From the rapid rise of ecommerce, to labor shortages, to geopolitical  and macro economic tensions - CPG & Retail supply chains are being impacted in a myriad of ways. Join us as we share how companies have solved key inventory planning challenges by leveraging AI driven demand forecasting to bring agile, smarter decision making to daily supply chain operations. 

Hear how o9 integrated Google Cloud’s Vertex AI Forecast into their Digital Brain solution to solve forecast accuracy problems and ultimately reduce risk, improving financial performance. The Vertex AI Forecast offering, when integrated into the o9 Digital Brain’s open architecture framework, providing a more comprehensive supply chain solution.The end result helps ensure that demand will be met without running the risk of having excess and obsolete inventory.

Supply chain planning with AI

The increasing adoption of multicloud is leading to business-critical data getting more siloed. This makes it difficult and time-consuming for data engineers, architects, and analysts to quickly gain value from data. BigQuery Omni connects to various cloud data sources and provides a single pane of glass to access the data using SQL – like queries without leaving the interface. Looker allows you to explore, share, and visualize your company’s data to make better business decisions. In this session, you’ll see a demo of how to break down data silos using BigQuery Omni and Looker to securely and cost effectively analyze and visualize data across clouds.

Cross-cloud analytics and visualization with BigQuery Omni and Looker

At the core, Machine Learning Operations (MLOps) takes an experimental machine learning model into a production system. MLOps is an emerging practice distinct from traditional DevOps. ML lifecycle involves using patterns from training data, making MLOps workflow sensitive to data changes, volumes, and quality. Additionally, matured MLOps should support monitoring both ML lifecycle activities and production model monitoring.

Key Takeaways:
• Build cloud-native serverless analytics pipeline. 
• Learning different data automation pattern including data lake, lake house and data mesh architecture.
• Automate end-to-end data pipeline with MLOps.

Decoding Data Automation With Cloud-Native Analytics Pipeline and MLOps

Content is more pervasive than ever, and enterprises need to keep pace to effectively manage and leverage it. Whether managing owned in-house content or from outside sources such as social media and YouTube, organizations need a way to manage that content at scale, improve searchability and shareability, and automatically produce insights that help them more efficiently and effectively run their businesses.

In this webinar you will:
 - Uncover the top areas where you’re likely not making best use of your content today
- Learn how AI-powered digital media asset management can automatically produce more insight and workflow automation
- Discover how AI can help identify faces, logos, on-screen text, and more in unstructured media content such as video and audio
- Hear how PROGRESS, a German film archive with more than 20,000 assets, is making its trove of historic and modern films easier to discover, share, and monetize
- Understand how to accomplish all of this and more using an enterprise AI platform approach

Don’t miss this informative event — register today.

Next-Generation Asset Management: How AI Helps Enterprises Activate their Media

Promises, promises… everybody’s swearing that your unstructured data is a goldmine. All you have to do is buy their software, engage their services, hire fancy tech pros and magic will happen. Buy, buy, buy, right? But you’re not so sure.

The real secret to lucrative results is no secret at all. You’ve got to align data and analytics with your everyday business goals and practices. It’s not rocket science, yet you must understand and manage the process.

In this session, you will learn:
• How to spot the easiest opportunities to profit through data analytics. 
• Why technical staff can’t produce the results you want without active management involvement.
• When to integrate unstructured data with predictive analytics to support decision making.

Make Your Data Pay -- What Non-technical Executives Need to Know

The extraction of actionable information from data is a key enabler for optimizing workflows, automating processes, and providing meaningful insights to decision makers. But we live in an unstructured world, and unstructured sources of data can be particularly difficult to parse. In this session, we will share the latest developments in the field, including the modern tools and machine learning techniques employed by insight engines to intelligently extract information from unstructured data. We’ll also address the following questions:

  *   What are the challenges we face in processing unstructured data sources?
  *   Which tools are available to extract insights from this data?
  *   How can we use extracted insights to automate processes and workflows?

We will close the session by providing a real-world example of process automation employed to assist with bid management.

Business Transformation Through Insights-Driven Automation

While traditional approaches to data integration provide much needed aid in efforts to synchronize data across silos, this delivery is just table stakes now. To maximize and deliver scalable value of data investments, organizations should employ a semantics-based approach. Semantic frameworks embed human understanding of concepts and relationships with your data assets, helping your systems grow smarter and better equipped to discover rich linkages and reveal non-obvious relationships. Through semantic frameworks, business and technical users can have access to a semantically enriched, highly linked and available store of firm data, upon which they can perform smart data analysis and leverage a reusable framework for deploying machine learning models. 

In addition to explaining and making the case for a semantic integration framework, Reclassify AI will cover key use cases and the successful deployment of semantic data integration for AI-enabled decision-making.

The Future of Decision-Making: Human-Powered AI

Organizations looking to maximize business intelligence (BI) use may consider constructing a business intelligence team consisting of four key roles -- the expert, designer, analyst and steward. In this session, we'll detail these four important roles -- how to hire them, how to inspire them and how to manage their work.

Attendees will learn:

• How to integrate existing informal skills and practices into a new practice.
• The steps to build BI team from scratch.
• Inspiring and retaining key roles
• Pitfalls -- personal and technical -- to watch for .
• Expanding new capabilities such as data science and AI

Keys to building a successful business intelligence team

Business Intelligence as a practice has rapidly evolved since the pandemic - the expectations in 2022 are that Analytics are not only accessible, but also actionable in real time.  Data Automation capabilities in Customer Data Platform solutions closely align with BI organizations, both disciplines share common goals and benefits, and, if integrated correctly, can lead to new Revenue streams that add 5-15% to ROAS due to Personalized Customer Experiences.  

Setting them up as a Data Center of Excellence enables the CDP to produce significant operational ROI due to real time automation and uniform process governance.

Join this session to learn:

• What a Data Center of Excellence (DCoE) is
• How incorporating BI and a CDP into your DCoE can increase revenue and operational excellence 
• Business practices and topics in scope for optimization by a DCoE

The Perfect Marriage: BI and CDPs Create a Data Center of Excellence

With a gigantic increase in data capturing capacity, corporations, government agencies, and research organizations face complexity in determining the best variables for their data models. Feature engineering techniques like exploratory data analysis are valuable for establishing the right variables for different statistical and machine learning models. In this session, attendees will learn the basic steps in R programming (and comparable techniques for Python) with use cases to apply these techniques in a sample training data model.

Topics will be based on the following: R programming, R Studio, Python, Machine Learning, Data Science , APIs, Feature Selection/Feature Engineering, Exploratory Data Analysis, and Dimensionality Reduction.

This session is meant for developers and managers using R or Python with an interest in statistical models or machine learning. The session will cover a few basic data type concepts. Prior experience with R or Python is not necessary.

Do More With Data We Have: Engineering Strategies To Match Good Data to Products

Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They also need a platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. Above all, they need a worry-less experience with the architecture and its components.

A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $2M to $14M. Get this data point as you take the next steps on your journey.

Setting the Budget for the ML Stack for Analytics

The amount of global data creation and replication will grow at a compound annual rate of 23% for the next three years, according to research firm IDC. That makes managing enterprise data more critical than ever, and business intelligence is playing an important role. As adoption of BI increases across enterprises, businesses are looking to advanced capabilities to address challenges with sophisticated use cases and allow all levels of stakeholders to perform tasks that previously required  the skills of a data scientist. 

In this panel discussion, experts in the field will explore the features that will help organizations move toward a data-driven culture. These include augmented analytics, embedded analytics, self-service and interactive enablement, mobile application delivery, and natural language querying.

Join us to find out:
--Which BI features can best support your business model and goals
--Augmented analytics capabilities that give business users power to build their own queries
--Guided analysis that steers users through the appropriate workflow
--How self-service and interactive tools can improve productivity and expand data comprehension and usage

Moderated by:
Craig Stedman, Industry Editor, TechTarget

Panelists:
Ryan Ries, Practice Lead, Mission
Donald Farmer, Principal, TreeHive Strategy
Pierre DeBois, Founder and CEO, Zimana

Advanced BI Features Help Democratize Data

While there are a lot of discussions on what defines good analytics and reliable insights, how do we recognize bad analytics? Bad analytics involves more than lacking good insights. Unless companies can actively identify bad analytics, they cannot prevent them. In this presentation, Dr. Prashanth Southekal, Data Analytics Consultant, Author and Professor, and inventor f the DEAR model for data-driven decision-making, discusses the 10 key characteristics of bad analytics. 

Here are the key takeaways for the audience upon attending my presentation.
1. Understand good and bad analytics.
2. Spot for patterns for bad analytics based on 10 key characteristics.
3. Improve the adoption of data and analytics solutions in the organization.

Bad Analytics: What They Are and How To Avoid Them

As data is increasingly critical in order to draw insights and expand business potential, companies must be able to process this data faster, more accurately and in time to monetize on critical business advantages. That's when automation of data processing techniques become essential. 

Data security is another critical aspect to consider as companies increasingly handle more customer data. In this chat, we will look at certain data automation and security principles that an organization should follow in order to best ensure the trust of their customers.

Ensure customer trust with data automation and security best practices

The Future of Business Intelligence

The past two years have taught us that "business as usual" is a thing of the past, exposing sharp differences between companies prepared for the digital economy and those that were not. Organizations are looking for ways to leverage their digital assets to best position themselves to succeed through disruption. In this session you will hear stories of different organizations that used business intelligence and analytics to successfully navigate their digital transformation journeys.

The Future of Business Intelligence

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