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:
We don’t have dupe lead problems
All web forms and activities are captured and retained
Our marketing automation – emails are aligned and captured
Our entire end to end activation journey across 4 disparate clouds are in sync with clean data
Our Salesforce management and development costs are extremely low.
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!
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.
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.
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.
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 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.
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.
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.
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:
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.
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.
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
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.
Regularly Monitor and Validate Metrics: Continuously monitor metrics to ensure they remain accurate and relevant. Regular validation helps in identifying and addressing any discrepancies.
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!
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.
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.
We took these questions and built a Metric Analyst toolthat 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!
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.
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!
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.
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
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.
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.
Photo Credit- OpenAI.com
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.
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!
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.
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.
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.
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.
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.
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.
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 DataToolsPro.com 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!
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.
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.
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.
