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The Role of a Salesforce Metrics Dictionary in Promoting Team Cohesion

Salesforce Metrics meeting

To understand Salesforce metrics challenges, let’s evaluate a common situation. Your executive leadership asks Sales, Marketing and operations to present last quarter’s results. Everyone shows up with slides and reports pulled from Salesforce or a Business Intelligence platform like Tableau. Frustration grows, as presented numbers and statistics may not align or contradict each other. Instead of discussing strategy and tactical adjustments to improve performance, time is wasted asking for clarification on the validity of information. If this sounds like your experience you are not alone. Prioritized, correct, and consistent information does not happen overnight. In this article we will explore our approach to help create a better foundation, working with the people, process, and technology you already own.

Salesforce Metrics Meeting

Most enterprises have multiple sources and approaches to acquire data and transform it into information. We love Salesforce because of the relative speed and ease to build and make changes to process, with clear and easy reporting. There are over 150K organizations like yours that have standardized marketing, sales and/or revenue operations on the Salesforce platform. So why would a team with a system of record and  “source of truth” from Salesforce still struggle reporting and understanding and maintain continuity of information as change happens?

Avoiding people, process, and communication blame game

If you have been a part of reporting and analytics initiative that goes sideways, it’s sometimes based on these factors:

  • Flawed requirement gathering
  • Change management or lack thereof during implementation
  • Incomplete or incorrect definitions
  • Lack of consensus across lines of business for goals and metrics
  • Data completeness, availability, and quality

Building an inventory of metrics and KPIs can be an exhaustive process leading to gaps in requirements as a result of not having the right people or experience on hand. In other cases, data quality and availability becomes a friction point that leads to failure. Modern data and analytics technology will help you move faster, dig deeper, model and blend data but not solve un-resolved definition and alignment problems.

In many organizations, there isn’t a solution in place to maintain a unified record and historical log of goals, metrics and data relationships together. Documents, PowerPoints and Excel are typically the system of record for metrics and KPIs until they are coded into data and analytics tools.

If your previous data lake, analytics, and business intelligence initiatives fell short, the blame is all to often put on process, people, and communication often encapsulated sometimes as “poor requirement gathering”. Experienced and tenured data and analytics leaders understand this excuse wont fly in 2024, so our team learned into these challenges to see how we can help!

Our DataTools metrics glossary approach

1. How do we capture and encapsulate the previous work that has happened inside of Salesforce to understand existing metrics and KPIs are adopted and in-use?

2. From this understanding, what is the knowledge that we need to capture and resulting information assets that we need to produce and distribute? One of those key information assets is Salesforce Metrics Documentation

.3. Eliminate most if not all of the manual and redundant work that typically occurs between teams that can be easily extracted from Salesforce metadata?

4. Knowing that this is a live, organic, information asset how do we understand and surface changes that stakeholders should be aware of?

From those questions, we constructed our vision of a metrics glossary that not only captures the metrics but all of the relationships that stem from those metrics.

Lean more about DataTools Pro

Automated Salesforce Metrics Glossary

We took these questions and built a Metric Analyst tool that attempts to automate most of the process.

Live Salesforce Metrics Documentation

One of the important pieces of information that anyone in your enterprise wants to know is “what’s important”? A metric and KPI glossary can exist as a word document, spreadsheet, email, or application that organizes the business definitions. Salesforce metrics documentation should inventory the definitions semantics for metrics where data originates in Salesforce. This document should serve as a knowledge asset and guide to to help cross organization collaboration for business, data, analytics, and technology teams. When properly implemented it should ensure everyone speaks the same, specific language in business terms. A metrics glossary can also include technical / data details to help understand some lineage details.

What are Salesforce metrics?

Salesforce metrics are quantifiable measurements that track business processes, and activities that occur in Salesforce. Salesforce is much more than a customer relationship management platform. Some companies run their entire end to end operations on Salesforce. A metric can encompass anything from sales pipeline health to customer support resolution times. However, with a vast amount of data and numerous metrics available, ensuring consistent understanding and interpretation becomes crucial. Learn more: Analytics, Metrics and AI. Oh My!

Why do you need a Salesforce metrics dictionary?

Let’s revisit the scenario at the beginning of this article. If we take a simple measurement for “Lead conversion”, you can imagine the many variations and iterations of this metric. For example marketing could consider a marketing qualified lead, where sales considers “sales qualified” leads. Conversationally they can be interchanged, but at an organizational level, this misunderstanding could be simple semantics and labeling. A Salesforce metric dictionary acts as source of truth ensuring everyone speaks the same language when clarity and precision is mandatory.

  • Standardization: Defines clear and consistent definitions and calculations for all metrics.
  • Improved Communication: Eliminates confusion and fosters better collaboration across teams.
  • Enhanced Data Accuracy: Reduces errors by ensuring everyone uses the same metrics and formulas.
  • Streamlined Analysis: Makes data analysis faster and more efficient by providing a central reference point.

What Does a Salesforce Metric Dictionary Include?

An effective Salesforce metric dictionary should encompass the following key components:

Mandatory definitions that are managed and governed across lines of business

Metric Name: The name of the metric, clear and concise. There should be 1, official name that ties to a definition. If there are multiple names for the same metric, that is captured and tracked independent of the official name.

Definition: In simple terms what is the metric measuring. This definition may require some detail to how it is calculated but should be readable and understandable to business information consumers and owners.

Ownership: Who is the person ultimately responsible for the metric? The premise is that if there is no clear ownership and accountable person to sign off or accountable for the metric then it shouldn’t be managed.

Important context and ownership information to support usage of definitions

Description (optional): A detailed explanation of what the metric measures and its significance to your business goals. In a world with AI agents, my recommendation is the longer the description and the more context, the better!

Calculation (optional): The specific formula or steps used to calculate the metric. This ensures everyone understands how the value is derived. This work can be time consuming and requires salesforce admins to acquire these definitions.

Target Value/Benchmark: (optional): A target or benchmark to measure your metric against is common practice. Not all metrics will have a target, but a KPI absolutely should!

More reading on metrics, OKRS and KPIs: Analytics, Metrics and AI. Oh My!

Salesforce Metrics Dictionary Template

While Salesforce doesn’t provide a built-in metric dictionary, you can create using a spreadsheet tool like Microsoft Excel or Google Sheets, and now a live connected Metric Dictionary like DataTools Pro. The following table showcases a sample structure:

Additional Tips for Managing Salesforce Metrics

  • Maintain and Update: Schedule regular reviews to assess the dictionary’s accuracy and completeness. As Salesforce evolves and your business needs shift, update metric definitions, calculations, and target values to reflect these changes. This is an important component for information stewardship, governance, and safeguarding the integrity of your organization’s management information systems.
  • Access and Distribution: Don’t let your metric dictionary become a hidden and outdated document. Share it widely with all Salesforce users – sales reps, marketing teams, customer service agents, and anyone who interacts with your CRM data. This is a big part of fostering a culture of data literacy and ensures everyone interprets metrics consistently.


By implementing a Salesforce metric dictionary, you empower your organization to leverage the true potential across teams and lines of business using a language that should be universal (business performance and outcomes). Standardized metrics ensure clear communication, accurate analysis, and ultimately, data-driven decision-making that fuels business success. Here are some resources to help you take control of your Salesforce metrics today and unlock the key to a more informed and strategic CRM strategy.

Putting Salesforce Metadata To Work with New DataTools Pro

Salesforce metadata

We built DataTools Pro first and foremost for individual contributors who understand the impact of turning the treasure trove of Salesforce metadata into real time savings. The bi-product of DataTools Pro new Salesforce metadata analysis and generation tools are information assets that will help business and soon AI agents learn and understand the relationships between your data, analytics, Salesforce, and other business applications.

The same way “metadata” connects and explains relationships and meaning of data, we want to transform explanation into true understanding between data, Salesforce, and analytics teams.

The key to Salesforce Data Cloud success is mastery of meta data. In the most recent Salesforce earnings call, meta data was a hot topic.

“But the AI is not going to work because it needs to have the seamless, amalgamated data experience of data and metadata. And that’s why our data cloud is like a rocketship.”

Marc Benioff

We are excited to share some new DataTools Pro features that puts Salesforce metadata to work for you to accelerate onboarding for data workers and soon for new AI agents!

A smarter Metrics Analyst AI – Contextual metrics recommendations

We are rolling out the second release of our Data Analyst AI to round out our original vision to ensure recommendations get smarter as your library of metrics grows. We we have improved results and automate relating dashboards and reports to metrics as one automated step.

Lookup for updates and announcements where we will showcase how Metrics Analyst AI takes common change management challenges head on!

Learn more

Metrics Analyst AI for Salesforce

Visualize your metrics influence – Metrics map data visualization

Metrics Map visualization

There is no better way to conceptualize and understand complex relationships than to visualize them. We have built the first node of our metrics map vision, allowing you to see at a glance, how a single metric relates to data, analytics, and business topics!

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Metrics merging

As your metrics library grows like any data set, so does your need to manage that data over time. While Metric Analyst will help make good recommendations, DataTools Pro is getting enhanced merging functions to make it easier for enriching and preventing duplicating metrics. We are actively working with our first power users to continue to expand our merge functions to balance fine grained control with automated recommendations!

Expanding metrics ingest and management with Tableau Pulse

We love the new Tableau Pulse advancements, making it fast and easy to build powerful metrics based analytics. As you implement and grow your metrics, library it will quickly require the same metrics management and relationship management that we are performing for Salesforce. Document and manage all of your Salesforce and Tableau based metrics in one place!

Learn More

Import and Merge from Tableau Pulse

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Creating a Metrics Mind Map with DataTools Pro

Metrics Mind Map

One of the best tools for visualizing and conceptualizing relationships between any topic is a mind map. We with a mind map when we started DataTools Pro in late 2023. The mind map is easy to conceptualize visually as we connect the dots between people, process, metrics, and data. This is something that all enterprises struggle with while transitioning from service and product based businesses to information based businesses. Businesses are not static, so managing complex relationships that change regularly requires building and understanding these relationships at the speed business happens!

As we started turning our mind map concept into reality, we knew relationships between metrics, topics, data and analytics assets like reports and understanding changes that occur is hard enough.

That is where data visualization delivers immense value to bring data to life. The same way data professionals understand “Entity Relationships”, business professionals should have “Metrics Relationships” to understand how business initiatives, operations, and strategy connect.

That is why we created our Metrics Map visualization, powered by DataTools Pro to systemize this process. The first iteration makes each metric the center of the universe (in our visualization visualization). From a single metric we want to know what influences a metric or KPI and what the metric has influence over. With this starting point to discover, understand and relate metrics, we can work backwards to data and forwards to outcomes!

Metrics Map

Many analytics industry tech companies have focused on solving problems for accelerating data acquisition, transformation, and delivery. Generative AI, without contextual metrics glossaries jam packed with meta data will produce negligible results. It is the equivalent of hiring a data analyst and not explaining goals, metrics and analytics relates to the decisions out outcomes they influence.

We are excited to work with a number of like-minded partners in the realm of AI and data management to demonstrate profound improvements we are seeing when feeding our soon to be released metric maps API into generative AI analyst agents!

Create your first Metrics Mind Map from Salesforce and Tableau Pulse!

DataTools Pro is freely available for individuals and supports Salesforce and Tableau Pulse to build metrics glossaries and metrics maps.

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Learn more about DataTools Pro

Webinar Alert: Introducing DataTools Pro Metric Analyst for Salesforce

DataTools Webinar

We were thrilled to extend an invitation to the unveiling of DataTools Pro Metric Analyst for Salesforce – your key to transforming your Salesforce organization into a beacon of metrics and KPI excellence.

Webinar Date: March 13 2024
9:30 AM PST / 12:30 PM EST

Register to get access to the recording – Week of 3-18-2024

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In just 40 minutes, discover how to revolutionize the way you align and agree on KPIs, all with the speed and precision that only AI-aided automation can offer. This is more than a webinar; it’s a doorway to enhancing productivity and insights within your Salesforce org.

What you will Learn?

  • Plug and Play Salesforce Connected App: Seamless ways to incorporate DataTools Pro into your existing Salesforce org with 1 click.
  • AI-Aided KPI Alignment: How our batch, AI enhanced meta data analysts fast-tracks consensus on crucial KPIs, making your team more unified and focused.
  • Real-World Applications: Insightful demonstrations on leveraging DataTools Pro to elevate your organization’s data analysis and decision-making tools.
  • Interactive Q&A: Have your questions answered in real-time.

Analytics, Metrics and AI. Oh My!

Recent AI advancements with large language models have broken through and forever changed how we think about information access and retrieval. Metrics and AI is at the top of my mind as AI agents today provide universal translation and curation of information. Here, in our data tools niche where we wrangle and transforming data into information, we are seeing incredible results using AI to write code and deliver the same results that traditionally required an analyst. We don’t believe AI will replace analysts, but we know already that AI augmented retrieval for researching large bodies of information a job better suited for machines. Enterprises need to re-frame documentation as context data generation for people and AI. You will likely see a rise in “knowledge graphs” as a hot topic. Unstructured data has always been deemed “untapped” gold, so now the race down the yellow brick road is on!

Behind the Curtain: Unveiling the Reality of Modern Bots

Chatbots have propelled large language models into the forefront. The benefit of these AI chatbots to individuals is the way an LLM can breaking down knowledge and experience into personalized bite sized information chunks. We are not too far from this being a shared experience in a collaborative setting. This is where we can see early adopters super charge team productivity. The big opportunity is how how enterprises will use AI to curate and deliver information us using the vast collections of empirical knowledge created over time. It goes without saying there are lots of smart people working tirelessly on these problems. Here at DataTools Pro, we are obsessed with this problem as a small scrappy team.

Does self-service analytics help?

Analysts, data scientists, and data professionals have always been required to distill complex business concepts into quantifiable analytics. Business Intelligence (management information systems) and Analytics disciplines still have the same problems today as 15 years ago. AI, LLMs and data platforms will not solve these problems without radical changes how people work.

  1. Age old “multiple versions of truth” problems still exist. It has moved from spreadsheets to self service reports and dashboards
  2. Empirical knowledge gained from pulling data together becomes disparate in spreadsheets, documents and PowerPoints, and email.
  3. Methods to build a live, connected semantic layers to categorize and measure quantitative performance remain siloed and technology oriented.

Reports, dashboards, and data remain the primary delivery mechanism for performance metrics and KPIs. The need for speed to prepare and deliver self-service analytics has shortcut slow moving BI platforms of yesterday. Similarly, modern cloud data platforms have helped democratized working with structured and unstructured data that historically required database administrators, software engineers, expensive technology components. “Deluge” is the best word to describe the state of most enterprises in regard to the number of data and analytics assets flowing thus creating newer “data mesh” and “data fabric” methodologies to help strategize designing systems and process to tackle the deluge problem. We are experimenting with this ourselves with Azure Co-Pilot while our team, data, and metrics library is small.

What about Self Service via Natural Language Requests?

Natural language queries is a feature and not a solution. Many professionals simply do not know what data and metrics are available to start asking questions. This is where AI agents armed with a glossary, semantics and a large body of context data will be transformational. AI co-pilots are still very new, so we are experimenting ourselves what is real vs art of the possible. The keystone is aligning AI and business professionals with a common taxonomy and language and where we are working to build a common thread between business, data, analytics, and soon auto-pilots aiding these teams.

What about data?

Many enterprises do not have enough resources behind data governance and management. I still think this is a massive area of opportunity to somehow democratize and distribute data management in a way that is non-intrusive. Otherwise our point of arrival for AI automation will be autopilots and agents spitting out useless information. Incorrect information leads to mistrust and failed adoption of “co-pilots”.

Data storage is dirt cheap and modern data platforms make it fast and easy to analyze and model data into sophisticated analytics. How do you create universal focus?

All roads to AI metrics and analytics mastery leads back to goals and data governance

Every company has a set of metrics that indicate the health of the business. Your financial metrics within your income statement and balance sheet don’t get a lot of love on social media, but they are the bedrock to your performance (assuming you are a for profit business). Highly sophisticated metrics or amassing hundreds of metrics wont translate to good performance. Universal understanding, consistency, correctness and execution against a finite set of metrics will!

Quick metrics maturity quiz:

  1. Do you have an inventory of all of the metrics your operational team is using?
  2. Who are the owners, stakeholders, and oracles (keepers of institutional knowledge)?
  3. Are you certain those metrics are calculated and deployed consistently across teams and individuals?
  4. Are your business, technology, data and analytics teams aligned how to implement these metrics into analytics?

Fact Finding Process:

Traditionally, this is the process most consultants utilize to thoughtfully acquire and organize your metrics glossary. Many enterprises already have documents, presentations, or spreadsheets with this information formally gathered. Rarely are they universally understood and up to date.

Metrics requirement gathering

Getting consensus and universal understanding is slow and cumbersome. It is one of the challenges we wanted to tackle at DataTools Pro

From Metrics to Key Performance Indicators and OKRs

There are a number of different organizing principles and methodologies to translate your organizational goals into metrics. A KPI differs from a metric in that it has a specific target, timeline, and direct impact on your organizational goals and objectives. You may have dozens of metrics without targets, and that is okay. There are a number of widely adopted models to help you formally structure and organize your KPIs:


SMART created by George Doran that offers a system for organizing and defining and measuring your business goals.

  • Specific – target a specific area for improvement.
  • Measurable – quantify or at least suggest an indicator of progress.
  • Assignable – specify who will do it.
  • Realistic – state which results can realistically be achieved, given available resources.
  • Time-related – specify when the result(s) can be achieved.

OKR – Object Key Results

  • OKR – An objective is a clearly defined, inspirational goal aimed at driving motivation and direction. Key results are specific, measurable outcomes used to track the achievement of the objective.

As you get deeper into the performance management, process improvement, you will discover what works best for your corporate culture.

How are metrics and KPI is evolving with AI

No article is complete in 2024 without a hot take on AI. A lot of the focus in technology and analytics is centered on amassing feeding large volumes of quantitative data into machine learning models to predict outcomes. Now with generative AI, we are vectorizing large bodies of data to train, fine tune or simply retrieve data using natural language requests. Unfortunately, without the right semantics, definitions, governance, and context data, your AI investment won’t feel magical. Our team is racing ahead knowing the path to have meaningful dialogue and results with AI co-pilots requires context. We have taken a novel approach to run along side enterprises on their journey down the yellow brick road to help with our upcoming DataTools Pro metrics analyst!

Join us for a webinar March 13 where we will formally introduce our Metrics Analyst AI.

New ChatGPT Store is Proving Ground for DataTools Pro GPT

DataTools Pro GPT

OpenAI unveiled a new ChatGPT Store and teams subscription, further asserting their dominance in mass adoption of Generative AI. The new OpenAI GPT Store is rolling out after a huge surge of creativity from a community of creators. There are over 3 million custom GPTs. OpenAI is initially rolling out this new store to ChatGPT Plus, Team, and Enterprise users.

ChatGPT Store
Photo Credit-

Our First New ChatGPT Store Release: Marketing Metrics DataTools Pro

To participate in this exciting GPT Store launch, we released Marketing Metrics DataTools Pro GPT. This was a great opportunity to use our own curated metrics database. Participating in the excitement and initial launch of ChatGPT store is a great opportunity to safely test. Additionaly, we are using our own GPT for internal product marketing competency, design and rollout of DataTools Pro metrics glossary.

New ChatGPT Teams

The second exciting announcement from OpenAI is the release of ChatGPT Teams. For $30/month, this license provides an affordable solution for any business. We ae betting big on collaborative AI and AI agents. These ChatGPT releases are not the point of arrival for AI mass adoption. It is one point along a path to help you boost adoption, understanding, and competency with AI.

Keeping Pulse on AI Agents Advancements

At DataTools Pro, our vision and role is to help curate critical semantic data in the form of intelligent metrics glossary. When you adopt AI agents, creating awareness fluency in your business terminology is what will make or break your AI experience. If you ae trying to make sense of OpenAI ChatGPT, Azure GPT, or Salesforce GPT we are here to help you de-mystify and plan accordingly. Our team is working to simultaneously support them all!

DataTools Pro Beta3 is Here for the Holidays

DataTools for the Holidays

Just in time for the holidays, we hit our final beta release milestone before we officially launch DataTools Pro in early 2024! Our approach to product is release early and often so we can get feedback and incorporate it into our roadmap. DataTools Pro milestone to exit beta is our release into the Salesforce App exchange. We look forward to formally delivering a webinar in January to celebrate our launch. We have a jam packed roadmap to deliver in 2024. We can’t wait to help connect your Salesforce, data, and analytics teams and accelerate your data cloud initiatives.

Dec LinkedIn Newsletter: DataTools Pro Holiday Special

Salesforce Entity Relationship Views

A simple and effective tool to classify your Salesforce objects, our new views features allows you to organize objects and create custom entity prelateship diagrams aligned to business topics, organizations, and initiatives.

Data Tools Object and ERD Views

ARTICLE: Salesforce Entity Relationship Diagrams Makes Visual Storytelling Simple

Metrics Enhancements

We have continued to button up our metrics glossary tools to simplify the the process for managing metrics and KPI glossaries and their lineage to Salesforce dashboards and reports. We added support for custom links.

Improved Metrics Bulk Batch Import

Bulk Editing Metrics Records

Fine Grained User Permissions and Sharing

In preparation for team-based work in DataTools Pro, we added fine grained permissions. Next, we are working to refine the experience and standardize roles to make permissions and sharing simple.

Managing User Permissions and Sharing

DataTools Metrics API

An impotent aspect of DataTools Pro is not only automating most aspects of metrics glossary creation and management, but also securely distributing it across your enterprise. We have been quietly experimenting with our own DataTools API to build new integrations. Those will come in the form of add-ons, open source projects, and direct integrations in 2024.

ARTICLE: Using DataTools Pro to Create New Microsoft Copilot Studio Custom Actions

Support and User Onboarding Resources

We have added contextual help, more documentation, and new support engagement options to work with our team. This is just the start as we work on guided onboarding videos to help deliver best practices from our team and other users.

New Community and Social Resources

DataTools Pro Flash Newsletter
Join our monthly newsletter on LinkedIn

Join us on Reddit

New DataTools LinkedIn Page

Coming in January!

Our development is razor focused on self-service onboarding. Additionally, we have some very exciting, novel features for our metrics glossary that will go into private preview. Our first webinar to officially launch DataToolsPro and is also planned for January. We look forward working with our early adopter beta users to deliver overwhelming incremental value to your Salesforce data cloud initiatives for 2024!

Salesforce Entity Relationship Diagrams Makes Visual Storytelling Simple

Salesforce ERD

One of the most useful tools in the admin or data professional’s toolkit are Salesforce entity relationship diagrams. Understanding conceptual and physical data models is difficult enough. A business stakeholder responsible for sales, marketing, and revenue typically has little interest in the Salesforce data model. When information coming out of Salesforce is incorrect, sometimes you need to revisit your existing data model.

Bringing Salesforce admin, data and business professionals together, sometimes a conceptual entity relationship diagram is very useful to algin to the same level of understanding to make the right forward decision. To help explain and prioritize data work for a client, I recently used our entity relationship diagram to pinpoint and explain the root cause of reporting problems.

Salesforce entity relationship diagrams

Real World Lead Attribution Use Case with Salesforce ERD

Lead attribution is one of the most important and challenging aspects of running your “got to market” stack. To do so requires attention to data consistency and quality. One of our customers had an ambitious and practical approach to connects Leads, Accounts, and Opportunities with a junction object called “Vintage”. The ability to automatically track a lead vintage (when the lead enters the funnel), is very useful to report funnel conversion and lifetime value. Reports for revenue and lifetime value by lead source is important for planning and budgeting independent of campaign activity.

To communicate the issue, I used the following DataTools Pro ERD Diagram to demonstrate the additional data relationships that were maintained. Additionally, I explained how existing reporting requirements could easily be achieved without the vintage object. The following is the exact picture I painted to describe the specific linkage that was effectively broken in the Lead Attribution Funnel.

Salesforce Attribution Diagram

Resolution with Empirical Proof

There were some objections to remove the Vintage object. During the meeting, I clicked to demonstrate where those data relationships are maintained. It was very effective to satisfy most objections in real time.

There was one objection we had to clear to deprecate the Vintage object. Using historical data analysis I discovered the Vintage objection use case occurred 1 in every 500 opportunities which made it a true edge case. Sometimes you engineer a solution to account for anticipated scenarios that rarely occur in real life; this was one of those cases.

The consensus was the vintage object and all of the processes needed to maintain it could be deprecated. Rather than trying to accomplish detailed lead attribution from the lead object, campaign and campaign members are used to capture clients that enter the funnel multiple times from multiple channels.

How to Build a Salesforce entity relationship diagrams for Free

Salesforce provides an out of the entity diagram for Salesforce administrators to visualize and manage the Salesforce data model. I find them useful for administration but not for sharing and distribution.

Build better, easier to visualize ERDs with DataTools Pro: Our desire to build a better ERD for Salesforce led us to create ERDs. Here are some of reasons you may want to check out the free diagraming capabilities we offer:

  • Simpler, minimal design
  • Exportable to single page document (SVG)
  • Connected directly to Salesforce
  • Custom views aligned to business topics and tech modules.