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Manish Agarwal, Vice President, Data and Analytics, Skechers

Describe your career to date

Over two decades, I have spearheaded data and analytics initiatives, demonstrating leadership across diverse industries. My career began at Nortel Networks, where I pioneered one of the earliest implementations of Database-as-a-Service (DBaaS), setting the stage for future advancements in cloud technologies. This innovative approach continued at Deloitte, where I transitioned database-driven client-server applications to the SaaS model, and at Myspace, where I led the development of a petabyte-scale data warehouse and analytics system. 

At Oracle, I led CIO advisory services, focusing on Cloud, Big Data, Data Warehouse and Business Intelligence (BI), guiding Fortune 100 companies to effectively meld technology with strategic business needs. Then, at MGM, I created the company’s first innovation lab and drove digital innovation and agile data delivery strategies. I also pioneered the rollout of AI-driven in-room delivery robots and enhanced insights delivery, BI reporting, and data quality. 

At Hertz, as the first global Vice President of Data and Analytics, I led a critical migration from on-premises to the AWS cloud, establishing a state-of-the-art data and analytics platform. This transformation included processing IoT data and developing a machine learning-based recommendation engine, which significantly enhanced our analytical capabilities and customer experience. 

Most recently, as Vice President of Data and Analytics at Skechers, I have been responsible for establishing and leading the Data and Analytics department, spearheading an enterprise-wide transformation that strategically aligns data processes and deploys advanced analytics, including BI reporting and a Customer Data Platform (CDP). This effort has enhanced decision-making and operational efficiency while improving customer engagement and personalizing marketing efforts. 

My career underscores a commitment to innovation and leadership, with a proven track record in leveraging big data, artificial intelligence (AI) and machine learning, and cloud computing to foster business growth.  

How are you developing the data literacy of your organization, including the skills of your data teams and of your business stakeholders?   

At Skechers, I champion the advancement of data literacy through a design thinking approach, focusing on improving user experience and fostering a culture of continuous education and innovation across business functions. Our initiative enhances the technical skills of our data professionals and the analytical capabilities of our business stakeholders. 

We are actively training our internal teams to standardize the look and feel of data products, enhancing storytelling and effectively communicating business value through data. Each line of business is partnered with a dedicated Data Product Manager, who is becoming a subject matter expert in the specific business function, providing tailored support and ensuring effective communication. 

To ensure quality and transparency in our data reporting, we are working towards comprehensive documentation for each dashboard and plan to incorporate detailed release notes that define metrics with data lineage, business glossary, and data cataloging. We regularly conduct training sessions with business users to educate them on effective dashboard usage, thus enhancing decision-making and operational efficiency. 

We have also enhanced collaboration within the data and analytics pillars to facilitate knowledge sharing and are actively engaging with teams to standardize key metrics and definitions. Our close partnerships with business units help streamline, automate, and standardize reports and dashboards, actively driving a data-driven culture. 

We plan to conduct more brown bag sessions and organize roadshows to further our objectives and improve data literacy, ensuring our teams are well-equipped to leverage data for strategic decision-making and innovation. 

What role do you play in building and delivering conventional artificial intelligence solutions, including machine learning models? Are you also involved in your organization’s adoption of generative AI?  

In my role as Vice President of Data and Analytics, I am committed to driving impactful artificial intelligence and machine learning initiatives that align closely with our business goals. Over the past few years, our data science team has been engaged in exploring and testing various models to identify those that offer the most effective solutions.  

Recognizing the importance of accelerating our AI and machine learning applications to translate these efforts into tangible business value, we have initiated a strategic shift towards operationalizing our AI strategies. 

This year, we have placed a strong emphasis on enhancing our infrastructure by implementing a robust AI and machine learning platform designed to streamline our processes and focus on delivering end-results that provide measurable impact and value. We are currently in the final stages of configuring this platform and anticipate launching it shortly. Upon completion, our team will begin tackling three specifically identified use cases that promise to effectively leverage our data and analytics capabilities. 

Additionally, I have taken proactive steps in exploring the potential of generative AI (genAI) technologies to transform our operations. These efforts are designed to ensure that our genAI applications not only meet stringent security and efficiency standards but also drive innovation within our business model. 

How do you ensure high standards of data quality and maintain them as you scale data systems and processes in your rapidly growing organization?

Ensuring high data quality is foundational to our operations at Skechers, as it directly impacts the integrity of business decisions. Recognizing the principle of garbage in, garbage out, my approach is centered around establishing and maintaining impeccable data standards to avoid misinformation and inefficiencies. 

One of our key strategies has been to build a single source of truth. This centralized data repository ensures that everyone in the organization accesses and utilizes the same set of data, which significantly enhances consistency across all business units. To ensure data accuracy, availability, and completeness, we have implemented robust data observability software. This tool performs nearly 3,500 data quality checks to identify and address any anomalies related to data delays, volume discrepancies, schema changes, and data drift. This system employs AI-powered anomaly detection, real-time incident alerts, and remediation workflows, enabling us to proactively catch and resolve data issues. 

Data quality is an ongoing journey. We are constantly learning from the data and refining our processes. As part of this, we are developing a data quality dashboard that provides a unified view of our end-to-end data and analytics platform, with a particular focus on data quality metrics. This dashboard is instrumental in standardizing definitions and ensuring data cleanliness right from the source, which is crucial for maintaining the integrity of our datasets. 

Furthermore, maintaining high data quality is essential for building trust with our business users. It not only ensures reliability in our data-driven decisions but also facilitates broader adoption of our data products across the company. By prioritizing data quality, we not only safeguard our operational standards but also reinforce our commitment to delivering reliable, actionable insights that drive business growth and innovation. 

Manish Agarwal
has been included in:
  • 100 Brands 2024 (USA)
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