Name: Next-Generation Asset Management: How AI Helps Enterprises Activate their Media
Start: 2022-05-12T17:00:00Z
End: 2022-05-12T17:00:57.000Z
Location: BrightTALK

Veritone is a leader in enterprise AI software, services, and applications, helping companies transform their operations and solve the complexities of digital information today. With our AI solutions and rich partner ecosystem, our customers can address their current and future challenges, empowering them to run more efficiently, accelerate decision making, and gain a competitive edge.

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

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

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.

AI and machine learning are closely linked to digital transformation, and is perhaps why 2021- a year of immense business transformation - has seen such a shift towards MLOps and ModelOps. In fact, according to a recent IBM survey, 21% of respondents said that AIOps had a “transformational” relationship between IT and other parts of the business.

Some key takeaways from organisations implementing MLOps include observability and end-to-end visibility, automation of complex processes, and support on the cloud. Evidently, MLOps and ModelOps can have a real impact on the relationship between moving parts in the organisation, and done right, can have a transformative impact on your business.

Join us in this session as we discuss:
-  How MLOps and ModelOps have impacted transformation efforts in 2021
-  Best practices for implementing MLOps in your organisation
-  Overcoming challenges when it comes to implementation
-  ModelOps challenges and how to overcome them
-  What’s next for MLOps and ModelOps as we move into 2022

MLOps & ModelOps: What’s next?

The last 18 months have seen rapid transformation in the workplace with the onset of hybrid working. A parallel can be drawn between the growth of hybrid working, and the growth of AI, but how can the two go hand in hand to support employees and leaders, enabling them to work efficiently and productively even when the workplace faces rapid change? 

AI-powered tools automate business processes, enable collaboration between distributed employees, offer opportunities for learning and development and much more. As we look to the future, and hybrid work becomes our ‘new normal’, AI and machine learning will undoubtedly accelerate our transition to hybrid work, and shift working processes. 

Join Ramesh Dontha, DX pro, and speakers as they discuss: 
-  The problems that AI can solve in the move to a fully hybrid workforce
-  Privacy concerns, especially when considering employees who are fully remote, and how to overcome these challenges
-  Chatbots, and the possibility of robot coaches in remote work settings 
-  AI-enabled solutions and tools available today, and how to leverage their full potential

How AI is Powering the Move to a Hybrid Workforce

There is a culture of fear behind AI and Machine Learning that stems from a lack of understanding and a fear of job loss. But in 2021, how rational is this fear? 

In this month’s episode of AI: The Future of Business, we’re asking this question. We’ll be breaking down how AI is enabling many businesses and industries to do their best work, and we’ll be asking to what degree AI can open doors rather than close them. 

Join us as we discuss:

-  Why there is the misconceived notion that AI and Machine Learning are threats to the human workforce
-  Where the mainstream culture of fear derives from and how businesses can help their workforces overcome this fear
-  The practical ways in which AI enables us to work more productively and collaboratively
-  The role of AI in enhancing employee happiness, productivity and careers
-  And more

AI and The Future of the Workforce: a Hindrance or a Help?

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

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

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

Asset Management

Monetization

Welcome to the big data and data management community on BrightTALK. Join thousands of data quality engineers, data scientists, database administrators and other professionals to find more information about the hottest topics affecting your data. Subscribe now to learn about efficiently storing, optimizing a complex infrastructure, developing governing policies, ensuring data quality and analyzing data to make better informed decisions. Join the conversation by watching live and on-demand webinars and take the opportunity to interact with top experts and thought leaders in the field.

Big Data and Data Management

As an IT professional, many of the problems you face are multifaceted, complex and don’t lend themselves to simple solutions. The information technology community features useful and free information technology resources. Join to browse thousands of videos and webinars on ITIL best practices, IT security strategy and more presented by leading CTOs, CIOs and other technology experts.

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

Presented by

About this talk

Veritone, Inc.