought new capabilities, but the changes outpaced the documentation, certifications, and market memory built around the previous name.

Name	Years	What Changed
Wave Analytics	2014 to 2017	Launched at Dreamforce in October 2014 as Wave, the Salesforce Analytics Cloud, built on the 2013 EdgeSpring acquisition
Einstein Analytics	2017 to 2020Relaunched in 2017 under the Einstein AI brand, alongside Beyond Core’s rebrand as Einstein Discovery	Relaunched in 2017 under the Einstein AI brand, alongside Beyond Core’s rebrand as Einstein Discovery
Tableau CRM	2020 to 2022	Renamed in October 2020, after the $15.7 billion<sup>1</sup> Tableau acquisition closed in 2019
CRM Analytics	2022 to present	Renamed in April 2022 to name the product for its function in the CRM rather than for an acquired brand

Each name left something behind. Wave’s schema-free architecture lets business users analyze millions of rows without building cubes or predefined schemas first, and that foundation still sits underneath CRM Analytics. The Einstein Analytics era, in turn, produced the largest volume of certifications, tutorials, and training material, from Trailhead modules to community tutorials, still circulating online.

The Tableau CRM rename then created immediate confusion. Tableau and Tableau CRM were different products, built on different technology for different users, requiring different skills, but the shared name blurred the line between them. The 2022 rename to CRM Analytics fixed this by describing the product’s function more plainly, and it remains the name used in current documentation and licensing.

One distinction clears up most of the remaining confusion. When Salesforce announced the Tableau CRM rename in October 2020, it stated that “Einstein remains the overall AI brand for Salesforce, represented across the portfolio.” In short, Einstein is the brand, and CRM Analytics is the product.

The older names also live inside the platform. The CRM Analytics REST API still sits under /wave endpoints, and Metadata API types still carry the Wave prefix, such as WaveDashboard and WaveRecipe, so admins and developers meet the original name every time they deploy. In practice, use CRM Analytics with vendors when searching Salesforce documentation, and expect every older name in forums, older tutorials, and your own org metadata.

What Is Salesforce CRM Analytics in 2026?

Salesforce CRM Analytics is Salesforce’s native analytics platform, built for the questions standard Reports cannot answer. It delivers answers inside the records where sales and service teams already work.

1. When Standard Reports Are Not Enough

Standard Reports handle most day-to-day operational questions. But they fall short when teams need cross-object analysis beyond a handful of related objects, historical snapshots that preserve how data looked over time, or blended views that combine Salesforce records with external systems. The warning signs are familiar: metrics living in spreadsheets, KPI definitions drifting between departments, and users asking for interactive exploration instead of static tables.

CRM Analytics fills that gap. But understanding how requires looking at its building blocks described below.

2. Datasets and Recipes

Datasets store data in a format built for fast querying and interactive exploration. Salesforce objects hold live transactional records. Datasets, by contrast, hold prepared snapshots designed for analysis.

Recipes are what build those datasets. A recipe takes Salesforce objects, external sources connected through native connectors, or Data 360 (formerly Data Cloud) objects. Teams use recipes to combine opportunity records with account hierarchies and external financial data, including join types that standard reporting does not support. Recipes can also run on a schedule, so datasets stay current without manual intervention.

3. Dashboards, Lenses, and Analytics Studio

CRM Analytics dashboards deliver interactive visualizations in which selecting one element recalculates the surrounding components to reflect that selection. Lenses are saved explorations that let users test hypotheses without touching existing dashboards. They function as sandboxes for analysis. Analytics Studio serves as the workspace where all of this comes together, the environment where builders create apps, dashboards, and lenses, and where datasets are managed.

4. Native and Embedded Analytics

CRM Analytics runs on the Salesforce platform with the same login and admin tools, and its dashboards embed directly on Lightning record pages. No separate security architecture needs building. No manual performance tuning is required as the user base grows.

Embedded analytics takes this further. Dashboards and insights appear directly on record pages where sales reps and service agents spend their time. Because of this, a rep viewing an account can see pipeline analysis, churn risk, or next-best-action recommendations without leaving the record.

That kind of in-workflow guidance pays off. A 2026 survey of 227 chief sales officers found that sales organizations providing AI-enabled next best actions are 2.6 times2 more likely to achieve commercial growth.

5. Data Cloud Integration and 2026 Updates

Data 360 (formerly Data Cloud) unifies customer data across systems. CRM Analytics visualizes that unified view and queries Data 360 alongside historical Salesforce data. The platform handles structured data from Salesforce, semi-structured data from sensors and mobile apps, and external sources connected through ETL tools or public APIs. It is also administered through standard Salesforce tools rather than separate infrastructure.

Here's What You Need to Know About Salesforce Einstein AI

How Does Einstein Discovery Deliver AI Forecasting in CRM Analytics?

Einstein Discovery is the predictive engine inside CRM Analytics, and it is what delivers the AI forecasting capability. Discovery trains supervised machine learning on your datasets. It supports regression for numeric outcomes, binary classification for yes-or-no questions, and multiclass classification for outcomes with three to ten possible results. No data science team is needed to build the first model, although statistical judgment matters when choosing the data and reading the results.

Model quality still depends on data quality. In Salesforce’s3 State of Data and Analytics report, a survey of nearly 8,000 executives, data and analytics leaders estimated that 26% of their organization’s data is untrustworthy. For Discovery, clean, well-governed datasets come before any prediction worth acting on.

I. Predictions with Explanations

Discovery analyzes historical data, identifies statistically significant patterns, and builds a model that your team reviews and deploys. Each prediction comes with its top predictors, the factors that most strongly influenced the outcome, ranked by impact. The platform also suggests improvements, which are actions users can take to change the predicted outcome. Every prediction arrives with its reasoning attached, not as an unexplained number.

II. Pipeline Risk, Win Likelihood, and Churn Scoring

Teams use Discovery to score pipeline risk, estimate win likelihood opportunities, and predict churn and renewal risk. Pipeline models learn from opportunity history, stage velocity, and owner win rates to score each deal. Churn models combine interaction data, billing history, usage, and service cases to calculate propensity scores along with recommended next actions.

Revenue-trajectory views then roll those scores up into a CRM Analytics dashboard that weighs open pipelines by predicted probability across forecast periods.

III. Write-Back to Records

Discovery writes scores and recommendations back to Lightning record pages. The same outputs can also feed Salesforce flows, CRM Analytics recipes, and Tableau dashboards. As a result, predictions show where decisions are made, not in a separate analytics environment.

IV. How Predictions Inform the Forecast

Discovery scores appear on the same records that teams review during forecast calls, so each deal of uncertainty becomes visible as a probability and the factors behind it. People then weigh the context the model cannot see, such as a champion leaving or a procurement freeze, and decide what goes into the committed number.

V. Einstein Discovery vs. Einstein Forecasting

Sales Cloud includes a separate feature called Einstein Forecasting that predicts quota-facing forecast rollups. It is not the same as Discovery, and mixing up the two is one of the most common errors in content on this topic.

The guiding principle is simple: predictions inform the forecast conversation, and humans commit the number.

How Do CRM Analytics, Tableau, and Tableau Next Fit Together?

Salesforce now owns three analytics products, and the Salesforce Einstein and Tableau story that began with the 2020 rename runs through all of them. Knowing where each one fits matters more than guessing which one will survive.

1. CRM Analytics: Native and Embedded

CRM Analytics is built from the ground up for Salesforce users. The platform brings insight into the flow of work. It sits inside Salesforce pages and follows CRM security and record-level context. When teams need analytics that live within Salesforce workflows and inherit platform permissions, it provides the fastest path to adoption.

2. Tableau: Visualization-First Platform

Tableau operates as the leading visualization-first analytics platform in the Salesforce portfolio. It connects to almost any data source, including Salesforce, deploys on-premises or in the cloud, and scales across departments. Tableau runs as Tableau Cloud or as on-premises Tableau Server, though Salesforce’s newest AI capabilities arrive through the cloud.

3. Tableau Next: The Agentic Analytics Direction

Tableau Next, introduced as Tableau Einstein at Dreamforce 2024, is an API-first, composable analytics experience built on Data 360, with Tableau Semantics as its AI-infused semantic layer. It sells standalone or bundled with Tableau Cloud in the Tableau+ Bundle. Its agentic capabilities run on Salesforce’s Agentforce platform, covered in depth in ours Agentforce guide.

4. Are These Products Merging?

“”One of the downsides with CRMA is that you can’t click into the visualizations and deep dive like you can with Tableau. Our plan has been to take the front end of the user experience and pair it with Tableau’s exploration capabilities. The combination means that we can feed these rich analytics into Sales Cloud, Service Cloud (etc.) UI. Over time, this will become the new CRMA.”

– Ryan Aytay, President & COO Code Metal

All three products maintain active roadmaps as of October 2026. Salesforce’s current public positioning treats the products as complementary: Tableau for end-to-end analytics across every business function, and CRM Analytics for native, actionable insight inside Salesforce workflows. Existing customers keep their investment and support. This guide does not predict a retirement or a date. It sticks to the public record and points out the signals worth watching.

Which Salesforce Analytics Tool Should You Choose When?

Start with two questions: where do users need to see insights, and which systems feed that analysis? The answers sort most organizations into one of three paths, and sometimes into two at once.

I. Choose CRM Analytics When Salesforce Is the Work Surface

CRM Analytics fits when your users spend their day in Salesforce, and you have hit the limits of standard Reports. Typical signs are a need for snapshot history, cross-object questions, or blended external data. Native, embedded analytics wins here because the insight appears on the record the user is already working on. If Reports still answer your team’s questions, stay on Reports. CRM Analytics earns its license only once that ceiling is real.

II. Choose Tableau When Analysis Spans Many Systems

Tableau fits when your business intelligence spans multiple systems and departments; visualization quality matters, or insights need to be shared widely with Salesforce and non-Salesforce users alike. Analyst-heavy teams and customer-facing analytics are strong signs that Tableau is the better fit.

III. Think About Tableau Next When You Are Committed to Data 360

Tableau Next fits organizations already committed to Data 360 and to Salesforce’s agentic direction. Tableau Next is positioned as the next generation of Salesforce analytics, but its packaging is still evolving.

IV. The Composite Reality

For many organizations, the answer is both products. CRM Analytics runs inside Salesforce workflows, and Tableau handles deeper cross-system analysis. Running both is a governance decision, not a contest between products. Both have active roadmaps, and each meets real needs.

V. Compare Beyond Salesforce

If Microsoft Power BI or another vendor is on your shortlist, the full cross-vendor decision lives in ours Tableau vs Power BI comparison. Use this framework to settle the Salesforce side first, then take that shortlist into the comparison.

So, choose by use case, not by brand. If the use case lives inside Salesforce, CRM Analytics is the natural fit. If it lives outside Salesforce, Tableau is usually the better choice.

What Should Existing CRM Analytics Customers Do Now?

If your org runs CRM Analytics, perhaps with dashboards built back in the Einstein Analytics era, the consolidation talk raises a fair question about what to do now. Four moves cover it.

1. Stay Calm

As of October 2026, Salesforce has published no retirement notice and no migration timeline for CRM Analytics. The product shipped new features in Spring ‘26, and its consultant certification received a Spring ‘26 maintenance module. Nothing in the public record requires you to move, so existing dashboards and recipes can keep doing their jobs while you plan.

2. Get Commitments in Writing

Contract renewals give you negotiating power, so use it to turn verbal reassurance into contract language. Ask for four things in writing:

Salesforce’s feature-parity intentions between CRM Analytics and Tableau Next
A minimum notice period before any end-of-life date
Migration tooling or services if a move is ever required
Credit for remaining license value toward a successor product

Verbal assurances carry little weight once vendor priorities shift.

3. Watch the Signals

Salesforce ships three releases a year, so the signal is not how often release notes appear, but what each release adds to CRM Analytics compared with Tableau Next. Track feature-parity announcements between CRM Analytics and Tableau Next, along with any packaging changes that bundle or separate the products. Pricing adjustments and changes to certification tracks often show the direction before any official announcement.

4. Invest in Portable Capabilities

Semantic layers and governance frameworks outlive specific tools. Skills in data modeling, recipe design, and dataset architecture transfer conceptually to the next generation. Focus development on capabilities that keep their value however vendor consolidation plays out.

Drive Smarter Business Decisions with Salesforce Einstein Analytics

What Does “Einstein” Mean Across Salesforce?

Einstein arrived in 2016 as the umbrella name for the AI built into Salesforce. Over the following decade, Salesforce attached it to an analytics product, a predictive engine, several Sales Cloud features, a generative assistant, and the launch name of a rebuilt Tableau. Some of those have since been renamed, and some still carry the name, which is why a single search turns up all of them at once. The table sorts them out.

Name	What It Is	Where It Lives
Einstein	Salesforce’s overarching AI brand, used across the portfolio since 2016	Across the entire portfolio
Einstein Analytics	The former name of CRM Analytics (2017 to 2020)	Retired product name
Einstein Discovery	The predictive and AI forecasting engine	Inside CRM Analytics, with predictions in Flow and Tableau
Einstein Forecasting	AI revenue predictions for each forecast period	Sales Cloud
Einstein Opportunity Scoring	Built-in scores for how likely each deal is to close	Sales Cloud
Einstein GPT and Einstein Copilot	The generative AI layer; the Copilot assistant is now Agentforce	Agentforce, governed by the Einstein Trust Layer
Tableau Einstein / Tableau Next	The converged agentic analytics branding	Tableau and Data 360

Two pairs cause most of the confusion. Einstein Discovery and Einstein Forecasting both predict revenue outcomes, but Discovery is a model-building engine inside CRM Analytics, while Forecasting is a Sales Cloud feature on the Forecasts page. Einstein Discovery and Einstein Opportunity Scoring both score deals, but Opportunity Scoring works out of the box with no model design, while Discovery models are built around the outcomes and data you choose.

Governance questions about the generative layer, including the Einstein Trust Layer and hallucination risk, are covered in ours responsible AI in Salesforce guide. The practical rule for Salesforce Einstein and Tableau conversations is simple: when a vendor says Einstein, ask which Einstein.

How Does Achieva Help with CRM Analytics, Tableau, and Tableau Next?

Achieva is a Salesforce Summit Partner that implements CRM Analytics, Tableau, and Tableau Next inside Salesforce environments. Alongside them, it delivers the Microsoft BI stack through Damco’s data practice.

Our engagements typically take four shapes:

Orientation and fit assessments that apply the framework above to your org, data sources, and user base.
CRM Analytics builds covering datasets, recipes, embedded dashboards, and Einstein Discovery models, all with clear governance in place.
Tableau and Tableau Next implementations for analytics environments that span multiple sources or use agentic capabilities.
Commitment reviews for existing customers that turn the playbook above into renewal language and a list of signals to watch.

Whether you are outgrowing Reports, untangling a legacy Einstein Analytics deployment, or weighing Tableau Next, talk to Achieva about CRM Analytics, Tableau, or the path between them, from the Summit partner that implements all three.

Conclusion

Salesforce Einstein Analytics never disappeared; it evolved into CRM Analytics, and it now sits alongside Tableau and Tableau Next rather than being replaced by them. When selecting a platform, choose by using case, not brand name: CRM Analytics for native, in-workflow insight, and Tableau for cross-system BI. Additionally, confirm roadmaps in writing, and treat predictions as inputs to human judgment.

References:

Salesforce Einstein Analytics and Tableau: CRM Analytics, BI, and AI Forecasting Explained