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Who Wins the Efficiency Game: Data Management vs AI Chatbots

What truly propels an organization to the forefront of technological innovation? Is it the meticulous governance and curation of data, or is it the deployment of sophisticated AI chatbots and Large Language Models (LLMs) capable of digesting, synthesizing, and translating this data into actionable insights?

This pivotal question marks the forecourt where two giants from our March Madness tournament face-off: Data Management and Artificial Intelligence.

This blog is going to take us on an interesting adventure. We’re going to look closely at two big players in the world of technology:  data management and  AI-driven chatbot technology.

We’ll explore what makes each one special and compare them based on their efficiency outcomes within enterprises. We will also discuss how organizations can leverage both to achieve maximal operational efficiency.

So, the court set, and the stakes are high.

Will the precision and order of top-notch data management take the crown, or will the speed and adaptability of AI chatbots and LLMs win the day? 

Welcome to the crucible of efficiency, where the March Madness of technology unfolds. πŸ†šπŸ€

Data Management

In the ever-evolving digital landscape, “data management” has transcended mere buzzword statusβ€”it now stands as a foundational pillar for modern businesses. But what exactly does it entail?

According to Wikipedia, data management encompasses any discipline related to handling data as a valuable resource. It involves managing an organization’s data to facilitate informed decision-making.

The umbrella of data management covers a wide array of practices, including Data Governance, Data Observability, Data Integration, and Data Sharing. Its expansive scope underscores its pivotal role in today’s enterprises, where data-driven insights steer actionable strategies.

The economic impact of data management is equally staggering. Grand View Research reports that enterprise data management raked in a whopping $85.55 billion in 2022 and is projected to soar to $170.46 billion by 2029.

AI Chatbots & Co-pilots

Empowered by large language models, we are going to see AI enabled chatbots change the landscape for customer service and engagement, ushering in an era of seamless chat-based interactions. With a projected market value soaring to $1.3 billion by 2025, AI chatbots stand at the forefront of redefining customer experiences

The allure of AI chatbots lies in their speed, availability, and personalized approach to customer engagement. Capable of handling a vast volume of interactions, they swiftly provide tailored assistance, enhancing operational efficiency and user satisfaction.

Ladies and gentlemen, as the curtain rises, let the showdown between data management and AI chatbots commence!

The Efficiency Showdown: Data Management vs. Chatbot Assistants

As we gaze into the efficiency spectrum of technology in 2024, two prominent players are under the spotlight for their potential to streamline operations and enhance customer engagement: Data Management and Chatbot Assistants.

Let’s use the following as our yardstick for efficiency measurements.

1. Time-Saving Capabilities

  • Chatbot Assistants: They take the lead with their ability to provide instant responses, a critical factor as surveys indicate customer frustration with long wait times. Chatbots efficiently reduce wait times, offering swift service that keeps pace with the digital era’s demands.
  • Data Management: While pivotal for informed decision-making, it doesn’t directly influence customer-facing response times, focusing instead on backend data organization and analysis.

2. Cost-Effectiveness

  • Chatbot Assistants: Shine brightly here, with significant cost savings estimated at around $11 billion in 2022, a number only expected to grow. By automating customer service, chatbots can slash costs by up to 30%, showcasing their financial efficiency.

source: Digital Marketing Community

  • Data Management: Its contributions to cost-effectiveness come indirectly, through the optimization of business operations and strategic planning based on data insights.

3. Scalability

  • Chatbot Assistants: Excel in handling unlimited customer interactions simultaneously, making them incredibly scalable and capable of managing vast amounts of feedback and inquiries without the need for proportional increases in human resources.
  • Data Management: Scalability is more about managing growing data volumes and ensuring the system can expand to meet analytical demands, which is crucial but operates behind the scenes.

4. Customer Satisfaction and Experience

  • Chatbot Assistants: Offer 24/7 availability and quick responses, but they may struggle with complex queries that require a human touch, affecting customer satisfaction in nuanced interactions.
  • Data Management: Doesn’t directly interact with customers but plays a crucial role in understanding customer behavior and preferences through data analysis, indirectly influencing customer experience by informing business strategies.

Both Data Management and Chatbot Assistants hold substantial potential for improving efficiency, each in their individual domains. Chatbot Assistants shine in terms of immediate customer interaction, scalability, and cost-effectiveness, while Data Management is pivotal in structuring, securing, and leveraging data for informed decision-making. 

As the technological landscape continues to evolve, the integration of these two can lead to even greater efficiency gains, with chatbots benefiting from the rich insights derived from sophisticated Data Management systems.

The verdict in this showdown suggests that while chatbots may lead to direct customer interaction efficiency, the synergy of combining Data Management and Chatbot Assistants could offer the best of both worlds.

The Synergy Effect: Integrating Data Management and AI Co-Pilots

AI co-pilots are getting really good at chatting with customers. They don’t just follow scripts; they understand what your customers are saying, figure out what they need, and even learn from each conversation. 

This means whether someone’s shopping at 2 PM or 2 AM, they get quick and smart help, no waiting needed. Tools like Zendesk and LivePerson show us how it’s done by mixing AI smarts with a human touch for tricky questions, making sure every customer walks away happy.

Then there’s the data magic. When you mix AI co-pilots with your business data, you get something special. These co-pilots can look at a customer’s history, know what they like, and make suggestions that hit the mark, turning a simple chat into a personalized shopping spree. It’s like having a salesperson who knows your customers as well as their best friends do.

So, what’s the big deal about mixing Data Management with AI co-pilots? It means businesses can offer help anytime, understand customers better, and make shopping online as friendly and personal as walking into your favorite local store. It’s not just about answering questions faster; it’s about making every chat feel like it’s between good friends.

When this intelligence is powered by robust Data Management, the synergy amplifies. A case in point is Amtrak’s “Julie,” which leveraged this synergy to handle 5 million inquiries annually, boost bookings by 25%, and slash customer service costs, demonstrating the practical benefits of integrating AI co-pilots with data insights.

Strategic Implementation and Measuring Success

To make sure Data Management and AI co-pilots hit the mark in your business, it’s all about mixing the smarts of AI with the solid ground of Data Management. You’ve got to find the right people who know their way around AI and machine learning. With the demand for these skills skyrocketing, it’s clear they’re key players in getting things set up just right.

When it comes to seeing if all this tech is doing its job, keep an eye on the numbers that matter like how happy your customers are, how fast they’re getting help, and how many are chatting away with your AI co-pilots. With the right tools, you can track these signs of success, tweak things as needed, and make sure your AI buddies are pulling their weight.

Conclusion

Throughout this discussion, it’s clear that both Data Management and AI co-pilots are pivotal in advancing operational efficiency. The strategic integration of these technologies is not a one-size-fits-all solution but rather a tailored approach that considers the specific needs and contexts of each business. As the digital landscape evolves, so too will the tools we use to navigate it, leaving the door open for continued innovation and refinement.

Share your thoughts in the comments below. How have these technologies impacted your business? What strategies have you found most effective? Your experiences and insights are valuable to this conversation.

March Madness: The Road to AI

Mach Madness Bracket AI

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

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 β€“ target a specific area for improvement.
  • Measurable β€“ quantify or at least suggest an indicator of progress.
  • Assignable β€“ specify who will do it.
  • Realistic β€“ state which results can realistically be achieved, given available resources.
  • Time-related β€“ 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

Salesforce Data Cloud Resources

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.

Do you have insights and experience to share Data Cloud Resources?

Feel free to send us a note on any channel and we are happy to add your data cloud resource to this list.

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.