Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource. 

Subscribe to this channel to learn best practices and emerging trends in a variety of topics including data governance, analysis, quality management, warehousing, business intelligence, ERP, CRM, big data and more.

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

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

BI Salesman: Business Intelligence!
Inigo Montoya: You keep using that word. I do not think it means what you think it means.

Business intelligence is mostly a misnomer. It is more accurately defined as the automated analytics of internal metrics that determine the current state of the business. In neurological terms, it is referred to as "Interoception" or the ‘conscious or unconscious sense of the internal state of the body. While business intelligence is certainly important, it is not machine intelligence. So the distinction is important.

The future of business intelligence is outward-looking, capturing data and information about the business ecosystem -- consisting of markets, supply chains, competitors, customers, regulation, laws, and Russian dictators -- and inferring the strategic and tactical actions available to best achieve the corporate mission.

Today’s business intelligence failed the artificial intelligence test considering supply chain disruptions and war in Europe. Tomorrow’s business intelligence can and will get much better. Tune into this webinar to find out more.

Business Intelligence? Not so much....yet.

The ability to turn raw data into business intelligence is a key strategic advantage for organizations in the information age. According to research from ESG, 78% of businesses plan to increase spending on BI platforms in 2022 in order to improve operational efficiency, forecasting accuracy, and quality of products and services. This involves a clear understanding of emerging BI platforms and tools and how to use them to increase data extraction and automation, implement privacy and security mechanisms, and generate results that are easy for users to grasp.

This panel of experts will discuss how to effectively integrate BI initiatives across the organization while maintaining data security and integrity, providing valuable insight that leads to a future of better decision making and optimized business processes.

Join us to learn about:
--New types of BI platforms, tools, and integrated systems
--Pros and cons of cloud-based BI
--Automating data integration to streamline processes
--Advances in intelligent data extraction
--Emerging best practices to holistically ensure data security

Make Data Accessible and Actionable, Today and Tomorrow

At the core, Machine Learning Operations (MLOps) turns an experimental machine learning model into a production system. MLOps is an emerging practice distinct from traditional DevOps. The ML lifecycle aggregates 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

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

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

Bringing a Data Science program into large, entrenched companies poses significant challenges on two fronts: First, Enterprises' internal processes, organizational structure and culture pose a barrier to building a successful Data Science program. Second, data science and machine learning come with their own intrinsic challenges. Some problems take longer to solve than others. Furthermore, not all problems are solvable by data science. The speakers share their experience and views on both the Internal and Intrinsic challenges of establishing Data Science as a Core function within Enterprises.

The Essentials to Data Science Success within Enterprises

We are witnessing ever increasing innate human desire to gain insight in every industry and every walk of life. Our professional and personal lives are reliant on data and frictionless data technology to make sense of the insight to achieve significant outcomes. This insatiable human demand can only be fulfilled provided data and frictionless technologies are enabled with free and open approach.

Join me and industry leaders to explore industry trends to democratise data for humans to thrive in a digital data economy, we will discuss:

• What are significant challenges corporate humans (I.e., employees) face?
• Why should they care about this?
• What impact it has on their customers and their company’s success?
• Can AI/ML models ever make these humans effective in a closed data ecosystem?

Debarshi Bandyopadhyay, Principal Consultant & Delivery Partner, Infosys UK Limited
Hari Yedoti, Group Head of Data, Liquid Telecom

The Human Data Democracy

The global big data and business analytics market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 274.3 billion U.S. dollars by 2022, according to Statista.  As there's more data being made available on nearly anything in the world, so is the number of ways businesses can leverage that data. Big data management solutions and technologies are evolving to give companies the best data-driven analytics and predictive insights with unparalleled accuracy.  While there are endless possibilities in what can be done with big data, companies need to know not only how to organize their data but leverage it to get the most out of it.  

Join this panel to stay up to date with what you need to know to plan for 2022 and learn:  

-What trends to look out for in 2022 
-How to sort through and leverage big data to get quality data-driven insights 
-How to use common Big Data technologies 
-How to keep your company up and track real-time data

Panelists:
Pragyansmita Nayak, Ph.D., Chief Data Scientist at Hitachi Vantara Federal
Sandra Carrico, VP, Machine Learning at Sorcero
Sean Hearne, Director, Program Management Office at Fiserv

Trends in Big Data and Analytics in 2022

Today, big data has the potential to transform functions, industries, organizations, and governments with an unprecedented impact. Complacency is not an option. You may be asking, “Where are the opportunities?”, “How can we drive business value using data visualization with bottom-line impact?” Join me as we explore these questions and more.

Leveraging Big Data Analytics & Visualization for transformational growth

With data quickly evolving into an organizations most vital asset, it has become increasingly important to invest in visualization tools that help generate insights. Companies such as Tableau, Power BI, and Looker have made it easy to create visuals and share information across an organization, but just creating a visual isn’t always enough. There must be a compelling narrative, anchored by facts, to facilitate the communication that ultimately drives decision making. In this session, you will learn the science behind the message your visuals give off and how you can create more engaging visuals for your audience.

The Art of Storytelling through Engaging Visuals

Data monetization is used for realizing a type of competitive capability for organizations. It provides organizations with flexibility for using information assets in response to customer expectations and marketplace exposure.
Traditionally, Business Intelligence and Data Visualization were used only for descriptive analytics on internal data assets. In the recent times, there is increasing adopt of visual analytics in data monetization initiatives. Building a self-serve B2B Embedded Analytics Portal to productize the data offering from an organization.

Summarizing the benefits:
· Analytics application for data monetization strengths the business value of the product
· Improved data exploration and data analysis for a data product
· Consumption of greater volumes of data in less time, which improves operational efficiency
· Early detection of otherwise overlooked trends, outliers, and correlations between data assets, which may result in a competitive edge

Building Commercial Products using BI and Visual Analytics

With cloud permeating everywhere, it has become imperative for all Telco companies to adopt and migrate their data to cloud for all analytic workloads.
How has this effected BI workloads? Do cloud based BI solutions help Telcos?
Does Analytics in cloud deliver simpler path to predictive models? Uniform and standardized workloads?
Does it help in reducing implementation time, better data availability? And scalability of models?
A user’s perspective in Telco

Data Fabric for Analytics & Cloud : A Telco Realty

Due to recent events, the remote work process is a necessity now. It's equally important for startups and the corporate world. As a startup founder or a product manager, it's your duty to ensure your business success not only from the marketing perspective but from the perspective of building a great product users would love. During this presentation, Alexey will share the process his team used to manage over 150 projects and over 600 developers over the past 3 years. You will learn:

- How is remote work process different from "in-office" setup
- How to structure the remote team management process for your product development and UX teams, and much more.

How to Structure Remote Team Management Process for Results Based on Data

Panel discussion on the advent of artificial intelligence and machine learning in startups and business.

Panelists:
Mike Schmidt, Co-Founder, Dovetale, Inc.
Vijay Nadadur, Co-Founder & CEO, Stride.ai, Inc.
Joshua Montgomery, CEO, Mycroft AI
Brett Kuprel, Ph.D. Student, Standford AI Lab

Panel: AI - The Backbone of the Modern Startup

artificial intelligence

machine learning

Enterprise applications have become a crucial piece of infrastructure for many businesses. The enterprise applications community on BrightTALK features the latest insights in enterprise application integration, enterprise application architecture and EAS software. Join the community to gain access to thousands of videos and webinars presented by recognized enterprise information systems experts and arm yourself with the knowledge you need to succeed.

Enterprise Applications

Practicing business intelligence allows your company to transform raw data into sets of insights for targeted business growth. The business intelligence and analytics community on BrightTALK is made up of thousands of data scientists, database administrators, business analysts and other data professionals. Find relevant webinars and videos on business analytics, business intelligence, data analysis and more presented by recognized thought leaders. Join the conversation by participating in live webinars and round table discussions.

Business Intelligence and Analytics

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.

Panel: AI - The Backbone of the Modern Startup

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