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Webinar Alert: Introducing DataTools Pro Metric Analyst for Salesforce

DataTools Webinar

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

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

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

Name

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

What you will Learn?

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

Analytics, Metrics and AI. Oh My!

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

Behind the Curtain: Unveiling the Reality of Modern Bots

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

Does self-service analytics help?

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

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

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

What about Self Service via Natural Language Requests?

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

What about data?

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

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

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

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

Quick metrics maturity quiz:

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

Fact Finding Process:

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

Metrics requirement gathering

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

From Metrics to Key Performance Indicators and OKRs

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

SMART 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.

New ChatGPT Store is Proving Ground for DataTools Pro GPT

DataTools Pro GPT

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

ChatGPT Store
Photo Credit- 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.

New ChatGPT Teams

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

Keeping Pulse on AI Agents Advancements

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

DataTools Pro Beta3 is Here for the Holidays

DataTools for the Holidays

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

Dec LinkedIn Newsletter: DataTools Pro Holiday Special

Salesforce Entity Relationship Views

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

Data Tools Object and ERD Views

ARTICLE: Salesforce Entity Relationship Diagrams Makes Visual Storytelling Simple

Metrics Enhancements

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

Improved Metrics Bulk Batch Import

Bulk Editing Metrics Records

Fine Grained User Permissions and Sharing

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

Managing User Permissions and Sharing

DataTools Metrics API

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

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

Support and User Onboarding Resources

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

New Community and Social Resources

DataTools Pro Flash Newsletter
Join our monthly newsletter on LinkedIn

Join us on Reddit
https://www.reddit.com/r/datatoolspro/

New DataTools LinkedIn Page
https://www.linkedin.com/showcase/datatools-pro/

Coming in January!

Our development is razor focused on self-service onboarding. Additionally, we have some very exciting, novel features for our metrics glossary that will go into private preview. Our first webinar to officially launch DataToolsPro and 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!

Salesforce Entity Relationship Diagrams Makes Visual Storytelling Simple

Salesforce ERD

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

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

Salesforce entity relationship diagrams

Real World Lead Attribution Use Case with Salesforce ERD

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

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

Salesforce Attribution Diagram

Resolution with Empirical Proof

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

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

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

How to Build a Salesforce entity relationship diagrams for Free

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

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

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

New Microsoft Copilot Studio Custom Actions

Azure CoPilot Studio

New Azure Copilot Studio custom actions have opened the door for us to connect live, connected Salesforce metric and data dictionaries into the MS Copilot experience. Over the weekend I jumped into Azure and setup a functioning Azure Copilot, trained on our website data, that is available for you to try out below. A little bit of work and reading landed us in the same place we found ourselves a few weeks ago while testing OpenAI’s GPT actions for the first time. In a similar process, I embedded our DataTools Pro app as an action, in the same time it took to finish a cup of tea.

Unlike OpenAI, Azure OpenAI and now Azure Copilot are designed with enterprise in mind with the full suite of Azure services behind it.

DataTools as a GPT

Microsoft Copilot Bot Live Demo

This weekend, I dug in and with only a few clicks, built a co-pilot built a co-pilot based on the DataTools Pro website. With a little more work, we were security connecting in real time to DataTools Pro API and surfacing Salesforce metrics as context to Copilot our own business. We will continue to update this live demo below with our DataTools API demo account connected to Salesforce Essential Metrics.

Ask Questions about DataTools

Azure OpenAI and Copilot won’t fix your data

We are in unprecedented times with the speed that these AI advancements are rolling out and evolving. The real benefit of a Copilot is:

  1. Increasing speed and ease for consuming large bodies of information
  2. Improving the level and depth of understanding (for people who are inquisitive)
  3. Translating and communicating information (text and visual).

While the innovation and art of the possible is very exciting, a sobering reality is you still need to double down on the same data and metadata management and governance.

The path is clear with Azure AI services.

Microsoft has done an incredible job weaving AI into the existing suite of data services and tools..

As we officially roll out new and novel solutions with our DataTools App for Salesforce, we will continue to integrate our APIs throughout the Azure Open AI and Copilot stack. Schedule a call to learn how we can help bring fully trained co-pilots to your organization!

Check out Microsoft Marketing on Copilot Studio


Learn about our DataTools Pro API