Balvinder Kaur Khurana
Thoughtworks Technologies, Data Strategist, Global data community lead
Pune, India
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Balvinder has 18 years of experience in developing custom software and big data platforms for complex client problems, with a focus on Agile practices. She has worked in various domains such as retail banking, lending, credit rating, heavy equipment manufacturing, health, and retail. Balvinder's expertise includes complex systems architectures, distributed computing, business and data/tech alignment, data strategy, data architecture, data platforms, data modeling, data quality and security, and business intelligence. Balvinder is also the Global Data Community Lead for Thoughtworks, in addition to her work on client projects.
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Kickstarting Data Mesh: A Tailored Approach to Decentralized Data Architecture
Data Mesh is a socio-technical paradigm designed to manage analytical data at scale. While we've heard extensively about its concepts, including the four pillars and how it can help organizations become truly data-driven, implementing Data Mesh in practice requires careful consideration. This paradigm is not just about technological transformation; it also involves changes in people, processes, and operations. Therefore, these concepts must be tailored to fit the specific organization undergoing this transformation.
In this talk, I will share our experience of kickstarting such a transformation. We began with a small-scale data initiative for a complex enterprise using the Data Mesh approach. By examining each principle individually and determining which aspects are most suitable for the organization’s current state and needs, we tailored the implementation to align with the organization’s maturity level. This led to a truly decentralized data architecture, where each business team owns its data and related assets entirely. This decentralization helps the organization evolve and scale rapidly in response to changing business needs.
Data Mesh from a practitioner’s lens
Data mesh is a novel approach of data strategy and executing enterprise data architectures. It goes beyond just the technology and architectures, and also covers people, processes, operations and organization structures.
In this talk I will discuss one such practical implementation of Data Mesh based data platform for one of largest media producer broadcaster UK based client.
I will talk about how the social and technical aspects are implemented using domain driven boundaries within the organisation and on data platforms.
Data Mesh philosophy talks about a core platform engineering team, which is responsible for building capabilities and service the data product teams who will use these capabilities to realise their value use cases/ E2E journeys.
In this client engagement, we created these capabilities teams, defined and onboarded domains, and on boarded the data products belonging to the domain. The anatomy of the data product makes multiple different aspects around data to be handled very efficiently, like privacy by design, compliance etc. which were also part of our delivery. I will showcase the design of these core capabilities, the domains and the data products along with architecture and code snippets.
I will talk about the current state of the platform and how it will evolve in future with Data Mesh principles.
Data driven Pricing in Retail
This case study talks about the Pricing solution for a giant European retailer with the goal of revolutionising the pricing. Its vision is to provide the right price at the right time and the right place to the right customer.
We partnered on enabling the retailer to price more than 40,000 articles; for 25 countries to build better customer price perception, engage more customers and generate uplift on margin and revenue.We also focused on price discrimination strategies guided by customer centricity and zonal difference.
This retailer, by virtue of its size and geographic spread, collects huge amount of sales and customer data in all countries it is present in. However, with the heterogeneity of pricing processes across countries, there is significant variance in quality (vs. quantity) of data. Combined with its business models of both B2B and B2C, and the presence of established competitors in every country, made Pricing a very interesting challenge.
The vast assortment of retailers necessitates employing multiple pricing strategies depending upon certain attributes of the products. The pricing solution was aimed at providing a uniform yet flexible way which would enable the retailer to do price optimization and provide a seamless and transparent way to price their products with confidence across geography, product assortment and model of operations. This talk will explore the pricing landscape which is an integration of 3 domains viz. Retail, Data Science & Economics. We will highlight the type of models in each domain relevant to pricing & dwell deeper into some of the important ones which will have maximum impact on a company's pricing strategy. I will discuss the typical data issues faced, data based identification of various product groups, such as KVIs (Key Value Items), the type of models used - machine learning & statistical models, inferences, Key Performance Indicators & processes involved in the validation of the solution. We were also able to show the reason and impact of the prices recommended by the algorithm which differentiated it from competitors in the market.
I will walk through how the solution evolved on tech stack for the data pipelines to accommodate multiple evolving models, as well to scale up (more products) and scale out ( multiple geographies).
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