Tag Archive for: Artifical Intelligence

ProcureSQL Speakers at Day of Data Pittsburgh and St. Louis 2026

The ProcureSQL team is bringing practical insights on SQL Server performance, data protection, Power BI, and Microsoft Fabric to two Day of Data events this October.

John Sterrett will present in Pittsburgh on Saturday, October 10, 2026, followed by Meagan Longoria in St. Louis on Saturday, October 24, 2026. Both events offer in-person opportunities to learn, connect with fellow data professionals, and explore solutions to real-world data challenges.

Day of Data continues the long-running SQL Saturday and PASS community tradition, bringing together data professionals for a day of practical learning, technical sessions, networking, and conversations about the tools and practices shaping modern data work. Whether your focus is SQL Server, analytics, Microsoft Fabric, Power BI, data engineering, AI, or data leadership, these events create a valuable opportunity to learn from peers and practitioners who work with real-world data challenges every day.

John Sterrett at Day of Data Pittsburgh

When: Saturday, October 10, 2026
Where: Commonwealth Charter Academy, The Waterfront, 162 East Bridge Street, Homestead, PA 15120.

At Day of Data Pittsburgh, John Sterrett will present:

DBA Basics: Getting Started with Performance Tuning

This session at Day of Data in Pittsburgh is a good fit for newer DBAs, developers, accidental database administrators, and IT professionals who want to build confidence in performance tuning.

This session at Day of Data in Pittsburgh is a good fit for newer DBAs, developers, accidental database administrators, and IT professionals who want to build confidence in performance tuning.

Performance problems can be intimidating—especially for newer DBAs, developers, and accidental database administrators who inherit an environment where users are already reporting that “SQL Server is slow.” John’s session provides a practical starting point for identifying and addressing common performance issues.

Attendees will learn how to find the most resource-consuming statements and get foundational guidance on reading and understanding execution plans. The goal isn’t to chase one-off fixes; it is to build a repeatable process for investigating performance problems, prioritizing the most meaningful opportunities, and making informed tuning decisions.

This session is a great fit for:

  • New and aspiring DBAs who need a usable performance-tuning framework.
  • Developers who want to understand how query design and execution plans affect application performance.
  • IT professionals responsible for SQL Server environments who want a clearer troubleshooting process.
  • Data professionals who want to move from reactive firefighting toward more proactive database performance management.

Advanced Data Protection Strategies with SQL Server

For teams responsible for database security, application access, or data governance, this session at Days of Data in Pittsburgh is for you.

For teams responsible for database security, application access, or data governance, this session at Days of Data in Pittsburgh is for you.

As organizations manage growing volumes of sensitive data, protecting that information requires more than defending against external attackers. Application users and privileged employees—including database administrators—also need appropriate boundaries around what they can access.

In this session, John will explain how to protect data while maintaining functionality and performance. He will explore three key capabilities:

  • Column-level encryption.
  • Row-level security.
  • Auditing.

Attendees will leave understanding how and why these capabilities contribute to SQL Server data protection, helping them make more informed decisions about securing sensitive information.

For teams responsible for database security, application access, or data governance, this session complements the performance-tuning fundamentals with another essential responsibility: protecting the data entrusted to your organization.

Day of Data Pittsburgh takes place on October 10, 2026. If you are attending, be sure to connect with John, ask questions, and share the performance challenges you are seeing in your own SQL Server environment.

Meagan Longoria at Day of Data St. Louis

Two weeks later, Meagan Longoria will represent ProcureSQL at Day of Data St. Louis on October 24, 2026, hosted at the Microsoft Technology Center in St. Louis.

When: Saturday, October 24, 2026
Where: Microsoft Innovation Hub, Microsoft Technology Center, 4220 Duncan Avenue, 5th Floor, St. Louis, MO 63110.

Meagan will present:

Challenges and opportunities in using AI for Power BI Report Development

 

Exploring AI for Power BI development, this session will help you think beyond speed and focus on the quality and usefulness of the finished report.

Exploring AI for Power BI development, this session will help you think beyond speed and focus on the quality and usefulness of the finished report.

AI-assisted report development is generating plenty of excitement, but it does not remove humans from the design process. Why do some attempts to generate charts and reports with AI work well while others fall flat?

Meagan will examine the report and visualization development process, taking an honest look at what AI tools can do today and where human judgment still drives the outcome.

The session looks beyond generating visuals faster. It explores how AI can support better design decisions, clearer communication, and reports that people actually use.

Attendees will leave with a practical framework for deciding:

  • When to lean on AI.
  • When to take the wheel themselves.
  • What to expect as these tools continue to evolve.

If you’re exploring AI for Power BI development, this session will help you think beyond speed and focus on the quality and usefulness of the finished report.

Mirroring for SQL Server in Fabric: Inside the Replication Process

Meagan will take a technical look at the full replication process, tracing changes from the source database through how data lands and is retained in OneLake.

Meagan will take a technical look at the full replication process, tracing changes from the source database through how data lands and is retained in OneLake.

Mirroring for SQL Server in Microsoft Fabric promises near-real-time replication to OneLake—but what is actually happening under the hood?

Meagan will take a technical look at the full replication process, tracing changes from the source database through how data lands and is retained in OneLake.

The session will also cover:

  • Configuring replicated objects.
  • Monitoring replication health.
  • Understanding the conditions that trigger a table replication restart.

For DBAs, data engineers, and architects evaluating or implementing SQL Server mirroring, this session offers an opportunity to move beyond the feature overview and understand the mechanics that matter when operating it.

The St. Louis event is free and in person, though attendance is limited by capacity. Organizers note that prior events have reached capacity, so prospective attendees should register early.

Practical Learning, Community Connections

At ProcureSQL, we value opportunities to share knowledge, learn from other practitioners, and help data professionals address the challenges they face every day.

These four sessions reflect that commitment: finding and understanding performance problems, protecting sensitive information, making thoughtful use of AI, and understanding the replication processes behind modern analytics platforms.

Day of Data builds on the SQL Saturday and PASS community tradition, bringing professionals together for technical learning and connections across the data community. St. Louis’s event includes tracks spanning SQL Server, Microsoft Fabric, Power BI, data engineering, AI, and professional development.

Whether you’re focused on maintaining your existing SQL Server environment or expanding into modern analytics, we hope you’ll join us and bring your questions.

Register and Join Us

  • Day of Data Pittsburgh — October 10, 2026: View event details and the latest schedule. The event offers a full day of free training for Microsoft data-platform professionals.
  • Day of Data St. Louis — October 24, 2026: View the schedule and register. The event is free, but organizers say purchasing a lunch ticket provides priority attendance and guarantees a spot; without one, entry is available only while space remains.

Join John in Pittsburgh or Meagan in St. Louis for practical learning, technical conversations, and a chance to connect with the ProcureSQL team.

Follow ProcureSQL for future event updates, practical SQL Server and data-platform guidance, and insights from our team as we help organizations build, manage, move, analyze, visualize, and govern their data.

/0 Comments/by

Machine Learning for Operational Efficiency

Every year, hundreds of billions of packages are shipped around the world. And in 2023 alone, that number broke the record at 356 billion shipped worldwide.

 

That’s a 100% increase since 2016. The industry will only get bigger and more complex, as estimates show that by 2028, nearly 500 billion packages will be shipped globally.

 

As supply chains become more bloated with delivery demand, they become more complex, multiplying efficiency problems.

 

This is where Machine Learning and Artificial Intelligence (AI) step in.

Companies worldwide, including many Fortune 500 names, have turned to Machine Learning and Artificial Intelligence to fine-tune their logistics and, most importantly, increase revenue and reduce costs

If you are not using Machine Learning, you are falling behind.

 

Companies worldwide, including many Fortune 500 names, have turned to Machine Learning and Artificial Intelligence to fine-tune their logistics and, most importantly, increase revenue and reduce costs.

 

When shipping out millions of packages per year, saving 5% in cost at every step is a massive boost to profits.

 

The numbers don’t lie either. Companies have already shown that AI is helping them meet their revenue goals. According to McKinsey, 61% of manufacturing executives report decreased costs, and 53% report increased revenues as a direct result of introducing AI into their supply chain.

Three Common  Machine Learning Models

Several kinds of Machine Learning models have been designed to tackle different problems. Today, I will focus on three types of modeling systems: Time Series Models, Regression Models, and Classification Models.

 

Each offers something unique, and all can lead to efficiency improvements in the supply chain in different ways…

 

Time Series Forecasting Models

 

Time series forecasting is as simple as it gets when you want to improve efficiency with Machine Learning. The model takes historical data such as sales, seasonality, and trends and uses it to make predictions for the future.

 

For instance, let’s say you’re in the clothing apparel business and need to stock your warehouses full of winter gear for the upcoming season. Time Series forecasting would allow you to look at previous years’ sales and show you exactly when to stock winter gear for the upcoming cold months. This means you won’t stock too much wrong gear too early and take up precious space.

 

Likewise, it can keep you prepared for any major spikes in a single item based on sales trends. If you notice that in November, sales of winter apparel increase by 40%, you can ensure you aren’t caught off guard and avoid the risk of being understocked.

 

In doing so, you also keep customers happy, knowing they can always get the item they need. Plus, you’re meeting the demand of what they will buy. Very few are buying sandals in January. But if you find out that data shows that it’s still 10% of your sales, you might as well keep selling them and not lose out on that profit.

 

Regression Models

 

A regression model is an AI tool that finds the relationships between two variables and how one affects the other.

 

In our supply chains, regression models are well-equipped to aid in predicting delivery times of goods. The reason is that there are so many variables that go into transportation such as distance, weather conditions, and traffic.

 

Machine Learning and AI can look at the historical data of all of these variables and give you a leg up on the proper route to send your material from point A to point B. If you can avoid traffic, reroute around bad weather, and fully optimize routes taken, then you save on gas and time – reducing overall costs in the process.

 

Much like time series forecasting, regression models also allow you to predict demand on how big of a staff you need. If you’re in charge of a warehouse, for instance, variables such as the number of orders, seasonality, and even the day of the week affect exactly how many staff you need to pick items, box up, and ship out orders.

 

Furthermore, these factors affect how large of a fleet you need to be able to deliver your items. Having two trucks does no good when your demand requires eight. Likewise, having 100 workers at a warehouse doesn’t make sense when you only need 20 working that day.

 

Classification Models

 

Finally, let’s talk about classification models – AI tools designed to factor in many different variables and allow you to assess a wide variety of things from risk assessment to how to market to certain customers.

 

For instance, when it comes to risk assessment, you could use a classification model to let you know which items are particularly prone to getting damaged. Factors that you could put in are the packaging used to handle the item, travel conditions (if it is going to be on a bumpy road or in extreme temperatures), and distance (how many different handlers are going to touch it).

 

If you know all of these factors ahead of time, and allow AI to assess it as a high or low-risk item, then you can take precautions so it doesn’t arrive damaged. You can beef up the packaging, adhere to strict orders, or arrange for special transportation to ensure that nothing goes wrong.

 

On top of that, when delivering items, you can use classification models to determine if a package is at a high or low risk of arriving late. Factoring in traffic, weather, distance, holiday seasonality, and package weight all are factors that you can use to give an estimate of when something can be delivered. This keeps customers and clients happier, as they’ll have a better understanding of when exactly they will receive their items.

 

Finally, you can even group your customers into different categories to give you a better idea of who to target with internal marketing. For instance, those with high spending habits or those that always pick next-day delivery would be more worth your while to target with marketing efforts than those that spend less or select standard delivery.

 

Machine Learning Can Improve Your Business Today

 

As we can see, the different Machine Learning and AI models out there offer a huge variety of insight across the board when it comes to supply chains.

 

The best part is that the examples I mentioned are only the tip of the iceberg when it comes to providing solutions to supply chains and logistics.

 

Machine Learning and AI have changed the game from inventory management to quality control to delivery efficiency and more. Its ability to fine-tune processes and optimize efficiency gives companies a leg up on their competitors. And every year, AI and Machine learning models get better and better.

 

With companies from Walmart to Nike to FedEx and more adopting Machine Learning and AI into their supply chains, it only makes sense that other companies mimic their success and do the same.

Contact us to start a discussion about whether ProcureSQL can guide you along your Machine Learning and AI journey.

 

 

/by