Vector was ushered down the carpet with Mona Patel, a tech entrepreneur who even used kinetic butterfly sculptures previously as an interesting homage to fashion and tech’s joint slay. In 2024, the Met partnered with OpenAI to create a custom chatbot associated with the final garment in its exhibition ‘Sleeping Beauties: Reawakening Fashion’. Built on decades of data, the chatbot took on the persona of New York socialite Natalie Potter, who once wore the gown from the 1930s, and answered visitors’ questions.

Looking good is serious business. And with the beauty and fashion industries continuing to generate incredible demand, AI-powered interventions can help brands put their best foot forward and make data-driven decisions across an industry driven by competitive margins.

At Tiger Analytics, we’ve been working with fashion and beauty brands to give their processes the right kind of AI-driven optimization makeovers, build powerful, meaningful connections with their customers through well-thought-out and bespoke customer journeys, and deliver a perfect match between brands and customers.

How is AI helping plug the gaps of the fashion and beauty industries? Our latest edition of AI of the Tiger talks about this and more.

Victoria’s Secret? A comprehensive #processglowup for better compute costs and reduced runtime

10 months, 600+ scripts, and 40+ modules migrated from legacy on-prem systems to Azure and Snowflake. We successfully executed a comprehensive migration strategy with Victoria’s Secret & Co (VS&Co) that served: 34% lower compute costs and 37% reduced runtime. In fashion, it’s the details that make the difference. With data, it’s the architecture. Our head-to-toe process makeover included effective change management, upskilling, and stakeholder engagement with continuous performance monitoring, cost management, and optimization to maintain efficiency and keep costs in check in the new cloud environment. That’s a slay.

Hey, big spender? How propensity models are helping brands rope in the 2 percenters

Nearly 40% of all luxury goods are purchased by the top 2%, a study found. What if luxe brands could identify these A-listers and build meaningful relationships and lasting loyalty? We helped our client, a global retailer, do just that, doubling the average revenue per customer. Stitching together data from multiple touchpoints along with customer segmentation techniques can help brands build propensity models to predict future Very Important Clients (VICs) and unlock smarter cross-sell and upsell opportunities.

It’s giving – results! Mapping customer-product chemistry to deliver a perfect match

Today, the real question isn’t just ‘what does the customer want?’ – it’s ‘how can we truly understand and deliver it?’

• 4.5% rise in repeat purchases
• 7.8% increase in app usage
• 23-basis-point increase in marketing ROI across digital channels

These are the results that a global beauty brand observed by rethinking how it connects customers with products. Customers don’t always explicitly state their preferences. Their choices are shaped by trends, context, and discovery. Customer product affinity embedding brings brands closer to the customer by placing the consumer at the heart of every decision. With data-driven customer understanding, brands can build deeper and more personalized connections, driving loyalty and growth. Read how we powered this.

Safety, Pinned: Winning customer trust by building the right foundation

When data becomes currency, how can we safeguard data privacy and exclusivity? Luxe brands have to tread delicately, balancing the rising expectations of hyper-personalized service and a clientele that values discretion while maintaining long-term customer trust. When data governance principles around privacy and consent are built into the systems we architect, they allow brands to confidently and ethically deliver superior and memorable customer experiences. Here’s our POV.

AI is reshaping how fashion and beauty brands connect with customers, but the real challenge is balancing innovation with responsibility. How is your organization weaving AI and data governance into its fashion and beauty story?

This edition was originally published on LinkedIn on May 31, 2025.

The post Code and Couture: How Fashion and Beauty are Using AI to Stay Ahead of the Curve appeared first on Tiger Analytics.

The 438 chase of 2006, South Africa vs Australia 5th ODI

The 352 chase, Australia vs England, 2025 ICC Champions Trophy

The 322 chase, Sri Lanka vs India, 2017 ICC Champions Trophy

Target? Chased.

Any cricket team captain and fan will tell you that chasing big totals is all about striking the right balance of strategy, persistence, and adaptability. Successful chases begin with a solid foundation once the first ball is bowled. From a solid opening partnership to set the target in motion to calculated risk-taking during crunch time. From adapting to the on-ground situation while managing pressure, to bouncing back even after early losses. What can technologists learn from the best run chases in the game?

In this edition of AI of the Tiger, we take a leaf from the chase master’s playbook and explore how data technologists can evolve into all-rounders. We’ve compiled a special edition to all aspiring run-chasers to help you S.C.O.R.E.

S – Streamlining projects with Agile: Project Management mindset for data scientists and engineers

How can project management methodologies like Agile help drive better delivery outcomes? The Agile methodology, at its heart, is all about breaking projects down into manageable pieces for faster, more efficient delivery. This data engineering approach prioritizes delivering one component at a time with a focus on value-driven development, greater adaptability, and continuous feedback. Each component is tested and reviewed to ensure it meets expectations before we move on to the next one. By adopting the agile mindset, along with our Tiger Gene framework, we’re able to streamline the engineering process, keep projects on track, and adapt quickly to evolving needs – here’s how.

C – Coding assistants: Powering up your development game with the right partners

Can AI-powered tools become a technologist’s best friend? AI-powered tools are reshaping development processes, but the real innovation comes when data engineers combine their expertise with these technologies. Tools like GitHub Copilot assist by generating code suggestions, explaining complex code, and automating tasks like refactoring SQL queries or Python scripts, making the development process faster and more efficient. This collaboration is helping engineers accomplish tasks faster and with greater precision. However, even as AI shares some of the workload, human oversight remains a cornerstone of the process. While AI can optimize workflows, it’s the data engineer’s expertise that ensures the solutions are accurate, secure, and aligned with business needs. Our blog explores the impact of AI-powered coding assistants and why human judgment continues to play a pivotal role.

O – Owning the future – Evolving into the data engineer of tomorrow with a strong base of technical skills

Continuous learning is an integral part of a data engineer’s career. As organizations increasingly rely on scalable and flexible data environments, expertise in cloud-native platforms like AWS, GCP, and Azure has become essential. By mastering tools like AWS Glue and Spark, engineers can build real-time, scalable data pipelines, ensuring seamless integration across platforms. We explore setting up data lakehouses across AWS, Azure, GCP, and Snowflake.

R – Responsible AI at heart: Doing our part by helping build fair and transparent systems

While everyone – from data scientists to business leaders – relies on accurate data for AI models, it’s data engineers who ensure the data is trustworthy and well-prepared. By maintaining data integrity throughout the pipeline, engineers lay the foundation for AI systems that are transparent, unbiased, and ready for use. The five principles of Responsible AI — interpretability, fairness, user safety, human-centered design, and privacy — serve as a guide at every stage and encourage clear accountability across all team members. In this article, we explore how, with a commitment to responsible practices, we can build AI systems that stay on track.

E – Engineering for business outcomes: Why domain expertise is a necessity

A great data engineer understands the bigger picture, knowing how and why their solutions will drive business value. Domain expertise bridges the gap between industry knowledge and technical skill, providing the crucial context needed to tailor solutions to specific business needs. In the fintech sector, for instance, addressing data inconsistencies, such as merchant identification errors, requires both deep industry insight and technical proficiency. Here, domain expertise helped engineers understand the root causes of data issues, enabling them to leverage Snowpark’s local testing framework and Python to create and test DataFrames and stored procedures locally. The solution helped improve productivity while also ensuring an efficient data pipeline development. Domain context helps solve tech problems better across industries such as Victoria’s Secret & Co. in retail, Inspiro in outsourcing, insurance, and consumer services.

Much like chase experts on the field, data technologists can turn champions by embracing continuous learning, evolving methodologies, domain expertise, AI collaboration, and ethical responsibility for a perfect delivery.

This edition was originally published on LinkedIN on March 3, 2025.

The post Unlocking Your Powerplay: What Lessons Can Data Engineers Learn from the Chase Masters? appeared first on Tiger Analytics.