Name: Using automated visualizations to ensure confidence in your data
Start: 2022-03-22T20:00:00Z
End: 2022-03-22T20:00:30.000Z
Location: BrightTALK

Anomalo data quality management automatically detects data issues and understands their root causes so you can quickly and efficiently solve them. 

Digital transformation has been a buzzword for years, but for many companies the process is still an ongoing one. Determining where to automate legacy processes, or digitize these systems, can often be a headache for the business leaders involved. Business intelligence helps to relieve that. BI and data can provide the insights needed to make the best decisions on what to automate, what to digitize, and when. 

In this episode of Business Intelligence Report, we’ll examine how companies can leverage business intelligence to achieve their automation and digital transformation goals. 

Topics for discussion include: 
- BI’s role in digital transformation and how to make the most of your organization’s data 
- How business intelligence can help organizations make better-informed decisions to compete in today’s digital landscape
- The ways business intelligence and data help drive digital transformation initiatives
- And more

Transformation, Automation, and how Business Intelligence Affects the Process

While all companies value data quality, it is required in some industries through regulation. Learn from experienced data people that work across industries and many types of data on how companies can provide data quality in environments where legacy systems are combined with modern data stack tools with complex requirements.

Hear Antoine, Joe, and Jeremy discuss timely data topics such as:

- How is data engineering different than software development?
- How are companies using data management frameworks to help with cloud migrations?
- Are organizational changes due to technology changes? 
- How are data contracts used in regulated organizations?
- Why are we hearing more about data governance?

Scaling Data Quality in Regulated Industries

The new demands of digital-first strategies have pushed many companies to adopt new data platforms in order to keep operations running smoothly. Snowflake is one of the most popular choices in the market, but many users are only just scratching the surface of its potential.

Despite the best of intentions, McKinsey reports that fewer than 30% of digital transformation strategies are successfully implemented. Without the right support in place, a majority of companies are struggling to get a return on their technical investment.

This doesn’t need to be the case — and certainly not with your Snowflake warehouse. Data quality management (DQM) implementation can be tricky but there are a few key ways to optimize the rollout. 

This webinar presents a masterclass in Snowflake optimization, providing a deep dive into:

• The most common mistakes companies are making with DQM in Snowflake
• How to take your implementation from functional to optimal
• The power of compatible services: Qualities of the perfect partner for Snowflake customers
• And more

Snowflake optimization: How to achieve ROI with data quality management

Today’s digitized world has made every business into a data company: in fact, Domo reports that 2.5 quintillion bytes of data are produced every single day. The opportunity to uncover countless valuable insights has made data management an integral part of operations; when done right, it informs all other functions and streamlines the business.

But in reality, many companies are working with low-quality data and inefficient pipelines, which deplete resources for minimal results. And, while basic tools can be a good way to reduce overhead, with data quality management (DQM) this can actually cause more financial damage down the line. 

This webinar breaks down the causes and repercussions of a failing DQM strategy and offers key advice for achieving success. Webinar highlights include:

• What an inefficient DQM strategy looks like
• How to spot gaps in your existing data quality audits
• Which technology tools are most critical for a validated DQM pipeline
• And more

The business consequences of low-quality data

Future Tech's Dan Chaney, VP of enterprise AI and data science solutions, speaks with AI industry experts about how organizations and universities are seeking to educate, recruit, and retain tomorrow's AI leaders.

Accelerating AI & Data Science: Future Tech is at the forefront of enterprise AI & Data Science solutions. We help companied accelerate their use of advance AI & Data Science solutions. Our customized data science workstations can reduce end-to-end data science workflows by up to 80%, helping your team to work smarter, faster and safer. Ask how we can create you the right customized data science workstation stack.

Winning the War for AI Talent

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

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

Businesses today are collecting ever increasing amounts of data but are not extracting its full value. Many professionals struggle with problematic data structures, capability shortfalls and a lack of strategy. 

Join L.E.K. to learn how to make the most of the intelligence at your fingertips to improve your profitability and stay ahead of the competition.

The webinar will be interactive with plenty of opportunities to ask your own burning data questions and learn from peers across range of businesses like yours.

What will you learn?

• How to maximise the potential of the data you already have
• What are the right data strategies and structures to adopt
• How to use data to build commercial advantage

Who should attend?

• Senior leaders across all industries who want to extract value from data
• Strategy heads who want to embrace data-driven decision making
• Finance and Commercial Directors searching for performance improvement
• M&A and Business Development professionals qualifying opportunities
• Transformation teams hungry for data that can fuel change

How to Unlock the Value of Data in Your Business

Data teams and developers continue to serve as the lynchpin for data-centric businesses. As these personas increasingly collaborate, they enable organizations to overcome shortcomings associated with using growing data sets to gain insight. Improving data analytics for real-time business intelligence (BI) and customer insight ranks as one of the business priorities driving the most significant technology spending. In this keynote session, learn how business leaders are modernizing their approach to BI to gain data intelligence faster.

Modernizing Business Intelligence Creates Path to Data Leadership

Data Visualization & Analytics for Business Intelligence

When exploring and visualizing large datasets, you are usually faced with a tough choice between sparsely sampling your data and aggregation. In this talk, we'll show how to use Anaconda technologies to show all of your data, revealing both trends and outliers, and letting you explore even the largest datasets interactively in a web browser.

We'll discuss:
- How Datashader can render your entire dataset faithfully in milliseconds or seconds, with both trends and outliers easily visible. 
- How pairing Datashader with the Python-based interactive tools Bokeh, Panel, and HoloViews lets you work interactively with your data, selecting individual datapoints and local groups for further inspection and exploration
- How to make sense of incoming datasets and make them actionable without getting distracted by tuning plotting parameters or missing out on important insights.

Seeing the Forest and the Trees: Data Discovery in Billion Row datasets

Let’s face it. For all the incredible innovations in modern BI, we still face limitations when it comes to live connections to large enterprise sources. Starburst provides the single point of live access to all enterprise data, wherever it resides, through Trino’s ANSI-SQL MPP engine. With big data access, and used in combination with ThoughtSpot, users can query and analyze billions of rows of data across many sources at speeds thought unimaginable. Starburst and ThoughtSpot give business analysts the tools to ask new questions with easy expanded access to new data sources.

This session with Tom Nats, Director of Customer Solutions at Starburst, along with Sean Zinsmeister, VP of Product Marketing at ThoughtSpot will share how ThoughtSpot, used in conjunction with Starburst, opens new avenues by incorporating new data sources and performing high-speed queries at scale.

Bringing Big Data to Enterprise BI with Starburst & ThoughtSpot

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

Automatically detect data issues and understand their root cause before anyone else.  If this sounds like it could save you hours (or days) spent on tracking down data issues and the reason they’re occurring, see how Anomalo can begin automatically monitoring your data for quality issues within minutes. We’ll be looking at simple, fast, and intelligent visualizations of anomalies automatically detected in your data.

Using automated visualizations to ensure confidence in your data

Data

Data Analysis

Data Anomolies

Data Detection

Data Engineering

Data Issue

Data Quality

Data Quality Analytics

Data Warehousing

Machine Learning

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Enterprise Applications

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Using automated visualizations to ensure confidence in your data

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Anomalo