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 PactBub on the topics of Salesforce, Snowflake, analytics and AI.
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.
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Outdated metric definitions.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.
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
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!
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.
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 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.
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.
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.
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!
Visualize your metrics influence – Metrics map data 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!
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!
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.
March madness is our favorite time of year where the top college basketball programs face off on their road to the Final Four. March Madness earned it’s name from intense competition and exciting buzzer beater finishes!
In the spirt of March Madness, we have our own road to AI where we are looking at 4 important factors that will directly influence near term AI adoption and success. Our team reviewed a list of 15 topics and narrowed it down to our own final four for 2024!
1AI Ethics and Privacy: AI ethics and privacy tackle the moral principles and data protection measures critical to maintaining user trust and upholding human rights in the digital age.
2Large Language Models: Large language models, like GPT, have transformed natural language understanding and generation, enabling more sophisticated and nuanced human-AI interactions.
3Data Governance: Data governance ensures the proper management, quality, and security of data assets, serving as the backbone for trustworthy AI systems.
4AI Chatbots and Co-Pilots: AI chatbots and co-pilots are enhancing work productivity and knowledge experience through large language models.
In March, we going to deep dive into these topics and let them face off head-to-head. We are set for an exhilarating journey of discovery and debate while enjoying a few weeks of exciting basketball at the same time! Join our linked in newsletter for updates!
AI Powered Picks for the 2024 March Madness
We created a GPT March Madness bracket bot available in OpenAI GPT Store to help anyone wanting to make pics based purely on season stats. The beauty of march madness is that the stats don’t matter as teams face off. We intend our stats driven bracket to be busted by the end of the first weekend!
