Project vs Product

There’s a difference between project and product. A project has a defined start and end date - it is inherently tactical by nature due to its short lifespan. Multiple projects linked together based on a core design ethos become a strategic initiative, but nevertheless, there is a time-bound horizon. What is incredibly important for projects is to have defined outcomes which serve as the exit criteria for the engagement. Projects generally have a project timeline that details step-by-step the key milestones and tasks needed with a clearly defined critical path to achieving the outcome. Success of a project is often narrowly defined by the ‘iron triangle’: finishing on time, on budget, and delivering a predefined scope.

Product, on the other hand, has a start but doesn’t have an end date. If the product is successful, it only ends if the product has failed. Products are inherently strategic as they are honed in on the core, overarching problem faced by multiple potential customers. Product requires a carefully crafted roadmap that details what product capabilities are coming now, next, and later. A product roadmap serves as a guide to inform customers on the capabilities, and customers often work closely with product managers to influence the priorities. Success of a product, by contrast, is not about a fixed output. It is defined by business outcomes - adoption, usage, and profitability.

Product Managers

Being the CEO of a product requires a level of autonomy that is foreign to most people working in enterprises.

This isn’t an exaggeration. In a typical corporate structure, roles are siloed. You have “strategy” over here, “engineering” over there, and “sales” somewhere else, all held together by project managers who are simply trying to hit a deadline.

The Product Manager shatters this model. That autonomy is non-negotiable because their ownership is absolute. They aren’t just handed a list of requirements to build; they are responsible for discovering the problem itself. They own the “Why” before anyone else even thinks about the “What” or “How.”

This level of ownership means they are accountable for the entire lifecycle, from ideation to sunset. They own the P&L, not just a budget. They own the market fit, not just the feature list. They have the authority to say “no” to a big customer request if it deviates from the strategy, and they have the authority to kill a feature that isn’t delivering. This is the existential burden of a founder, and it’s the core of product management.

Key Concepts

Problem

PMs must start with the problem statement - what are they solving for? This problem comes from hours of research into the problem space: why this problem exists, how many are affected, what is the total addressable market (opportunity), and what are the competing solutions. PMs then weigh their own skill-sets, experience, and technologies to determine if this problem is worthwhile pursuing, balancing the feasibility of the execution of the product with the cost.

Roadmap

Product strategy is owned by the PM, which defines the why and somewhat how we’re achieving the pathway of capabilities. Product Strategy leads towards the Product Roadmap. PMs often interview for hours across multiple potential customers to flush out the key pain points. However, it doesn’t mean every problem faced by customers becomes a product feature - that’s a sign of a PM without a strategy. PMs know their key strengths and comparable advantages, which they hone and leverage to define the roadmap. The pain points get synthesized into product features and prioritized based on feasibility, impact, and strategic alignment. One thing to note: often times PMs prioritize features based on potential lead to land deals. This is bad practice because it leads to the unintended consequence of the customer never getting their real features prioritized because other deals took precedence.

Finance

Managing financials as a PM is of critical importance. Like a CEO, they must be able to manage the operational and capital expenditures to translate to unit economics, while balancing the projected growth to achieve profitability. Ownership of the financials allows a PM to balance where to spend - whether it be on more engineering team, designers, infrastructure, or go-to-market.

MVP

Minimum Viable Product is another concept that is incredibly powerful. It requires a PM, based on all of their feedback and understanding, and a clearly defined vision and mission statement, to hone in on what is the minimum viable product. This requires ruthless prioritization to distill the capabilities down to the bare necessity - time, scope, and resources are limited. The resulting product must be something that is usable for the customer to solve a core, fundamental problem. If executed well, then a product should be able to go from ideation to planning to MVP in a matter of months (not years). This is where the mantra of “fail fast” comes into play. Hence it’s super important to get to MVP quickly to test the market on the hypothesis. If the product is viable, then it goes into the next round of iterations and with an existing customer base, the roadmap can be properly prioritized for continuous development.

Key Metrics

Adoption is needed to see how many users are onboarding onto the product. This is a key signal on the market demand and determines the product-market fit (PMF). If the product has PMF, then customers are willing to pay for the product because it has solved a clear problem. Usage is the signal to determine the product stickiness. Stickiness is critical because if the product is adopted and used once, then it’s essentially a disposable product. What’s important is to track customer engagement because that shows potential for up-sell of new capabilities. Cost/Profitability are the third metric that I like to focus on. Some times, products have wildly heavy customer acquisition costs (CAC) - some early startups spend $2+ for every $1 earned. This is clearly unsustainable and not scalable, which is why they often go back for additional funding rounds. As a PM, they must track the total cost of running the product which, compared to revenue, will be able to determine the pathway to profitability. There is no point in selling a product that is always unprofitable - you might as well deploy capital elsewhere.

Conclusion

If all of this sounds like a startup, then you are absolutely right. All startups start with the founders as Product Managers, where they deal with the existential crisis to survive. The incredible combinations of skills are why many startup founders are the Product Manager to drive the first set of iterations of the product development lifecycle until it hits a growth trajectory where the founder steps aside to be a CEO and hire a head of product to lead the way forward. Nevertheless, the key factors of success for any startup is to embrace and adopt the product mindset. Start with the problem, identify the comparable advantages, define and execute on an MVP, then go to market. It isn’t complex strategy that wins. It’s simple strategy, executed with brutal precision.

Learning is Like a Project

Frame learning as a project. To start a project, you need a clear objective. Similarly, when embarking on a learning journey, you should have a goal or outcome in mind (picking up new skills like public speaking or creating something like a machine learning system). This goal provides direction and serves as a benchmark for progress.

The Learning Curve

Every project has its progression curve. It starts off slow but eventually ramps up, then a highly productive phase, and finally ramps down. This curve is mirrored in the learning process. Initially, you may find it challenging to understand new concepts. However, as you immerse yourself more, you become highly productive in acquiring knowledge, leading to a ramp-down when you have mastered the material.

The Ongoing Nature of Projects and Learning

Despite having an "end date," projects often require ongoing support and adjustments. The same goes for learning. It's a lifelong process that doesn't end when you've mastered a particular subject. The world is always changing, and there are always new things to learn and understand - you’ll find that there are interesting new ways to approach the same problem and even relish in it.

Recognizing What You Don't Know

A key aspect of learning and growth is the ability to recognize what you don't know. This self-awareness is crucial, as it guides your learning process and helps you identify areas for improvement.

It’s key to identify the known unknowns. By knowing what you do not know, you have a clear path of moving forward to fill the gaps in your knowledge.

Seek Knowledge from Others

For me, what always helped is learning from others. I would look for experts in the particular field or problem space and reach out. When you do find those experts, tap into them for mentorship by setting up monthly or biweekly cadences to talk (being mindful of their time). It’s not always a bother because as a learner, the highest aspirations to teach others.

My favorite technique for learning is the Feynman method where you learn by teaching. Mentorship can provide invaluable insights and guidance. By learning from others' experiences, you can avoid common pitfalls and accelerate your learning process.

The Cycle of Learning: Ideate, Try, Experiment, Learn, Iterate

Learning is a dynamic process. It involves ideating, trying, experimenting, learning from the outcomes, and iterating based on feedback. This cycle is repeated continuously, facilitating constant growth and improvement. Once you learn something new, don’t be afraid to try out the new concept. You’ll find it to be quite delightful to test out the new concept and formulate new hypotheses as you progress your learning further.

Embracing a growth mindset turns life into an exciting journey of continuous learning. By viewing learning as a project, understanding the learning curve, recognizing our knowledge gaps, seeking mentorship, and continuously iterating, we can leverage our growth mindset to its fullest potential.

This isn't a journey for the faint of heart. If you want to make real, lasting change, you've got to move beyond just saying what you'll do - you need to actually do it. Actions truly speak louder than words here, making your transformation both tangible and lasting.

Environment of Innovation

Innovation doesn’t happen in a vacuum. It requires creating a nurturing environment that enables individuals across all parts of the organization to excel. Creating an environment of innovation is the cornerstone of organizational transformation. This involves:

• Strong Vision and Strategy: Having a clear and compelling vision is like having a roadmap for your organization - it points you right towards your goals. It’s super important to get this vision across to everyone effectively and consistently. Make sure it's simple enough that everyone gets it and knows exactly where they fit into the big picture. This way, everyone is on the same page and can nail their part in making it all happen.

“Having simpler ideas that one can execute perfectly is sometimes better than grandiose ideas that your company can’t execute one. When you get large as a company and when you’re trying to do complicated things, in fact, it is best, it is most prudent, to keep it simple.” - Jensen Huang, CEO/Founder of Nvidia

• Leading by Example: Leaders need to walk the walk, not just talk the talk. When leaders show commitment, resilience, and adaptability, it really sets the tone for everyone else. They need to be visible and actively involved, kind of like a commander who boosts morale by being right there in the trenches with their troops. It’s all about setting a clear example that others can look up to and follow.

“The leader sees things through the eyes of his followers. He puts himself in their shoes and helps them make their dreams come true. The leader does not say, “Get going!” Instead he says, “Let’s go!” and leads the way. He does not walk behind with a whip; he is out in front with a banner.” - Wilfred Peterson

• Rewarding Successes and Ensuring Safety for Failure: Who doesn't love celebrating big wins? It boosts morale and inspires more great work. But it’s just as key to have a safety net for those who try and don’t make it. Innovation involves risks, and without the freedom to try bold ideas, we'd all miss out. By encouraging smart risks and seeing failures as chances to learn, we create a culture that's all about continuous improvement.

“The agony of inaction, of not pursuing one's goals, far outweighs the pain of failure in the pursuit of one's ambitions.” - Anonymous

Outcome-Oriented Engineering

Aligning engineering efforts with business outcomes is critical for driving meaningful change. This strategic approach makes sure that every project and initiative does more than just push the tech envelope - it actively contributes to the core business objectives. It’s about bridging the gap between the nitty-gritty of technical operations and the broader goals of commercial success. Essentially, it highlights just how crucial engineering is as a fundamental driver of business value. This involves:

• Clearly Aligned Engineering Efforts: It's crucial for engineering teams to really grasp the business objectives. This way, their work goes beyond just tech development - it's about creating real value. Keeping up lines of communication and collaborations between the business folks and engineers is key to keeping everyone on the same page.

• Defining and Measuring Success by Outcomes: Rather than just looking at tech specs, success should be gauged by how much it moves the needle on business goals. This means setting crystal-clear, outcome-focused targets and keeping track of progress. To do this right, engineering teams need the proper tools and data to see the real impact of their work.

• Cultural Shift: Moving towards outcome-oriented engineering isn't just about changing practices; it's a whole cultural shift. Engineers need to widen their view from just the technical details to see how their work affects business objectives. On the flip side, the business team needs to start seeing tech not just as a cost but as a major driver of revenue outcomes. This kind of shift helps everyone work together more seamlessly and drives a more united effort across the company.

Meritocratic Culture

A meritocratic culture is key to tapping into the full potential of your organization's workforce. It's all about creating a space where opportunities and advancements are earned based on individual merit. This kind of environment can really boost innovation, motivation, and overall employee satisfaction. It involves:

• Encouraging the Free Flow of Ideas: It's essential that employees feel comfortable speaking up, sharing their thoughts, and pitching new ideas without worrying about negative consequences. This kind of open communication really sparks creativity and innovation. By valuing diverse perspectives, your organization can tap into a rich pool of insights and solutions.

• Leveraging Employees’ Passions and Talents: When you recognize and use the unique skills and interests of your employees, you see a boost in engagement and productivity. Aligning tasks and projects with individuals' strengths not only leads to better results but also helps employees feel more fulfilled in their work.

• Rewarding Achievement and Competence: Here, rewards and promotions are all about what you've shown you can do and achieve, not about how long you've been with us or who you know. This approach pushes everyone to give their best, knowing that their hard work and achievements will be noticed and appreciated.

Avoiding the Risk-Oriented Mindset

Enterprises are designed to minimize risks, resulting in inaction. The best way to mitigate risk? Do Nothing. But here’s the catch: while you’re standing still, your competitors are racing ahead. There’s an old saying: "doing nothing is your competitor’s opportunity to eat your lunch." Really embracing this idea is key if you want to transform your organization. It's all about sparking innovation, making sure your engineering efforts sync up with your business goals, and building a culture where the best ideas and hardest work get recognized and rewarded.

True transformation requires a deep commitment across the organization to turn visions into reality - build the vision you have imagined. It demands everything you can give, but the rewards are worth the effort. Don't just stand by - experiment, execute, and make it happen.

Inflection Point

AI is not just science fiction; it's a current reality causing significant changes across various sectors. In financial services, AI algorithms analyze market trends and optimize investment strategies with remarkable accuracy. In healthcare, AI aids in diagnosing diseases, customizing treatment plans, and even predicting outbreaks. Logistics benefit from AI through optimized routing and supply chain management, leading to cost reduction and improved delivery times. While this technological revolution holds immense potential, it also introduces a certain fear of the unknown.

Resistance to Change

Organizations and individuals often resist change due to fear of the unknown and comfort with the status quo. This resistance can manifest as denial, reluctance to adopt new technologies, or even active opposition to innovation. History is filled with cautionary tales of companies that ignored major technological advancements and paid the price.

Blockbuster famously turned down the chance to purchase Netflix, continuing to focus on its brick-and-mortar rental stores, only to go bankrupt as digital streaming took over. Despite having the technology in their own labs, Xerox missed the personal computer revolution, allowing Microsoft and Apple to dominate the market. Polaroid, once a leader in instant photography, overlooked the rise of digital photography, thus allowing Canon and Nikon to take over. These examples underline the risks of remaining stagnant.

Reward for Embracing Change

Conversely, those who embrace change often find themselves at the forefront, reaping significant rewards. Henry Ford revolutionized the automobile industry by embracing mass production techniques, making cars affordable and accessible. Thomas Edison's invention of the lightbulb, fundamentally changed how the world illuminated the night, opening a new age of industrialization. More recently, Amazon transformed retail by pioneering online shopping, setting new standards for retail and customer service and becoming the behemoth it is today. In these examples, the innovators were not the first in their fields. Instead, they recognized the potential of new technologies, embraced change, envisioned the future and executed their visions for widespread use. These innovators saw potential where others saw risk.

Risk Aversion and Mitigation

Most organizations, by nature, tend to be risk-averse, often adopting a cautious approach towards new technologies. They prefer predictable outcomes over uncertainties introduced by untested innovations. Efforts are frequently made to mitigate potential risks associated with emerging technologies. This sense of caution, while prudent, can often serve as a barrier to progress and stifle innovation.

However, you can innovate while navigating uncertainty. By delicately balancing the inherent risks and potential opportunities presented by new technologies, companies can successfully maneuver the complex landscape of technological change. This enables them to not only survive but thrive in a competitive, technology-driven business environment.

To achieve this, it's crucial for organizations to foster a culture that actively encourages and supports experimentation, continuous learning, and growth. By doing so, they can inspire their employees to embrace innovation and change, helping the company stay at the forefront of technological advancements and maintain a competitive edge in their respective industries.

Become a Change Agent

In a world where innovation is swift and continuous, embracing change isn’t just an option - it’s a necessity. With the emergence of AI, we are at an inflection point that will redefine industries and shape our future. As a change agent in your organization, adopting a proactive mindset is crucial. This means fostering a culture of innovation, promoting learning and experimentation, and involving others in this transformative journey.

Innovation goes beyond merely incorporating new technologies; it’s about cultivating a mindset that welcomes change and uses it to drive growth. Those organizations that resist change risk obsolescence, while those that embrace it stand to reap substantial benefits. As you navigate this rapidly evolving landscape, remember that the future favors those who adapt and innovate.

So, welcome change, experiment, learn, and evolve. Drive innovation, encourage growth, and steer your organization towards a prosperous future. Be the driving force in your company, ushering in an era of progress and success.

The AI Boom is Here

Why all the fuss? AI is revolutionizing every facet of our lives. Whether you're booking flights, shopping online, or getting a medical consultation, AI is probably at work behind the scenes. The release of Generative AI capabilities with ChatGPT has been a game-changer, pushing every organization to reconsider their stance on AI adoption. As businesses invest heavily in AI, they aren’t merely chasing a trend - they are strategically positioning themselves at the cutting edge of innovation to maintain competitiveness in a rapidly evolving marketplace.

Riding the AI Hype Cycle

Let’s face it: big trends often come with big hype, and AI is no different. We’re currently in the thick of the AI Hype Cycle, an exciting yet challenging journey. The excitement is real, the innovations are amazing, but so are the obstacles, such as the scarcity of skilled AI professionals needed to leverage AI effectively and the dangers of pumping too much money into AI initiatives prematurely.

Scouting for Talent

A major challenge? The acute shortage of talent and expertise. AI requires highly skilled individuals who can master the complex realms of data science, machine learning, data analysis, data engineering, and beyond. The intense competition for these experts is a major barrier of entry for most organizations, particularly if you are not a big tech or startup.

Consider upskilling existing folks in the organization who are already deeply embedded in data day-to-day. They would have the institutional knowledge of the business and the underlining data / systems. Augment them with new junior talent, who have the right mindset to learn and build new solutions. You aren’t going to find folks with 5+ YOE in Generative AI (hint: they don’t exist).

Overinvestment Trap

Then there’s the financial aspect. In the midst of AI enthusiasm, it's easy for companies to overshoot their budgets on AI projects that may not be mature enough for deployment. It could take years for many organizations to truly see a return on their AI investments. Thus, finding the right balance in funding these projects is crucial to avoid potential missteps.

Remember the hype around Big Data and Hadoop? It quickly became a legacy system, with many companies now struggling to transition from Hadoop to modern cloud-native data analytics platforms like Databricks and Snowflake. This is a cautionary tale about how quickly technologies can evolve. In the world of AI, I'm observing an even more rapid pace of innovation and change, underscoring the need for pragmatic investment and strategic planning in this landscape.

Instead, try focusing on key business problems where AI can deliver outsized value. Prioritize and focus on the actionable outcomes that can deliver value quickly and safely. Be sure to take into consideration the current capabilities and the lift needed to deliver the outcomes. With the ROI in mind and in focus, you can strive to deliver the first set of outcome through experimentation and piloting with limited users to prove the capabilities.

Data: The Unsung Hero of AI

At the core of AI's power is something seemingly mundane but incredibly powerful: data. Think of data as the coal that fueled the steam engines of the past. Without good quality data, your AI projects might as well be running on empty. Getting smart with data isn't just an option; it's a must if you want to get anywhere with AI.

Why Managing Data Matters

Handling data right is key. You need to acquire, store, and process data effectively to feed your AI systems. Mess up here, and your AI could end up making decisions based on faulty info - definitely not what you want. Imagine if you give your business partial or imperfect data to make decisions. If you can’t do that with humans, how can you try that with machines?

Climbing the Analytics Maturity Mountain

Getting good at AI is like climbing Mount Everest. It's not something you do on a whim. You cannot fly to the top of Mount Everest. It requires planning, the right gear, and a ton of effort. Companies need to level up their data handling and analytics skills step by step - like scaling a mountain - before they can really reap the benefits of AI.

Is Your Company Ready for AI?

Before jumping on the AI bandwagon, it's critical to check if you're actually ready. How long does it take for you to go from an idea to insight? If it takes months, consider ways to reduce that to weeks. Do you have the right tools? If your data is still living on-premise, consider migrating to cloud-native solutions to accelerate insights first. Is your team on board with evolving and adopting new technologies? If your organization is too resistant to change, you won’t get far. Getting these elements right is essential to making the most of AI without falling flat.

Wrapping Up: Smart Investing in AI

Navigating the AI landscape is all about balance. It's exciting, yes, but it's also filled with challenges that needs innovative ways to navigate the market. By focusing on solid data practices, building analytics expertise, and investing wisely, companies can make the most of this AI gold rush. It's not just about who spends the most but who can invests the wisest.

In this era of AI, understanding how to harness technology effectively is the real treasure. It's about creating lasting value and growing sustainably in a world where AI is reshaping how we live and work. So, let's move forward with a clear outcome and make the most of this incredible technological adventure!

Developer Velocity: The Key to Innovation

In the dynamic and rapidly evolving tech landscape, developer velocity, defined as the speed at which a developer can produce high-quality code, is an indispensable factor driving innovation. The pace at which an engineer can pivot, adapt, and reorient their approach directly influences their ability to deliver cutting-edge solutions quickly. The faster an engineer can respond to new challenges and opportunities, the quicker they can develop and deploy innovative solutions that meet the needs of the market and end-users.

However, achieving this level of speed and agility is not solely a matter of individual talent or effort. It requires a supportive environment in which engineers have access to the right tools and platforms. By empowering engineers with robust, developer-friendly tools and platforms, we can significantly enhance their productivity and facilitate their efforts to innovate. Ultimately, boosting developer velocity is not just about increasing speed; it's about catalyzing innovation and enabling engineers to make meaningful contributions to the business.

Tools for Success

I have seen first-hand that our data platform enabled our engineers to achieve amazing feats. In a matter of four months, we were able to successfully deliver a continuous data migration pipeline that syncs a legacy system to a modern policy system with just 2 engineers. This involved over a hundred dataset, numerous transformations, and dynamic data pipelines to scale quickly and safely. Key driver of this innovation was a combination of Dagster, with its asset factory for dynamic pipelining, Soda for data reconciliation and testing, Coalesce for accelerated data transformation, and Sigma for enabling operational monitoring.

The pivotal point is to identify which products and tools are most compatible with your organization. This should be achieved by understanding your organization's value chain, and strategically placing the best of breed products that can deliver outsized value. The emphasis should always be on selecting the right products for the right use-cases, ensuring the best return on investment for the team and the organization.

Trusting Analytics Teams to Drive Revenue Outcome

A crucial part of empowering engineers is the trust we build with the business. When engineers can quickly implement and adjust based on feedback, they become a much more valuable asset to the business. This leads to increased trust in the analytics team's ability to drive revenue outcomes, further cementing the importance of a well-oiled engineering team.

The sweet spot in all of this is when engineering teams are embedded directly within business teams. This alignment allows for a direct line of communication between the people who understand the business needs and those who can create actionable solutions to meet those needs. When these teams can work in tandem, the value they can deliver is exponentially increased.

In conclusion, the key to "democratizing the 10x engineer" is not about finding unicorns, but about creating an environment where every engineer has the potential to become one. By focusing on developer velocity, fostering trust in analytics teams, and integrating engineering teams with business teams, we can create a space where every engineer can deliver "10x" value.

Surprisingly, over 77% of companies are either using or exploring the use of AI. This has triggered a talent hunt among organizations looking to incorporate GenAI into their solutions. For an AI strategy to be successful, it's crucial for an organization to have a robust data strategy.

Time-to-Insight

How can we emphasize data in an organization?

Time-to-Insight refers to the period between the inception of an idea and the delivery of related insights.

An idea could be a simple question like, "What is the performance of our marketing campaign?" or "What are our sales to date?" or even "What is my customer's main concern?".

Insight may come in the form of a report, dashboard, or model that answers the question. Answering these questions requires a combination of data acquisition, modeling for structured data interpretation, and an analytical output like a dashboard to summarize the results.

The challenge lies in the amount of work and effort needed to deliver these insights. It involves engineering, infrastructure, and analysts. From my experience, this process typically takes several months. Furthermore, the answers to initial questions will lead to more questions, further prolonging the insight delivery process.

KPI-Driven Design

I've focused on Time-to-Insight as the KPI when designing teams and setting objectives.

KPIs are critical in team and organizational design due to the clarity they provide of the expected outcome. By focusing on the outcome, teams can concentrate on the how and who required to achieve Time-to-Insight.

If it takes six months for your organization to deliver insights, that business opportunity is long gone. It’s no wonder why most organizations are dissatisfied with their data teams! Funding to data teams get cut, data teams struggle to support the needs of the business, and the Time-to-Insight grows.

This vicious cycle can be changed. In the coming weeks, I will share how we addressed this challenge within our organization.

We've significantly reduced our Time-to-Insight from six months to just two days.

This transformation didn't require several years or a large team of engineers, but was achieved within a year with just four full-time employees.

I have witnessed how Time-to-Insight can transform organizations into data-driven entities. When our organization can answer questions within days, it prompts even more inquiries that our team can promptly address. This leads to an influx of insights, sparking more ideas. It's a rewarding experience where users no longer compete for priority, instead focusing on how their ideas can uncover new opportunities.

Data platforms were not a well known thing outside of big tech world. I was working at the time in a large asset management firm that was still on-premise with outdated tooling to handle their ETL - we were using Informatica PowerCenter at the time with Oracle Exadata. These tools were already a big improvement over my previous SQL Server + SSIS stack. But nevertheless, the issue still remained - taking a user’s idea to providing some sort of insight in the form of a report, dashboard, or summary took well over 6 months to deliver from start to getting the first cut.

Why did it take so long? Why does it involve so many people and across so many teams? Why does it have to be this way?

These were the questions that I pondered long and hard on a rainy night. There must be a better way to do this.

This is what I wanted to set out to explain as a blog. To share my experience and insight to the other data practitioners and leaders to help them change the trajectory of their data journey.

Idea to Insight was created to focus on that key concept to enabling a data-driven culture by accelerating a team/organization’s ability to deliver insights in days or hours, not months. I was obsessed with time-to-insight as the key metric to optimize for. If we can deliver insights in days or hours, what sort of innovations could be fostered? What sort of transformations could occur in an organization? Can this transform an organization’s culture, through data?

If this resonates with you, feel free to subscribe to my blog as I’ll be posting content like this in more detail to help others learn and grow to enable you to drive innovation and transformation in your organization or team.