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 PactBub on the topics of Salesforce, Snowflake, analytics and AI.

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Salesforce Data Cloud is a Game Changer

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

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.

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.

The Role of a Salesforce Metrics Dictionary in Promoting Team Cohesion

Posted on 2024-05-21 - 9:29 am by Ryan Goodman

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.

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.

Putting Salesforce Metadata To Work with New DataTools Pro

Posted on 2024-05-15 - 9:49 am by Ryan Goodman

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

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

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

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

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

A smarter Metrics Analyst AI – Contextual metrics recommendations

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

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

Learn more

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!

Learn More

Metrics merging

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

Expanding metrics ingest and management with Tableau Pulse

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

Learn More

Start using DataTools Pro for free!

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Creating a Metrics Mind Map with DataTools Pro

Posted on 2024-05-12 - 3:05 pm by Ryan Goodman

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.

Sign up for free

Learn more about DataTools Pro

March Madness: The Road to AI

Posted on 2024-03-17 - 11:06 am by Ryan Goodman

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!

1 AI 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.

2 Large Language Models: Large language models, like GPT, have transformed natural language understanding and generation, enabling more sophisticated and nuanced human-AI interactions.

3 Data Governance: Data governance ensures the proper management, quality, and security of data assets, serving as the backbone for trustworthy AI systems.

4 AI 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!

View GPT Powered Picks on ESPN

Webinar Alert: Introducing DataTools Pro Metric Analyst for Salesforce

Posted on 2024-03-04 - 9:48 pm by Ryan Goodman

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

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

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

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

What you will Learn?

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

Analytics, Metrics and AI. Oh My!

Posted on 2024-02-03 - 4:56 pm by Ryan Goodman

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

Behind the Curtain: Unveiling the Reality of Modern Bots

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

Does self-service analytics help?

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

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

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

What about Self Service via Natural Language Requests?

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

What about data?

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

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

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

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

Quick metrics maturity quiz:

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

Fact Finding Process:

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

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

From Metrics to Key Performance Indicators and OKRs

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

SMART Goals

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

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

OKR – Object Key Results

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

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

How are metrics and KPI is evolving with AI

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

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

Top Salesforce Data Cloud Resources for Learning

Posted on 2024-01-27 - 12:48 am by Ryan Goodman

We have pooled together Salesforce Data Cloud resources from top experts to help get you up to speed. Salesforce Data Cloud is designed to streamline the organization and unification of data across Salesforce’s extensive ecosystem and beyond. Integrating external data sources and salesforce data seamlessly, Data Cloud was designed to ingest, share, manage, and operationalize data, enabling a deeper connection with customers through personalized experiences and targeted engagement.

At the heart of Data Cloud’s capabilities is the creation of unified customer profiles. The holly grail of a single unified view of a customer to drive understanding and hyper personalized engagement. Data Cloud isn’t a simple Salesforce feature. It is a suite of capabilities that requires a cross breed of skills to succeed.