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Data Driven: Fueling Decisions with Analytics

Data Driven: Fueling Decisions with Analytics

Welcome to a new episode of The Zista Podcast, featuring an insightful conversation with Hari Saravanabhavan, an accomplished figure in the world of data analytics. Hari’s journey spans 25 years, starting with Contact Advertising and leading to his current role as Vice President of Global Analytics at Concentrix + Webhelp. His experience and insights have earned him industry recognition. Hari is part of the Forbes Technology Council and he has been listed as a top data science leader by Analytics Insights magazine.

In this episode, Hari shares his journey into data analytics and offers a clear understanding of the field. We discuss a range of topics, from the fundamentals of data analytics to the skills and certifications needed for a career in this rapidly evolving area. Hari’s expertise provides valuable perspectives for both newcomers and seasoned professionals in the industry.

Join us as we explore the impactful world of data analytics with Hari Saravanabhavan. This episode is a rich resource for anyone looking to grasp the practical applications of data analytics in business and technology, offering guidance and inspiration for those navigating this dynamic field.

Welcome to the latest episode of The Zista Podcast, focusing on the transformative role of data analytics in decision-making. Our guest, Hari Saravanabhavan, brings an impressive 25-year track record, starting at Contact Advertising and progressing through influential roles at Airtel, Genpact, IBM, and Cognizant. Now, as the Vice President of Global Analytics at Concentrix + Webhelp, Hari’s expertise continues to redefine the analytics landscape.

Recognized as a top influencer in the field, Hari is a member of the Forbes Technology Council and has been named one of the top 10 influential data science leaders of 2023 by Analytics Insights magazine. His deep understanding and passion for data science are not just inspiring but also pivotal in navigating today’s data-centric landscape.

In our conversation, we explore the journey that ignited Hari’s passion for data analytics and how he built a stellar career in this field. We then break down the concept of data analytics, making it accessible and understandable. Hari provides clarity on the various roles and functions within data analytics, offering listeners a comprehensive view of the industry.

Our conversation with Hari is a journey through the essentials of data analytics. We start by uncovering what sparked his interest in the field and how he embarked on his impressive career path. We simplify the complex concept of data analytics, making it more approachable and understandable for our listeners. Hari sheds light on the diverse roles and functions within data analytics, offering a panoramic view of this dynamic field.

We also discuss the critical process of pinpointing the right questions and challenges that can be effectively addressed with data analytics. This aspect is key for anyone aiming to leverage data in their professional life. Additionally, Hari talks about the nuances of implementing AI and data analytics solutions, providing insights into assessing their effectiveness and reliability.

For those aspiring to careers in data science, Hari’s advice on the necessary skills and certifications is invaluable, providing a roadmap for success in this evolving field. This episode is packed with wisdom and practical advice, making it a must-listen for anyone interested in understanding and utilizing data analytics to drive decisions and create an impact.

KEY TAKEAWAYS

  • Data analytics, despite its complexity, is essentially about using technology, science, and art to find meaningful solutions to problems.
  • The field offers a wide range of roles, including data management, analytics, presentation, domain expertise, and translation, all of which play essential parts in the data analysis process.
  • Effective data analytics involves identifying the right problem to solve, whether it’s improving existing processes, innovating, or undergoing a complete transformation.
  • To assess the effectiveness of data analytics and AI systems, methods like ‘champion challenger’, ‘out of time validation’, and ‘strategic use case evaluation’ can be employed.

QUESTIONS

Q1. How did data analytics capture your interest and propel your career in this dynamic field?

A: Hari’s journey into data analytics began about 25 years ago, rooted in his initial career in advertising and brand management. He was intrigued by the subjective nature of understanding customers in marketing. Faced with varied opinions about consumer profiles, he sought a more factual, empirical approach to understanding them, which led him to explore data analytics.

In addition to this, Hari’s role in managing a department in a large organization further fueled his interest. He realized the significant role of metrics and data in decision-making and saw an opportunity to enhance business strategies through data analytics. This shift from relying on personal knowledge and gut feeling to using data-driven insights became a key focus in his career.

Today, Hari’s work heavily involves marketing analytics, particularly in customer analytics and journey mapping. His experiences highlight the transformation from intuition-based to data-informed decisions in business. He emphasizes the value of data analytics in providing objective, reliable insights for strategic decision-making in various organizational contexts.

Q2. What does data analytics mean in simple terms?

A: Hari explains that data analytics doesn’t have one standard definition. It’s sometimes called big data analytics, business analytics, or data sciences. Despite its complex nature, in essence, data analytics is the combination of technology, science, and art to derive meaningful insights and outcomes for specific problems.

The complexity in data analytics arises from the different methods people use to apply technology, science, and art. At its core, it involves analyzing data to find the best technological, scientific, and artistic approaches for gaining insights or solutions to various challenges or opportunities.

A relatable example is online shopping, like on Amazon; when you search for a product, a lot of analytics work in the background. This process involves examining your browsing history and other data to provide you with the most relevant product recommendations. This recommendation engine exemplifies data analytics in action: processing and analyzing data to enhance customer experience. The ultimate goal of data analytics in this context is to ensure a top-notch customer experience by presenting the most appropriate recommendations.

Q3. What are the different roles and functions in the field of data analytics?

A: Hari highlights the ever-growing range of roles in data analytics, broadly categorizing them into several key skill sets. The first is data management, which includes roles such as data engineers and data analysts. This area, referred to as ‘upstream’, focuses on managing, housing, and processing data.

The second crucial skill set is analytics, where roles like data scientists and business analysts come into play. These professionals turn data into meaningful insights, a process crucial for understanding and utilizing data effectively.

Another important aspect is the presentation of data, involving roles like reporting analysts and visualization experts. These professionals, seen as ‘downstream’ in the analytics spectrum, make the insights accessible and understandable.

In addition to these, domain expertise is vital. This expertise varies based on the industry and specific problems being addressed, adding relevance and specific context to the data analysis.

Finally, translators play a crucial role across the entire spectrum. They simplify complex data and insights into forms that are easily understandable by people both within and outside the organization.

Hari emphasizes that these are the five primary roles in data analytics, though there could be additional nuances depending on the organization or specific use cases. These roles collectively cover the end-to-end process of data handling, analysis, and interpretation, making them essential in any organization focusing on data analytics.

Q4. How do you determine the right problem to solve with data analytics?

 

a: Hari explains that in data analytics, there are three key objectives to identify the right problem to solve: ‘run better’, ‘run different’, and ‘transformation’.

The ‘run better’ stage is about improving existing processes. It involves evaluating current operations to enhance efficiency and effectiveness, focusing on top and bottom line improvements. This is the foundational level where the goal is to outdo current methods.

The ‘run different’ stage goes beyond mere efficiency. It’s about finding novel ways to address problems or seize opportunities, thus changing the approach to tackling issues.

The ‘transformation’ stage is a complete shift in methodology. Hari uses the example of shifting from a traditional retail model, where consumers come to pick up items, to a delivery model. This kind of transformation requires a completely different set of data and analytics compared to just enhancing existing processes.

In each stage, problems are broken down into use cases. These cases are assessed for the availability of relevant data, the capability to analyze it, the computing power required for insights, and the execution of these insights. A well-defined, executed use case that demonstrates impact can then be scaled into a broader business strategy.

Use cases are further categorized based on impact and effort, like ‘high impact low effort’ or ‘low impact low effort’, aiding in prioritization. This methodology is well-established in the data analytics field, guiding organizations from specific use cases to comprehensive, overarching functions.

 

Q5. How do you assess if an AI or data analytics system’s recommendations are effective?

  1. Hari outlines several methods for evaluating the effectiveness of a data analytics or AI system. The primary method is the ‘champion challenger’ approach, where the current system is compared against a new or alternative system. This comparison, using test-and-control samples, helps determine which model is more effective.

Another method is ‘out of time validation’, where you apply the developed algorithms to a different time period to assess their performance. This checks if the model is robust and reliable across different contexts.

Hari emphasizes the role of use cases in this evaluation process. In data analytics, everything starts with a use case, which defines the problem or opportunity being addressed. He highlights that you’re not just looking at efficiency but also at effectiveness, meaning you explore different ways to tackle the problem or opportunity, and that becomes your use case. Depending on the stage of your journey, you define your use cases accordingly, whether it’s about doing things better, differently, or transforming the business.

The kind of data and analytics required for transformation can differ significantly from other objectives. For instance, the data needed for a retail transformation, moving from in-store to delivery models, is different from data used in enhancing in-store customer experiences.

Additionally, use cases can be positioned differently based on impact and effort, such as ‘high impact low effort’ or ‘low impact low effort’. This categorization aids in prioritizing them. Once a use case is well-defined and executed with a significant impact, it can evolve into a broader business strategy.

These methodologies — champion challenger, out of time validation, and strategic use case evaluation — together provide a comprehensive framework to assess the efficacy of AI and data analytics systems in addressing specific business challenges.

Q6. What skills and certifications are required for a career in data science or data analysis?

A: Hari outlines several key competency areas and skills essential for aspiring data scientists or data analysts:

Technology: A robust technology background is crucial. This includes skills in computer science, coding, data management, and data manipulation.

Techniques: This area covers the analytical aspect of the field, including predictive analytics, data science, advanced analytics, and model building. Typically, a background in statistics or economics is beneficial for this.

Business Intelligence and Reporting: This involves converting insights into formats that aid decision-making. It requires a blend of technical and analytical skills and the ability to present data compellingly.

Domain Expertise: Understanding specific industries, such as banking or supply chain management, is vital. This expertise ensures the relevancy and applicability of data analytics within a particular domain.

General Management Skills: Project management, effective communication, and stakeholder management are crucial. These skills help translate technical details for non-technical stakeholders and align projects with business objectives. Hari emphasizes the importance of this skill set, comparing it to client servicing in an agency.

Hari also notes that while there are specific tools and technologies to learn over time, the choice of specialization or broad skill acquisition depends on individual preferences. Some may choose to delve deeply into data science or AI engineering, while others might opt for a more versatile skill set across multiple areas.

In addition to these competencies, Hari mentions the evolving nature of the field, where professionals often strive to master more than one competency. This multidisciplinary approach, while challenging, can be beneficial in the dynamic data analytics and AI landscape. He emphasizes that understanding these competency areas and continually adapting to new tools and methodologies is key in building a successful career in data science or data analysis