Make a submission

Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

The eighth edition of The Fifth Elephant will be held in Bangalore on 25 and 26 July. A thousand data scientists, ML engineers, data engineers and analysts will gather at the NIMHANS Convention Centre in Bangalore to discuss:

  1. Model management, including data cleaning, instrumentation and productionizing data science.
  2. Bad data and case studies of failure in building data products.
  3. Identifying and handling fraud + data security at scale
  4. Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
  5. Feature engineering and ML platforms.
  6. What it takes to create data-driven cultures in organizations of different scales.

Highlights:

1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.

Why you should attend?

  1. Network with peers and practitioners from the data ecosystem
  2. Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
  3. Demo your ideas in the demo session
  4. Join Birds of Feather (BOF) sessions to have productive discussions on focussed topics. Or, start your own Birds of Feather (BOF) session.

Full schedule published here: https://hasgeek.com/fifthelephant/2019/schedule

Contact details:

For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email info@hasgeek.com

Sponsors:

Sponsorship Deck.
Email sales@hasgeek.com for bulk ticket purchases, and sponsoring 2019 edition of JSFoo:VueDay.

JSFoo:VueDay 2019 sponsors:

Platinum Sponsor

Anatta

Community Sponsors

Salesforce Ericsson freshworks
databricks

Exhibition Sponsors

Sapient Atlassian GO-JEK
Bayer

Bronze Sponsor

Sumologic Walmart Labs Atlan
Simpl Great Learning

Community Sponsors

Elastic Anaconda Aruba Networks

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Ravi Ramchandran

@rramchan

Data Quality Management @Walmart Data Lake

Submitted Feb 27, 2019

Erroneous decisions made from bad data are not only inconvenient, but also extremely costly. According to Gartner research, “the average financial impact of poor data quality on organizations is $9.7 million per year.”
In additional research for organizations that Gartner has surveyed, the analyst firm “estimate that poor-quality data is costing them on average $14.2 million annually.” Definetely, Bad data is bad for business.
In a Data Lake environment, Robust Ecosystem Products are required to govern Data Quality.Since Data Lakes are based on an ELT model, quality is much more difficult to govern. With legacy systems, and also an existing lake, it becomes a more uphill task.
In this talk I will take the audience on the journey to build DQAF (Data Quality Assessment Framework), which is Walmarts Product for Continuous Data Quality Assessment, and how we are changing the entire way we look at quality. DQAF is based on Wang’s Philosophy of TDQM. We believe To increase productivity, organizations must manage information as we manage products.

Outline

Key areas I will cover

Data Quality Management - Overview and Challenges

  • Here I will define Data Governance, and how Data Quality Management ties into the same
  • Different facets of Data Quality Management,
  • Problem Areas
  • Solution Areas

The DQAF (Data Quality Assessment Framework) - Providing Data Quality Management for Walmart Data Lake

  • Overall Data Quality Management Journey for Walmart
  • Introduce DQAF, and Definintion
  • Architecture Overview
  • Architecture Blocks Details

DEMO

  • How DQAF Solved our Problem Areas, and Built into our Solution Areas
  • Quality Improvement is a Continous Journey ! - Journey Going Forward
  • Thinking of Quality in your Data Lake - Tips on how to baby step it

Requirements

Internet Access

Speaker bio

Ravi is an Senior Architect with the GDAP(Global Data and Analytics Platforms) Group in Walmart Labs. Ravi started, building products for primarily for banks, and governments. He developed a passion for scaling large scale products, early on when he was involved with Gujarat Govt for creating workflow solutions. Looking at ways to impact lives directly, he moved towards healthcare, where was build API and Cloud Products for PHC, IoT Devices, MIC, Data Pipelines, Data Conversational Engines
Data being the next frontier, Ravi has been dabbling in all things Data Platform since the last couple of years. He was instrumental in developing the IoT Platform, for GE Healthcare. Currently Ravi is responsible for key oversight of Walmarts next gen Data Platform on hybrid cloud, primarily in areas of Quality, Governance, Privacy and Operability.

Links

Slides

https://drive.google.com/open?id=1wq2_IJKxWv5COUTQ_wVIyMtBULmO9jiA

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Make a submission

Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

Hosted by

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more