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Author Archives: Ryan Goodman

 About Ryan Goodman

Ryan Goodman has been in the business of data and analytics for 20 years as a practitioner, executive, and technology entrepreneur. Ryan recently created DataTools Pro after 4 years working in small business lending as VP of Analytics and BI. There he implanted an analytics strategy and competency center for modern data stack, data sciences and governance. From his recent experiences as a customer and now running DataTools Pro full time, Ryan writes regularly for Salesforce Ben and Pact on the topics of Salesforce, Snowflake, analytics and AI.
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Boost Salesforce with Auto-Generated Entity Relationship Diagrams

Posted on 2024-12-10 - 3:21 am by
Auto-Generated Entity Relationship Diagrams

Wouldn’t it be great to see all your Salesforce data connections in one place? Auto-generated Entity Relationship Diagrams (ERDs) can make this happen. These diagrams show how different Salesforce objects relate to each other. This makes managing your data clearer and easier to handle.

For example, a lead is converted to an account, contact, and opportunity. An ERD will visually display how these objects relate to each other and what specific datapoints in your Salesforce database create these connections for technical and non-technical understanding.

Conceptual ERDs help businesses by simplifying data relationships. They use visuals to highlight connections, making it easier to understand complex data. This can save time and reduce mistakes. For example, if you need to know how customer information links to sales data, an ERD can show this right away. 

DataTools Pro offers a way to auto-generate conceptual ERDs for Salesforce. This tool can pick out the objects you need and draw the diagrams for you. You don’t have to do any drawing or guessing yourself. It organizes everything neatly, so your data makes sense in a glance. This can help your team work more efficiently and make better decisions based on clear data insights. When you click on an object it will focus and visually declutter any noise that would otherwise make an ERD feel overwhelming.

Benefits of Auto-Generated ERDs for Salesforce

Auto-generated ERDs offer many benefits for Salesforce users. They create a clear picture of how different Salesforce objects connect. This makes it much easier to manage and understand your data. You spend less time figuring out relationships and more time using your data effectively.

With an auto-generated ERD, you avoid manual errors. Since the diagram is created by software, it accurately reflects your data structure without mistakes with the latest information available. This helps reduce confusion and ensures everyone in your team is on the same page. It also saves time, as you don’t have to draw these complex diagrams by hand.

Another benefit is that these diagrams can easily update as your data changes. Salesforce data is always evolving, and having an up-to-date ERD keeps you aligned with current processes. This dynamic updating ensures your team has the latest information, aiding in quicker and more informed decision-making.

How to Use DataTools Pro for Creating Salesforce ERDs

Using DataTools Pro to create your Salesforce ERDs is simple. First, you need to select the relevant objects from your data dictionary. Make sure you choose the objects that are important for your business needs. This ensures that your ERD focuses on the right parts of your data.

Once you have selected the objects, DataTools Pro will auto-generate the diagram for you. You don’t have to do any drawing yourself. The tool organizes your Salesforce Entity Relationship Diagram by using intuitive color-coding, making it easier to see connections.

After generating the ERD, you can organize the visual based on different business topics, initiatives, or applications. This customization helps match the diagram to your specific business needs. Now, you have a clear visual of how different data points are related.

DataTools Pro also allows for easy updates. As your business evolves, you can quickly refresh your ERD to reflect any new objects or changes. This keeps your data management system up-to-date and highly effective, improving how you handle your Salesforce data.

Key Features of DataTools’ Interactive Salesforce ERD

DataTools’ Interactive Salesforce ERD comes packed with features to help you manage your data. One standout feature is the automated generation of diagrams. By choosing the relevant objects from your data dictionary, you can instantly create a visual map of your data. This makes it easy to understand complex relationships.

Color-coding is another great feature. Different colors for different objects and fields help you quickly identify and trace connections. This visual aid is useful for quick referencing and reduces the chances of misinterpreting data.

The tool also allows for customization. You can organize your ERD by business topic, initiative, or application. This flexibility helps you tailor the diagrams to meet specific business needs. Whether you are focusing on sales, customer service, or another area, your ERD will display the most relevant data.

Tips for Maximizing the Efficiency of Your Salesforce ERDs

Here are some tips to make the most of your Salesforce ERDs:

1. Regular Updates: Make sure to update your ERDs regularly. Salesforce data can change often, and having the latest diagram will help you stay on track.

2. Custom Organization: Organize your ERDs according to your business needs. Focus on specific topics, initiatives, or applications to make the data more useful.

3. Leverage Color-Coding: Use the color-coding feature to your advantage. Different colors can help highlight important connections and make the diagram easier to understand.

4. Team Collaboration: Share your ERDs with your team. Having everyone on the same page will improve decision-making and ensure consistent understanding of data relationships.

5. Training Sessions: Conduct training sessions to help your team understand and use ERDs effectively. Improved skills can lead to better data management and fewer errors.

Conclusion

Using auto-generated ERDs for Salesforce can greatly enhance your data management capabilities. They simplify complex relationships and provide clear, updated visuals that aid in better decision-making. DataTools Pro makes creating these diagrams easy and efficient. With its advanced features like automated generation, color-coding, and customization, your team can focus on what matters most—interpreting data and taking actionable steps.

Maximizing the benefits of Salesforce ERDs involves regular updates, leveraging color-coding, organizing data based on business needs, and encouraging team collaboration. These practices ensure that your data remains accurate and useful.

Ready to boost Salesforce with powerful data visualization tools? Visit DataTools Pro and explore how our Interactive Salesforce ERD can help you manage your data better. Start enhancing your data efficiency today!

Process hacking Gmail and Salesforce with Zapier And OpenAI

Posted on 2024-12-05 - 12:53 am by
GMail to Salesforce

My desire to have a simple, streamlined GMail and Salesforce flow goes back 12 years. One of my guilty pleasures as a tech and data geek is automating repetitive, mundane tasks. However, as a business operator with limited hours in a day, there are two conditions I evaluate to determine if a problem is worth automating:

Impact of addressing the problem: If I want to make an impact through automation, I need to see a 3x return on time per month. In other words, if I invest 10 hours tinkering and automating something, it should generate 30 hours of time savings per month per person. This is a high barrier that prevents me from solving the wrong problems.

Pain the problem causes: If can’t get my desired return on time, I rate how much pain the problem causes? By “pain” I refer to mental or even emotional pains that you experience at work… Stress, frustration, cognitive overload, and context switching. It accumulates and manifests as friction between people and process.

When GMail and Salesforce friction reached a level 5 pain…

On a busy day with 20 plus email threads, I found myself fumbling around GMail, Excel and Salesforce rather than communicating and advancing my business. I decided to vent on Linked-In wondering if anyone else in my immediate network has the same pain point.

Business pain scale

The root of the problem

When I get an email from someone who does not exist in my CRM, I have not found a simple mechanism to get someone from email into Salesforce as a contact or lead. After 30 minutes of research I found that this problem may be solved with an Einstein tool.

To Salesforce credit, once someone exists as a lead or contact in my CRM, the native extension from Salesforce is extremely useful.

My Zapier and OpenAI solution explained

How does the GMail extension, Zapier and ChatGPT work together?

  1. Process the email signature and determine name, email, phone, geography, company, title.
  2. Enrich the data by classifying the title to role or group (seniority)
  3. Check to see if the person exists in the CRM comparing the email to contact
    • If the email exists as a contact, is it the same person (first name / last name) or “something close”. I put this in quotes because historically this was a human judgement call, but in 2025 it is an LLM judgement call for me.
  4. Check to see if the person exists in the CRM as a lead using the same process
  5. Insert the new record if it does not exist
  6. Append only missing data points if they are missing

Creating my first Chrome Extension with ChatGPT

With no working knowledge of how to build a chrome extension, I opened chat GPT and provided the following prompt. An hour of tinkering with 25 iterations back and forth with ChatGPT and the solution was complete.

Building my Zap for GMail and Salesforce

I have been working with Zapier for at least 10 years now. I am drawing from lots of experience automating my data flows in Salesforce. I put that experience to work creating a zap that achieved most of my solution design.

Zapier connects GMail to Salesforce

Letting the LLM do the heavy lifting

Zapier has a great interface to use ChatGPT to process data to produce a consistently structured output. If you have built wrapper apps with ChatGPT like I have, you know this was challenging in the early days. Now, I have ChatGTP process the email and with specific instructions, process and produce the output.

The Results

The result of my tinkering is complete removal of pain communicating in Gmail. I use my simple chrome extension with Zapier and OpenAI every day to handle intelligent additions of contacts into Salesforce.

Day-to-day, this tool has been incredible. At this point I am saving 2-3 hours a month entering data as I have continued to scale my outreach efforts with DataTools Pro. I have not expanded on my MVP or shared it until recent demos raised interests.

GMail and Salesforce

For now, if you want to test the solution or need help setting it up or want to explore adapting this for other scenarios like creating service tickets or Outlook integration, feel free to contact me or better yet email me so I can show you how your email flowed into my CRM! ryan@datatoolspro.com

New Azure DataFactory template makes Salesforce to Snowflake Pipelines fast and cost-effective

Posted on 2024-12-04 - 1:29 am by
Azure DataFactory for Snowflake and Salesforce

We built our free Azure DataFactory template to help you build your data pipelines from Salesforce to Snowflake in 5-30 minutes. The value of data is not realized by collecting and moving it. The value of data is realized when you transform it into information. Analytics insights and attributes for automation is the objective and reason why you invest in a data warehouse like Snowflake. That is why we have built our free data pipeline templates to reduce the level of effort to get your data ready for analysis up to 90%! View our documentation to lean how

In 2023, we launched the first version of our template tagging it as a “5 min data lake with Azure DataFactory”. Adoption and feedback led us to close 2024 with an upgraded version of our template alongside our new DataTools doctor service and our revised Snowflake rapid adoption service to help our customers extract value from data faster.

Download our Azure DataFactory Template Now

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Why we build Salesforce to Snowflake pipelines with Azure DataFactory?

Fast, cheap and easy rarely happens in technology, but the Azure team, without a massive marketing blitz or fanfare created a very solid product for moving data between enterprise data sources in to Microsoft data platform and Snowflake.

We use Azure DataFactory pipelines to move massive volumes of data daily for customers who have invested in Azure. They save thousands of dollars per month while getting enterprise grade data extraction and migration.

When do we turn to other solutions than DataFactory?

We stick to technology tools that are flexible, practical, and well adopted. DataFactory in particular we like deploying with customers who are running MS SQL or Snowflake in Azure. When it comes to data transformation and ETL patterns, DataFactory does offer a no/low code Spark based Flow builder. However, depending on customer needs, scale, and team makeup, we do recommend alternative solutions that is catered to the existing processes, investments, team makeup and roadmap. We are always on the lookout for new, streamlined data pipeline solutions.

What’s Next for our Azure DataFactory template?

We are actively working on client projects for MySQL and MS SQL version of our template. Contact us for more details

Azure Data Factory Tutorials

Salesforce data pipelines to Snowflake free template

 Video Tutorial

1. Create Data Connections

 Connect Azure Data Factory to Salesforce.com (New 2024)

 Connect Azure Data Factory to Snowflake

2. Create Data Sources

 Create a Snowflake Data Source in Azure Data Factory

 Connect to Salesforce Lead and Account Object with Azure ADF

3. Move Data Between Applications

  Create a Data Pipeline to Connect Salesforce to Snowflake

4. Publishing and Administration

Publish your ADF Pipeline, Data Sets, and Triggers

Create an ADF Scheduled Trigger

What’s New in our Salesforce to Snowflake Pipelines

This week, we rolled out a long overdue update for our free Azure DataFactory template that makes extracting Salesforce data into Since 2023, there have been a lot of changes to Azure DataFactory, so we have rolled out a long overdue update and upgrade.

Salesforce to Snowflake ADF Data Lake

From our version 1.0.1 release notes:

  • New meta data staging process and table called “SFDC_METADATA_STAGE_TEMP” that feeds SFDC_METADATA_STAGE
  • Support for new field detection and addition (enable append fields)
  • New parameter AppendFields will insert new fields when detected
  • New parameter SnowErrorHandling allows for configurable error handling to skip error rows, skip file, or throw an error.
  • New MetaData field called “Status” that allows for “Disable” attribute that will ignore fields from being synchronized.
  • Update to Salesforce metadata request that supports compound fields by default like FirstName, LastName, Street, City, etc.
  • Pre-install check – End to end flow checks for existence of the metadata object
  • Added status variables to determine results for each pipeline for easier debugging
  • Schema insert and updates managed via merge by object.field ID
  • Changed field from ID to DurableId Salesforce field to Snowflake SFDC_METADATA_STAGE “ID”

Other Azure Related Articles

Read More on our Blog

New DataTools Pro fights Salesforce zombie reports

Posted on 2024-10-25 - 4:30 pm by
Salesforce zombie reports

Just in time for Halloween, we have new tricks and treats for DataTools Pro. Jam packed with integrations and a brand-new Reporting and Dashboard management tool, you can take control over reports, and clean up Salesforce zombie reports that are clogging up your Salesforce org.

Salesforce zombie reports

Solving for Salesforce Report Deluge

Our latest DataTools Pro release includes a brand new Salesforce Report and Dashboard management tool. We recognize as data and analytics professionals that Salesforce has an incredible self-service reporting and dashboard function. However, managing and maintaining reports over time is a task that challenges the most experienced admins and analysts. Our team approached this problem with the goal of getting hundreds or even thousands of Salesforce reports under control.

Common Problems with Salesforce Zombie Reports

  • Aggregating data from different objects for same metric.
  • Multiple versions of truth.
  • Redundant copies of reports.
  • Outdated metric definitions.
  • Lack of tagging and business context.
  • No visibility on what reports and dashboards are utilized.
  • Over-used filters removing data from analysis.
  • Abandoned reports still accessed and used.
  • Lack of controls resulting in changes to reports used for key metrics.

Governing Salesforce Reports and Dashboards with DataTools Pro

To combat zombie reports, and make it easier to manage day to day Salesforce report and dashboard lifecycle, we have created a new tool that provides:

1. Relating reports and dashboards to business context. Enhanced tagging and search reports by line of business, topic, and status.

2. Manage Report and Dashboard lifecycle to declutter Salesforce. Bulk disposition reports with a status so you can search and filter your report repository.

3. Provide a lens for admins into Report and dashboard utilization. Search and tag reports dashboards based on last viewed – making deprecating reports easy.

4. Help focus from activities to metrics and goals. Connect relationships between reports, metrics and KPIs using DataTools Metrics Glossary.

New DataTools Pro 3rd Party Integrations

In addition to our new Report and Dashboard management, we have been beefing up our native integrations, making it easier to integrate existing metrics glossaries or push your metrics glossary where you and your colleagues already work.

Zapier

The DataTools Pro Zapier integration provides the ability to stream your metrics glossary. Zapier is the ultimate conduit to connect any cloud application containing metrics into DataTools Pro. Soon, we will offer bi-directional connectivity through Zapier so you can utilize DataTools to help centralize and manage metrics with with hundreds of potential integrations to link and distribute metrics across your enterprise. Our new Zap is currently available in beta.

Zapier DataTools Pro

Coda

The DataTools Pro Coda bundle ensures your Salesforce, data, analytics, and business teams have immediate access to your metrics glossary. Coda is the perfect knowledge and AI brain to deliver metrics and relationships.

Need Help Eliminating Salesforce Zombie Reports?

We will continue to add more DataTools Pro integrations into 2025 including other CRMs like Hubspot. We look forward to getting early feedback and hope to collaborate with you to make zombie reports obsolete! If you need help mapping out your metrics and analytics governance plan, we are always here to help! Schedule a free consultation with us anytime

Why we love automating knowledge retention with Zapier and DataTools Pro

Posted on 2024-09-26 - 2:06 pm by

At DataTools Pro, we’re always on the lookout for ways to streamline processes and retain knowledge to feed into our AI brain! We are obsessed with hacking cross team knowledge which is why we have chosen to innovate new ways to manage metrics with Zapier and DataTools Pro. Zapier has become an essential tool for automating our own internal workflows and ensuring that our team is always in sync with as little human intervention as possible.

Zapier: Automating Knowledge Flow Across Apps

Zapier is a powerful workflow automation platform that connects over 4,000 apps, allowing us to create seamless data flows without custom coding. For us, this means we can push critical metrics from various sources into DataTools Pro with just a few clicks. Whether it’s Salesforce, Google Sheets, or Tableau, Zapier helps ensure that all of our metrics definitions and changes are automatically centralized in one place: our Metrics Glossary in DataTools Pro.

This process not only saves time but also ensures that our knowledge retention efforts are smooth and consistent across all platforms.

How We Use Zapier internally at DataTools Pro

Lead Intake and Activation Funnel

Internally, we’ve integrated Zapier to manage our intake, activation and onboarding of DataTools Pro users across our website, app, and Salesforce. With Zapier we are running an ultra simple Salesforce org where our business process flow for lead intake exists in Zapier, not Salesforce.

As a result of our approach:

  1. We don’t have dupe lead problems
  2. All web forms and activities are captured and retained
  3. Our marketing automation – emails are aligned and captured
  4. Our entire end to end activation journey across 4 disparate clouds are in sync with clean data
  5. Our Salesforce management and development costs are extremely low.
  6. Returning users, customers, and prospects are routed and logged as activities

Risk we acknowledge

Zapier is a single point of failure to connect prospects and clients to activation. However, Zapier has sophisticated logging, debugging, alerting and replay capabilities, that you need to properly manage your onboarding funnel. There is no concept of “build and pray” that our critical pipelines don’t fail at DataTools Pro.

Metrics Management

We have just started scratching the surface of our brand new Zap for DataTools Pro, allowing our users to connect any app into Zapier. The first iteration of this integration allows Zapier to push metrics directly into our centralized Metrics Glossary. The flexibility of Zapier’s workflows will ultimately allow us to synchronize new metrics across knowledge management platforms. DataTools Pro will handle monitoring, change management and integration across business and analytics teams. Zapier handles distributing that knowledge to the productivity tools that you are already using!

A Simple, Powerful Approach to Knowledge Retention

By connecting our Metrics Glossary to Zapier, we’ve removed a significant pain point: the manual labor of gathering and syncing information across platforms. This automation gives us more time to focus on what matters—delivering value to our customers. With Zapier handling movement of data, our team can stay razor focused on driving education, utility and value to our DataTools Pro users. The next horizon for us is fully automating our metrics, roadmap, prioritization, and knowledge distribution as we ship DataTools Pro features!

New Salesforce ERD DataTools Released

Posted on 2024-08-10 - 1:20 pm by
Salesforce ERD

Our DataTools Pro team created a free Salesforce entity relationship diagram tool that generates clean, and clear Salesforce ERD visualizations. Our product philosophy is “release early and often” giving early adopters an opportunity to kick the tires and help shape our newest Salesforce ERD release! In this post, we are excited to share new features to that make designing and referencing ERDs significantly better.

Get Started

Decluttering your Salesforce ERD

If you have lots of objects and field relationships, it can get overwhelming to understand relationships in context of a larger ERD. Our focus mode allows you to select and focus on objects and their relationships, letting everything fade to the back.

Connection Grouping – When multiple relationships exist between 2 objects, we have enhanced our grouping mode to group and remove redundant lines in your ERD.

Salesforce ERD

Object Layout Locking

Add objects from your Salesforce org and arrange them in the ERD without leaving the page. As you drag and arrange your ERD, it now saves position and layout so you can ensure your views are locked in place.

Entity Relationship Diagram

Embedded Field Dictionary

Manage and select your Salesforce objects which can be refreshed anytime. In our latest ERD, you can add and remove objects from your dictionary without leave the page.

Create ERD Views: With a dictionary of objects from Salesforce, you can create multiple views to highlight data object relationships. This focus allows for a focused and uncluttered perspective how your data model and relationships align to your desired outcomes.

Embedded Salesforce ERD

Embedded Field Dictionary

We have merged our data dictionary into the the ERD, eliminating context switching between screens. Our dictionary allows basic access to field name and a tooltip to quickly reference other attributes that may be reference for relevance. Additionally, you can filter and export your dictionary without leaving the page.

Salesforce Data Dictionary

Your input can shape what’s next for DataTools Pro Salesforce ERD

With a solid foundation, we have a massive list of enhancements expand the utility of a connected Salesforce ERD. We are working with a wide range of experts and backgrounds from AI application developers to Salesforce admins who manage massive, multi-org enterprises.

What is Metrics Governance and why you need it

Posted on 2024-08-09 - 12:15 pm by
Metrics Governance

Ensuring accuracy, consistency, and reliability in business metrics

Metrics governance refers to the systematic approach to managing and maintaining the accuracy, consistency, and reliability of metrics used within an organization. It is crucial for achieving data-influenced decisions by ensuring that the metrics used in reports and dashboards accurately. Without metrics governance, organizations often encounter inconsistent reports, leading to confusion and mistrust in the data. This article explores how the “single source of truth” problem is best addressed by governance process.

Metrics Governance Throne

Why Metrics Governance is difficult?

Metrics governance is difficult mostly because it is a cross organizational problem relying expertise, understanding, and distribution of knowledge regularly across teams. Getting data governance right is tough enough! Modeling data and applying business rules to understand results and outcomes adds another layer of complexity. Typically this complexity is inherited by professionals responsible for creating business intelligence and operational reports. Your metrics and KPIs that drive your organization are extremely important. The reality for many growing enterprises is metrics definitions live scattered across teams, documents, and technology applications. Every business has to make the right decision where to implement a glossary of metrics but there is no shortage of great technology solutions to put those definitions into motion:

  • BI and Analytics tools like Tableau Pulse let analysts build a library of metrics
  • Data development platforms like DBT provide a semantic layer to code and manage definitions, including metrics
  • Google Analytics has built-in metrics and standardized definitions into the core application

These 3 examples are typically managed by different teams highlighting where gaps can occur thus providing the inspiration for the graphic for this post. We believe in a federated approach to analytics is effective but a centralized repository of metrics definitions is needed not only to improve analytics, but to improve employee onboarding and AI co-pilot training.

Metrics Governance vs. Data Governance

While metrics governance and data governance are closely related, they have distinct focuses:

  • Data Governance: This involves the overall management of data availability, usability, integrity, and security within an organization. It encompasses data quality, ownership, stewardship, and compliance with data privacy regulations.
  • Metrics Governance: Specifically focuses on the metrics that are definitions intended to measure business outcomes using data. It deals with the definition, standardization, monitoring, and validation of metrics to ensure they are accurate and consistent.

Metrics governance complements data governance by ensuring that the metrics used to make business decisions are based on high-quality data and are consistently applied across the organization. The key difference lies in the scope—data governance is broader, covering all aspects of data management, while metrics governance zeroes in on the metrics themselves.

Steps to Implement Effective Metrics Governance

To implement effective metrics governance, organizations you should consider these typical areas of improvement:

Metrics Governance Flow
  1. Promote a Culture of Accountability and Data-Driven Decision-Making: All metrics should have business owners. Accountability and ownership of metrics and how to use them helps every team involved. This fosters a culture of accountability and ensures that decisions are based on reliable data.
  2. Establish Clear Definitions and Standards: Define metrics clearly and ensure that these definitions are understood across the organization. This prevents confusion and ensures consistency in reporting.
  3. Create a Centralized Metrics Glossary: Maintain a centralized repository of metrics to ensure consistency and easy access. This helps in tracking and managing metrics effectively. Here is a free template on: Notion Metrics Glossary Template
  4. Implement Data Quality Management Practices to your metrics: Ensure that the data used to calculate metrics is of high quality. This includes data validation, cleansing, and regular audits.
  5. Regularly Monitor and Validate Metrics: Continuously monitor metrics to ensure they remain accurate and relevant. Regular validation helps in identifying and addressing any discrepancies.
  6. Metrics Governance management as part of your data strategy – Understand where and how metrics are managed and deployed. Learn more about analytics strategy playbook

We would love to hear how you manage and standardize your metrics and KPIs. Our team at DataTools Pro is working on solutions to help automate the traditional metrics fact gathering and metrics glossary preparation steps!

Salesforce Data Cloud is a Game Changer

Posted on 2024-06-22 - 4:01 pm by

Salesforce Data Cloud is here to help enterprises move from being transaction centric to information based. Recording sales, processing orders, and tracking customer interactions through various processes is what helped Salesforce grow to one of the world’s largest enterprise software companies. Typically, Salesforce is one node in a complex network of loosely integrated applications and services. Modern data warehousing and data lakes have served as a hub to bring disparate data sources together for analysis. Data Cloud aims to solve integrating disparate data for analysis and action.

Salesforce Data Cloud 

Activating data into assets into decisions and actions across systems is hard work. Salesforce Data Cloud offers a comprehensive suite of tools to create relationships not only be tween disparate systems, but also data lakes and data warehouses. Salesforce is leaning on modern standards and learning into Snowflake, DataBricks, and Google Cloud. Acknowledging and integrating existing data cloud investments is a big part of Salesforce’s vision to “bring your own data lake.”

So far most of the messaging and examples for activating Data Cloud are facilitating better data-driven customer experiences . It sounds great in writing, but the journey for enterprises will require true cross discipline and organization collaboration.

CRM Transactional System of Record

CRM systems have traditionally served as the transactional system of record, capturing customer interactions, sales transactions, and service requests. While valuable, these transactional records often provide a limited view of customer relationships and preferences. Salesforce Data Cloud expands on this foundation by integrating additional data sources and enriching customer profiles with contextual insights, behavioral data and centralizing many customer signals.

Salesforce CRM

How Salesforce Data Cloud Organizes Data Graphs

Salesforce CRM organizes a relational model that connects accounts, contacts, and activities. Data Cloud creates a data graph that establishes connections based on various objects, including purchase history, communication channels, social interactions, and demographic data. Learn more about Salesforce Data Graph Structures

This graph structure is powerful when implemented, but integrating disparate data into Data Cloud requires the same expertise, thoughtful design, and deep understanding of data / meta-data management. 

Salesforce Data Cloud Connects to Disparate Business Applications and Data Clouds

One of the strengths of Salesforce Data Cloud lies in its ability to connect to disparate systems meta-data all within a common and familiar Salesforce cloud. Putting structured and semi structured data from business applications, cloud tools, and other data lakes are possible.  Whether it’s CRM platforms, marketing automation tools, social media channels, e-commerce platforms, Data Cloud integrates data from diverse sources into a unified data graph. This integration eliminates data silos and timely data extraction and management processes to gain visibility needed to understand data relationships. 

Shift from Single Centralized System / Source of Truth 

Coupled with innovative “zero-copy” data integration via “bring your own data lake”, Data Cloud is primed to integrate many clouds like Snowflake, Databricks, and Google Cloud platform without the expensive and slow bottlenecks that can occur with big data and dealing with terabytes of information. The ultimate 360-degree view of a customer has been the battle cry for every business software product for years. Only a small fraction of enterprises that embarked on the journey have publicly shared their success and have also produced sustained growth numbers to match the success story.

Key Features of Salesforce Data Cloud 

Data Cloud Components

Data Governance

Data governance is another critical aspect of Salesforce Data Cloud. With robust security measures, data encryption, and compliance tools, organizations can ensure data integrity, privacy, and regulatory adherence. Additionally, advanced analytics and AI for deploying models, predictive analytics, and AI-driven insights are pivotal to putting data to action. 

Integration with Salesforce Ecosystem 

A key strength of Salesforce Data Cloud lies in its seamless integration with the broader Salesforce ecosystem. This integration extends to Salesforce CRM, Marketing Cloud, Service Cloud, and other Salesforce products, creating a unified data environment. Furthermore, integration with third-party applications and APIs enhances functionality and flexibility, allowing organizations to customize data workflows and processes according to their specific needs.

This unified ecosystem streamlines data management, enhances collaboration across teams, and accelerates time-to-insight. Whether it’s enriching CRM data with external sources, automating micro-segmentation marketing campaigns based on event and time sensitive insights, or leveraging AI-driven analytics for sales forecasting, the integration capabilities of Salesforce Data Cloud empower organizations to extract maximum value from their data assets.

Data Governance and Compliance

In heightened data privacy concerns and regulatory scrutiny, Salesforce Data Cloud prioritizes data governance and compliance. Robust security measures, including data encryption, access controls, and audit trails, ensure that sensitive information remains protected at all times. Compliance tools help organizations adhere to industry regulations such as GDPR, CCPA, HIPAA, and more, mitigating compliance risks and enhancing trust with customers.

Additionally, the growing emphasis on ethical data practices, transparency, and data privacy will shape the evolution of data cloud platforms. Salesforce’s commitment to data ethics, trust, and security positions Salesforce Data Cloud as a trusted partner in navigating the complexities of modern data management.

Challenges and Considerations 

While Salesforce Data Cloud offers immense value in concept, organizations may encounter certain challenges during implementation. These challenges may include data migration complexities, meta-data mapping, integration with legacy systems, and user training requirements. Addressing these challenges requires careful planning, stakeholder buy-in, and a phased approach to deployment. 

Getting Started with Salesforce Data Cloud

The future of data management revolves around agility, intelligence, and scalability. Salesforce Data Cloud is well-positioned to address emerging trends such as AI-augmented everything, machine learning, and real-time data processing. The convergence of data integration, analytics, and AI capabilities under one integrated platform is certainly attractive for enterprises vested in Salesforce.

Curated Salesforce Data Cloud Resources

Salesforce Data Cloud represents could represent a complete paradigm shift in how you think about Salesforce as a data integration platform. Conversely it could simply provide an opportunity to displace existing, expensive tooling that have been streamlined with Salesforce Data Cloud integrations and partnerships. Building and refining your strategy to transition from transaction-centric to information-driven solution should point to measurable results first. Your path to success starts with a clear data, analytics, and AI strategy.

Our team at DataTools Pro is thrilled to see how early adopters embrace Salesforce Cloud not only to displace previous technology investments, but also take a leap forward in customer service and engagement.

Other Data Cloud Posts

Read More on our Blog

The Role of a Salesforce Metrics Dictionary in Promoting Team Cohesion

Posted on 2024-05-21 - 9:29 am by
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.

Conclusion

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.