Ani: My Data Analyst Career Journey

Hi Ani, please introduce yourself to our site’s readers.

Hello, I'm Anirudh (Ani) Kuthiala. My career spans 18 years, beginning with a hands-on tech support role as a frontline agent. Those formative four years were not just about troubleshooting but also about laying the groundwork for problem-solving, developing resilience, and discovering the profound narratives that data can unveil—a revelation that propelled me into the analytics field.

Over the past 14 years, I've delved into the world of analytics, leading and nurturing teams across India, Poland, and the U.S., and collaborating with business stakeholders from over 25 countries. My expertise cuts across the dynamics of two-sided marketplaces, business operations, and contact centers, reaching into the strategic depths of product development, sales operations, and marketing.

From being a self-taught analyst to advancing to Director of Product Analytics, I’ve established a center of excellence for analytics and led a team of up to 10 analysts, transforming data into compelling narratives that drive strategic business growth for both B2B and B2C sectors.

In my current role, I am the founder of Framework Garage Consulting, where my passion lies in elevating analytical maturity for my clients and coaching analysts. It's about fostering a culture where data is not just observed but acted upon, and where every challenge is approached with a framework that simplifies complexity into actionable clarity. I’m here to share that journey, to coach and empower analytics professionals to leverage data for strategic decision-making, and to help them advance their careers as I have.

If I understood correctly, you started your career as a frontline agent, and from there you grew your career into the director for analytics role. Can you share with us what prompted you for the change, and how did you manage to navigate into your first role?

Absolutely, my journey in the tech industry began 18 years ago as a frontline technical support agent, where I was troubleshooting desktop and notebook issues for customers over the phone. Our work was evaluated by productivity, resolution rates, and customer satisfaction metrics. This sparked my curiosity about the data behind these metrics—its origin, my performance trends, and ways to improve.

I delved into the reports provided by my manager, maintaining my performance metrics in Excel, which helped me ask very specific questions about how I could improve. That line of thinking also steered me toward developing critical thinking early on. Interestingly, I didn’t set out with the goal of developing critical thinking; it was my curiosity and the comfort of asking questions that led me there. I soon discovered that many questions I had for my manager could be answered with data, and I could find those answers myself. This was a small yet significant realization for me. I started thinking in terms of what data I would need to answer a question. A skill that helped me the most while collaborating with Engineers later on when I defined what data should be captured to build the insight I needed for my stakeholders. 

Although promotions came my way and the typical next step was a technical supervisor, I craved challenges that would take me beyond conventional paths. The data realm seemed filled with such opportunities, and I was determined to be a part of it. In 2009, I began to upskill, mastering Excel formulas and pivot tables, and applied for analyst roles internally. Despite not clearing the first technical round for a much-desired position, I accepted another position laterally that many avoided due to its repetitive nature and long hours. It involved half-time reporting and half-time reviewing feedback from dissatisfied customers. This role wasn't glamorous, but it was rich with learning opportunities, which I valued above all else.

During this time at Dell, a simple yet powerful message from a Michael Dell poster resonated with me: 'Do what is right for the customer.' It took two years for its full meaning to sink in, but it eventually became the cornerstone of my professional ethos. As I transitioned from an analyst to a leader in analytics, that customer-centric approach remained my guiding principle. I focused on enhancing the decision-making capabilities of my stakeholders, which became my 'north star.' In turn, credibility, visibility, referrals, career advancement, backlog prioritization, and the ability to quantify impact naturally aligned with this focus. I learned that by concentrating on the larger purpose, all other aspects of success would follow—and indeed, they did for me. I’ve instilled that approach in the people I’ve led over the years.

What is it that you're personally finding most exciting about being in data analytics?

What excites me the most about being in data analytics is the immense potential it holds for transforming business operations and fostering significant growth. As an analyst, there's a unique power at your fingertips—the power to transform raw data into strategic business decisions. This role is pivotal because it sits at the confluence of information and action. Analysts are the custodians of 'information gold,' uniquely equipped to decipher data patterns, prescribe recommendations for improvement, surface actionable insights, identify the root causes of issues, and even predict future trends. Our ability to not just understand but also articulate the narrative hidden within the data can lead to pivotal changes in an organization’s direction.

However, my enthusiasm is often tempered by a reality that sees data frequently underutilized, confined to its most basic form—descriptive analytics. It's akin to having a sports car and never shifting out of first gear. Data’s true power extends far beyond simple reports and dashboards; it's the starting line, not the finish. By advancing beyond the first stage of analytics maturity, we unlock potential that can redefine what's possible. That’s the value proposition I've delivered to my stakeholders and now offer to my clients through Framework Garage Consulting.

The true worth of data doesn't lie in its storage, access, model accuracy, or even its presentation; it lies in its ability to inform decisions and propel business growth. It's the increasing impact of data on decisions that continues to excite me.

Looking ahead, the landscape of analytics is being reshaped by the remarkable advances in AI technology. The leaps in generative AI, large language models, and sophisticated machine learning algorithms—coupled with ever-increasing computing power—are democratizing capabilities that were once the exclusive domain of specialists. These tools are becoming more accessible, often free, placing powerful analytics within reach of anyone curious enough to explore them. This phase of democratization is indeed promising, and I eagerly anticipate being part of this exhilarating journey.

What would you say are the must have data analyst skills to thrive in today’s environment? How did you start improving your skillset as a data analyst?

On the technical front, proficiency in Excel, SQL, Python, or R, and a data visualization tool like Tableau or Power BI are non-negotiable. However, understanding the data journey is just as vital—knowing where data comes from, the touchpoints it has, and the transformations it undergoes before it reaches you. Familiarity with the ETL/ELT process and the tools provided by tech giants like Microsoft, Google, and Amazon will significantly enhance your collaboration with Data Engineers, Data Scientists, Software Engineers, and Data Governors.

• Yet, technical prowess constitutes only about 30-40% of what makes a good data analyst. From my 14 years of experience, I’ve observed that the most impactful analysts are those who delve beyond the numbers; they exhibit a profound curiosity about the 'why,' 'what,' and 'how' of data. This curiosity paves the way to learning, critical thinking, and ultimately, the ability to connect one’s work to the company's overarching goals.
  

• Being proactive is another trait I've championed with my teams. It’s about taking the initiative and approaching work with a mindset of contributing ideas rather than merely executing tasks. This approach has served me well, from my days as a support agent to leading analytics teams. It involves being the person who doesn’t wait to be asked but instead proposes solutions and strategies that improve the organization.
  

• Business acumen is another indispensable skill. It involves understanding the ins and outs of your company, the challenges different business units face, and what drives decision-making. Knowing which KPIs are tracked by leaders at all levels is critical. With this knowledge, you can align your analytical work with business objectives, making your contributions more valuable and enabling you to push back with data on requests and ideas that don’t contribute towards a bigger picture. I embedded myself into business reviews, and took notes on the challenges my stakeholders were working to address, how the business was performing, what the OKRs were, and what the goals were from the top down. It greatly aided my ideation process and once I started asking how the work I was doing connected to the bigger picture, I could position myself as a strategic partner and solve problems together.
  

• Perseverance is key, as working with data often involves a trial-and-error process. My time in technical support taught me resilience, a skill that was invaluable when dealing with complex SQL queries or any analytical task. Each failure is an opportunity to learn and refine your approach. I developed a framework early on to help myself learn from mistakes and recently blogged about it; I named it the LEARN Framework. Mistakes will always be made; understanding the root cause of these mistakes is crucial so we can act to address them. It’s been my go-to framework for solving challenges that restrict the effectiveness of my team.
  

• Maintaining a growth mindset is essential, as the landscape of data and technology is ever-evolving. We are on the cusp of fully realizing the potential of cognitive analytics, and no one is an expert yet. It’s about staying curious and being willing to learn and I highly recommend using ChatGPT for learning. Reading Carol Dweck’s 'Mindset' introduced me to this term. I practiced it in bits and pieces but couldn’t articulate it as a trait until the book helped me develop a refreshed perspective on ‘trying again’. I got comfortable with failing because the more mistakes I made, the more I learned, and was quick to bounce back.
  

• Simplification is my guiding principle—whether it's breaking down complex problems into manageable parts, writing clear and concise code, or communicating findings in a straightforward manner. Simplifying not only makes your work more comprehensible but also more impactful. Reading Edward De Bono’s book 'Simplicity' has been immensely helpful in my career. Making something complex is simple, but simplifying something is complex. I once used a picture of two bulbs, both illuminated, but one with tangled wires and the other without. Both were lit, and my message was that both solutions are working, but if bulb 1 isn’t working, finding the root cause will take time compared to bulb 2. I was amazed at the reception of that analogy because my business unit leader understood why I needed a few weeks to solve a problem and why it was necessary to go back to the drawing board and build simpler pipelines so this time investment would yield benefits for years to come and it did for the next 9 years. It could be a process, a pipeline, a dashboard's UX, or a language. If it’s simple, it’s easy to sustain, explain, understand, transition, or scale.
  

• Lastly, never underestimate the power of communication. An analyst must be able to clearly articulate their findings, ask questions, and convey their ideas. Strong communication skills are indispensable for networking and collaboration, and they empower you to share your work without relying on others to do it for you. Early on, I was always talking to my stakeholders, brainstorming, and probing, and when I developed solutions, I started demoing dashboards and other analytical products. I slowly transitioned to using analogies to explain my point of view or the merit of an approach. It helped me practice communication. I often represented analytics in all formats of meetings and town halls. The objective should be to get comfortable articulating the problem, your ideas, your work, your thought process, and your questions.

These are the skills and traits that have not only shaped my career but are also what I impart to the teams I lead and now serve as my value proposition to my coaching clients at Framework Garage Consulting.

Speaking of growing a career - what would you say analysts who want to grow into leadership roles, must know and do to stand out and rise through the ranks?

To grow into a leadership role from an analyst position, it's crucial to not only articulate the impact of your work but also to understand and demonstrate the competencies that leadership entails. This goes beyond listing tasks on your resume; it's about showcasing how your analytical insights have driven business decisions and growth. Learn about various leadership styles, situational leadership, conflict management, project management, and Agile methodologies. Enough to understand the merits and disadvantages. A conversation around these areas is your toolkit for leadership consideration. 

Testimonials are incredibly valuable. Proactively seek LinkedIn recommendations, especially from departing colleagues, to bolster your professional network and credibility.

Advocating for yourself is essential. Be direct in communicating your needs and career aspirations. Inquire about the milestones needed for a leadership role and integrate this information with SMART goals to track your progress. This clarity and self-advocacy can significantly influence your career path. Never assume someone will identify your good work, and promote you. Take charge of your career and if you don’t ask, the answer is always going to be a no. 3 times in my career, I directly asked my leader - “Over the last year, my work was effective in creating ABC impact, can I depend on your endorsement to be promoted in the next cycle?” If they said no, I could probe to get very specific feedback on what I should be working to improve as it wasn’t performance. A simple yet powerful approach. 

As you build relationships within your organization, establish yourself as a strategic thinker and problem-solver. When stakeholders seek your input for strategic decisions, not just data, it's a sign you're on the right path to leadership.

However, remember that being an exceptional analyst does not automatically qualify you for leadership. The transition to a leadership role involves acquiring a different skill set—strategic thinking, people management, and a broader business understanding. Demonstrating these skills, such as your business acumen, your proposals for solving problems, and the tangible impact of your work, is crucial. These experiences illustrate your leadership potential, not just your analytical expertise. If your pitch is about how excellent you are as an analyst, then you are walking into a conversation about growing as an analyst. Showcase your readiness for the next role. 

Leadership is about influence, impact, and decision-making. It’s about being in charge of your team, their careers, personal growth, and development as much as professional achievements. Crafting a narrative that convincingly showcases your readiness for leadership is key to setting yourself up for success.

Since you started your own Analytics Consulting and Coaching business, can you share what prompted you to start the business, what does your typical project engagement look like?

The inception of Framework Garage Consulting was inspired by a strong desire to channel my years of experience into a venture that could empower others. It all began with a newsletter and a blog I started in June this year, which quickly grew to 500 subscribers. The response was overwhelmingly positive, with email open rates averaging 51% and click-through rates at 6%—figures significantly above industry averages. These metrics not only validated the relevance of my content but also reinforced my confidence in the value I could bring to my audience.

When my role was impacted by offshoring and downsizing in mid-2023, I found myself at a crossroads. The opportunity to embark on a new path presented itself, and I decided to seize it. I turned to the concept of Ikigai to guide my portfolio of services. Reflecting on what I love, what the world needs, what I can be paid for, and what I am good at, I was able to articulate a clear service offering, commitment to clients, and business process, as detailed here. I've always told my team members, 'Once in my team, always in my team,' and I've been informally coaching 80% of my ex-team members over the past three years. Their journeys—spanning various industries and paths, some becoming leaders, others advancing as individual contributors—are incredibly rewarding and fulfilling. This, coupled with my determination to address a gap in the analytics community by nurturing thinking skills rather than just technical skills, led to strategy consulting and career coaching.

Now, as I navigate this career shift, I'm embracing the roles of consultant, coach, marketer, salesperson, administrator, and content creator. It's a multifaceted learning experience, and I'm cognizant that, regardless of the outcome, the learnings will be invaluable—as they have always been.

Currently, my consulting engagements primarily originate from within my network and those I've previously worked. As I concentrate on helping organizations of all sizes enhance their analytical maturity, I am in the process of creating a lead magnet that will enable me to tailor my services more effectively to clients, both inside and outside my existing network.

For those in search of coaching, anyone in the data field aiming to develop their non-tech skills and progress in their career is encouraged to reach out directly at ani@frameworkgarage.com or contact me here. My objective is to understand your current professional standing and aspirations, and then work together to create a customized roadmap to bridge that gap.

And when coaching the next generation of data analysts, what are some of the key things you focus on?

My coaching is designed to cultivate an analytical mindset that thrives on problem-solving, critical thinking, and strategic planning. I emphasize the importance of fostering curiosity, as it is the bedrock of continuous learning and innovation. Perseverance and a growth mindset are also pivotal, enabling analysts to navigate challenges and adapt to the evolving data landscape.

I will introduce my coaching clients to the concept of 'framework development' to instill structured problem-solving approaches. It's not just about being data-driven; it’s about being data-informed, integrating data insights with business context to make balanced decisions. This semantic nuance, while subtle, is crucial for making decisions that are both data-centric and strategically aligned with broader business goals.

As part of their progression, I guide analysts on their journey into leadership roles. This includes understanding the various stages of analytical maturity and how to be effective at each stage, from descriptive to prescriptive analytics. I also cover designing recruiting strategies, and stakeholder/client relationship management, which is key to transitioning from an individual contributor to a leader.

My approach is inspired by everyday experiences and the tools we use to navigate them. For instance, the simplicity and repeatability of using a stencil in school led me to develop 'thinking stencils'—frameworks that helped me approach and deconstruct problems methodically. By breaking down a problem into smaller, manageable components, whether it’s crafting an SQL query or tying an analytical insight to a business outcome, we can tackle each element with precision. I’ve blogged about it here

At Framework Garage Consulting, I impart these methodologies and coach analysts in applying them to their work. My aim is to help them build a playbook and a toolkit that can be applied throughout their career, enhancing their problem-solving capabilities and laying out a blueprint for analytical excellence.

Ultimately, my coaching is not just about imparting knowledge; it's about instilling a way of thinking that transforms analysts to increase effectiveness and build a rewarding and fulfilling career. It's about nurturing the skills and traits that have marked my career, and my team’s and can shape theirs, too.

Something that a lot of people are wondering and asking about - What recommendations would you give to someone who is looking to join the data industry and get their first full-time data analyst position?

Entering the data industry requires a strategic approach, especially for your first data analyst position. Start by researching the market demands. An effective tactic is to analyze at least 100 job postings for the position you're aiming for, as well as roles one or two levels higher. Create a keyword map from these listings to pinpoint the skills in high demand, not only for entry-level roles but for future advancement as well. This approach will help you focus your learning on skills that are not only relevant now but will also support your career progression.

It's essential to learn the foundations that are always in demand: SQL, Python, or R for data analysis, Excel for data manipulation, and a data visualization tool like Tableau or Power BI. These skills are the bedrock of data analytics and will serve you well throughout your career.

For those transitioning from another field, in addition to the tech skills foundations, cultivating a growth mindset is essential. The data world is filled with buzzwords that might seem overwhelming, but it's important not to be deterred by them. A growth mindset will enable you to overcome the first barrier - tech skills. You should then focus on comprehending the problems that need to be solved, their impact, and the methodologies to address them. Strive to become proficient in at least the descriptive and diagnostic stages of analytical maturity, and ensure you can confidently explain 'What happened?' and 'Why did it happen?' within a data context.

Learn about the Key Performance Indicators (KPIs) that are used to measure and track business performance. Engage in creating hypothetical scenarios about spikes or drops in KPIs and then systematically hypothesize what could have led to such changes. Consider the factors at play—this exercise will test your business acumen, and logical reasoning, and will help you prepare a list of necessary data points for investigation. This cycle is highly in demand; if you’re comfortable navigating through it, you possess the critical skills needed to build a successful career in analytics.

Don't be intimidated by terms like machine learning, artificial intelligence, predictive analytics, or feature engineering. Six years ago, I might have been daunted by these concepts, but a growth mindset propelled me to embrace them. Utilize tools like ChatGPT to learn and practice prompt engineering, which will not only improve your understanding of these terms but also ensure you stay abreast of what's cutting-edge in the industry.

By focusing on these strategies, you'll build a strong foundation that will not just kickstart your career in data analytics but will also prepare you for continued growth and success in the field.

Anything you'd like to highlight, or add? Something that not specified above but you hear a lot? Or would be helpful for people to know?

One key aspect I'd like to emphasize is the importance of focusing on the outcomes of your work rather than just the outputs. The true value lies in the impact of what you produce, whether that's a report, dashboard, presentation, or model. This impact doesn't always translate to financial terms; it can also be seen in the movement of a KPI. Concentrating on outcomes helps clarify priorities for you, your manager, and your stakeholders or clients. It simplifies what needs attention and what can be set aside, effectively reducing backlog and allowing you to allocate time to proactive work. This focus also enhances your ability to estimate the real-world impact of your analyses.

In job descriptions for analytics leadership roles, from Director to entry-level management, I've noticed an increasing demand for data engineering skills. While data engineering and data analytics often exist as distinct functions within organizations, a comprehensive understanding of the data journey, including ETL/ELT processes, can bridge the gap between the two. This knowledge empowers you to quickly adapt to and learn the necessary tools should the need arise.

Beyond the technical aspects, building your personal brand is paramount. And I'm not just talking about social media presence. Cultivate a reputation within your workplace as a problem-solver, someone who delivers solutions, not just data. You want to be recognized as the go-to person for overcoming challenges, not merely as someone who authors dashboards.

An extra one: How do you see the increased availability of AI tools such as ChatGPT, Bard etc, impacting a data analyst's career?

The advent of AI tools like ChatGPT, Bard, Claude, and Perplexity is revolutionizing the role of data analysts. Those who don't consider integrating these tools into their workflow risk falling behind in the job market in the next 2 years. It's not merely a matter of increasing productivity; it's about augmenting the value you provide as an analyst. While there's a lot of discourse around the impact of AI on jobs in data, the key takeaway is that AI itself isn't the threat—it's the people who effectively use AI that are setting the new standards. It's essential to embrace AI and learn to leverage these tools to augment your work, ensuring that you remain competitive in the field.

Generative AI has advanced tremendously, encroaching on areas traditionally dominated by humans, such as coding, presenting, and dashboard authoring. Take Tableau GPT, for instance—it can summarize a dashboard and engage in follow-up questions, tasks that currently form a substantial part of an analyst's role. Microsoft co-pilot is already positioned to increase self-service. The true value of a dashboard, after all, is in enabling decisions through the questions it addresses. If AI can fulfill or even replace this role, the implications are indeed significant.

I'd like to share a personal experience. Despite leading analytics teams for nine years, I've never been proficient in Python. Recently, I decided to test ChatGPT's capabilities on a problem where the results were already known to me. My team developed a detractor prediction model two years ago. The goal was to identify potential detractors (dissatisfied B2B partners) so that our sales team could proactively engage with retention strategies, ultimately aiming to reduce partner attrition.

Two months ago, using the free version of ChatGPT, I created a fully functional Python code and built a model by myself.

ChatGPT generated Python code step by step, chose the appropriate predictive model, and explained the model's purpose and its effectiveness. The code generated was sophisticated enough to recommend and run multilinear regression, classification, XGBoost, and Prophet. Remarkably, in just two hours, I achieved a 55% accuracy rate on my first try with a dummy dataset! More importantly, I had an explanation generated for each block of code so if I had to explain it to someone, I knew exactly what was happening at each stage. 

Two observations I had from this exercise: 1. Tech skills (Python) weren’t a blocker to build a model and eventually an analytical product and 2. It’s my critical thinking, prompt engineering, and reiterating approach that led me to an idea to build a solution and then improve it. This strengthened my belief that tech skills comprise only a fraction of an effective analyst. 

This experience also highlights a pivotal reality: if someone with limited coding expertise can construct an analytical model, we are entering an era where AI solutions are readily available, cost-effective, and potentially more efficient than traditional methods. Thus, embracing AI tools is imperative to remain competitive. 

As generative AI begins to take on roles traditionally filled by humans, such as coding and dashboard creation, there will be a rising need for analysts who can govern these AI solutions and ensure they are effectively generating insights. With this shift, the demand for thinking trilogy (critical, structured, and strategic) skills will increase. The competencies I've continually advocated for—curiosity, business acumen, communication, and the ability to simplify complex concepts—are becoming increasingly vital. It's these skills that will help you safeguard your career in an AI-augmented landscape.

Thank you so much Ani, how can people reach you if they wanted to chat?

You can reach out to Ani Kuthiala here

Framework Garage Consulting

Ani Kuthiala on LinkedIn