Submit a talk on data

Submit a talk on data

Submit talks on data engineering, data science, machine learning, big data and analytics through the year – 2019

##This space is open for submitting proposals on data engineering, data science, machine learning, big data and analytics through the year in 2019.

We will host data events round the year, in 2019. Talks for these conferences will be selected from here. Submit a proposal any time.

##Should you have queries, write to us on fifthelephant.editorial@hasgeek.com or call us on 7676332020

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

Sampriti Das

@sdas2112

How to eat an elephant?

Submitted May 4, 2018

Target Corporation is a global retail organization, has around 1800 stores & a digital presence, with over 350k team members. Given the scale and scope of the organization, it is even more important to have appropriate automated management of data in place. In this paper, we would share a story about how an ETL process, primarily used for Target’s Identity and Access Management (IAM) governance work, was automated. This did not only yield benefits from automation standpoint, but also saw analysts integrate into the engineering culture. Like for any product, the creative mind set continues to bring in innovative solutions/enhancements.

Background:
• To support our internal and external compliance requirements, it is imperative to be able to scan and get accurate and complete data for all in scope end points, be it infrastructure, applications, tools.
• Extraction, Transformation, Load commonly known as ETL is a known process in data warehousing, responsible for pulling data out of source systems, transforming the data into an accepted format and then placing into a data warehouse.
• ETL initially was a 100% manual process. Due to the sensitivity of the usage of this data, there is a high significance on the accurate, error free and timely delivery of this process. Because this complicated process is highly human dependent and real-time accuracy is critical, it was a natural contender for automation.

The approach:
• Hear how we moved from a manual to automated system while supporting continuous learning opportunities in new areas of work and making a difference.
• The answer was simple but not easy “Get the team that was operationally working like a human factory, trained on the new open source tool”. How to eat an Elephant? Bite by bite!
• The transformation had to be first brought in within the mind set to accept the change. Only an empowered and motivated team member can share the same vision and drive for results. This made it easier to tackle the technical aspect. This served our purpose very well.
o We identified 4 areas of focus for us:
 Being BOLD
 Being ACCOUNTABLE
 Being ONE team
 Being CURIOUS
The Results: With a strong focussed application of the above solutions, the team did not just meet the goal but went on to complete the project with a quarter to spare. This encouraged and enabled the team to head start on more automation projects.
• What worked and why?
• Challenges and learnings
• How do you sustain? What keeps it going?
• Next plan

Outline

Problem Statement
Approach to Solution
What worked
Maintenance

Speaker bio

Sampriti Das, Lead Information Security Analyst
Sampriti Das has been with Target for the last 3+ years and has close to 10 years of overall work experience.
Ms Das is currently serving as an Information Security Analyst in Target, wherein her role primarily is associated with compliance and governance within Identity and Access Management. In her current role, she is also involved with projects around automation in the Identity and Access Governance area.

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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