Feb 16 Demo Day – Enterprise Data Management

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DATE: February 16, 2022

TIME: 8:00 AM – 11:45 AM Pacific / 11:00 AM – 2:45 PM Eastern

PRICE: Free to all attendees

Welcome to DATAVERSITY’s newest education opportunity, Demo Day, an online exhibit hall. We’ve had many requests from our data-driven community for a vendor-driven online event that gives you an opportunity to learn more about the available tools and services directly from the vendors that could contribute to your Data Management program success.

Register to attend one or all of the sessions or to receive the follow-up email with links to the slides and recordings.

Session 1: Preparing for Enterprise Data Fabric – Metadata Driven Approach to Data Quality

With the emergence of Enterprise Data Fabrics and Data Meshes, Data quality is becoming more important than ever before. This opens new unique challenges and opportunities.

Ataccama has perfected massively scalable Data Quality implementations for the last decade and developed metadata-driven methodologies to address the challenges emerging from ever-changing and growing data landscapes.

Join our session to learn:

  • Commonalities between Data Fabric and Traditional data quality approaches
  • Solution characteristics to look for when preparing your data quality practice for a Data Fabric world
  • Benefits of applying Data Quality automatically to your Data Management initiatives
  • How to use the power of Automated Metadata Management to drive your Data Quality
  • How Metadata Driven Data Quality can look in practice

Marek Ovcacek

VP of Platform Strategy, Ataccama

Marek plays a key role in the process of creating strategic roadmaps for Ataccama tools and validates the planned features and platform directions for future development. With years of expertise architecting and delivering data quality, data management, and data governance projects to enterprise customers, he leverages his considerable hands-on experience to identify and explain the vision for key Product components and architecture patterns both internally and to the experts and analysts in the enterprise data management community.

Session 2: Achieving High-Quality Enterprise Data in a Fraction of the Time and Effort

An organization’s data typically grows at a rate of 40% to 60% per year, with myriad data-oriented initiatives to go with it. Proper data quality is the bedrock of any successful data-oriented business initiative, from data governance and analytics to digital transformations and migrations. It can be a daunting challenge to understand the quality of your data, let alone know how to fix it.

Join Innovative Systems as we demonstrate how you can become a data quality expert and ensure that your next data-oriented initiative is a success. Topics to be covered include:

  • Innovative’s crowdsourced AI approach to data cleansing for rapidly improving your data
  • Our transparent and intuitive approach to entity resolution and data linking
  • Efficient quality monitoring for continuous data improvement
  • How to get started with your project – establishing a methodology for success  

Hector Cordova

Director of Global Consulting, Innovative Systems

Hector Cordova is Director of Global Consulting at Innovative Systems, assisting clients primarily in the areas of Customer Relationship Management (CRM) and Data Quality Management.  Hector has helped industry-leading companies around the world improve and expand their customer relationships by establishing and maintaining high-integrity, error-free central and distributed information databases.

As a software engineer and consultant on a wide variety of projects, Hector understands the needs of diverse organizational environments and has contributed to the development of advanced systems in areas such as customer information, credit, marketing, billing, ATMs, and branches. Prior to joining Innovative, Hector held management positions at Bital Bank, Computer Associates, Lusacel Telecommunications, and Inverlat Bank, all in Mexico.

Hector has a degree in Computer Science from the National Polytechnic Institute, Mexico City.

Sean Purcell

Product Manager, Innovative Systems

Sean Purcell is a Product Manager at Innovative Systems, working closely with all areas of the business and with clients in both the Data Quality Management and the Compliance industries. Delivering high-quality software that supports market trends and technology advancements while meeting each client’s needs has been central to ensuring the success of Innovative’s solutions.

His experience in consulting and software engineering has been integral to Sean’s leadership in the development of Innovative’s products and identifying ways that the various technologies can be leveraged to improve results for clients.

Sean has a degree in Computer Science & Business from Lehigh University in Pennsylvania.

Session 3: The Dashboard Paradox – Breaking the Adoption Barrier with Infused Insights

It’s time to think differently about data and analytics strategies. While many organizations have progressed from relying on static reports generated by IT teams to using some version of a self-service model, most still struggle to reap the world-changing benefits long-promised by experts. Unfortunately, each evolution has delivered only incremental improvements over the last wave of analytics, leaving the biggest problems half-solved, and data assets underleveraged.

With analytics infusion, you can create a better way to work and a better customer experience.  Learn more about:

  • The future of business intelligence and how your organization can stay ahead of the curve by thinking about life beyond the dashboard
  • Enhancing your data strategy to get more out of your data
  • How other companies are infusing analytics to not just meet, but exceed their hardest business objectives

Ryan Waters

Senior Director, Field CTO Office at Sisense

Leading the Field CTO team at Sisense, Ryan Waters focuses on outcome-driven solutions that harness the purpose-built Infusion APIs that Sisense has developed. His vision is not to deliver advanced data exploration tools to Sisense users but to make it impossible for those users to ignore relevant and actionable data. Technology is nothing without adoption, and his teams will happily show you how to put the data where your users already are and accelerate your data monetization strategy. Prior to his work in the data space, he was a developer in the TV industry and you can blame him for writing the code that put ads in your TV shows. When off the clock, he and his wife enjoy finding creative ways to day drink while entertaining their daughter.

Session 4: Modern Data Modeling Platform – From Data Modeling to Data Catalog

Taking the right architectural approach to data governance can build a strong foundation for trusted data throughout the enterprise.

An agile Data modeling management is proactive standardize increment schema designs, preventing data governance costly. 

Integrate data modeling and development, landing business glossary to data model, review data model in each iteration and publish baseline of data model to data catalog.

Join us for a deep dive into how we enabled a successful modern data modeling platform –

  1. Landing business glossary to data model in design phrase.
  2. Collibrate data modeling and branch management.
  3. Review data model in development iteration
  4. Publish data model to data catalog  

Allen Wang

Founder & CEO

Allen Wang is the Founder and CEO of Datablau.Inc. He has 20 years of experience in data modeling. He led the erwin development team from 2006 to 2016. From 2016, he founded the startup Datablau.Inc and built the next generation data modeling platform, which has served more than one hundred corporations. Allen was a speaker at the IEEE Big Data Congress, EDW, DataArchitectSummit and is considered a thought leader in data modeling, with several articles and patents related to this subject.

Session 5: Unlock the Power of Your Data Lake with an Enterprise Knowledge Graph

Companies have struggled to gain value from data lakes. They require extensive preparation and coding to make data accessible and queryable, making them prohibitively expensive and slow for analytics. As a result, data and analytics teams spend the bulk of their time wrestling data problems vs. delivering analytic insights, costing organizations billions in lost productivity and missed opportunities.

An Enterprise Knowledge Graph can fill that critical gap in modern data and analytic tech stacks. It fits nicely between where data is stored, cataloged, and consumed to eliminate data access barriers, add meaning to data through semantic models, and promote a culture of self-service and self-sufficiency.

Join our session and see what an Enterprise Knowledge Graph is and how you can:

  • Streamline access to your data
  • Discover new insights through inference
  • Empower your analytics

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