 Hi, everyone. This is Ajanta. I am a Senior Product Manager with Microsoft for over 13 years of industry experience. I'm deeply passionate about data and analytics, and it has been instrumental in integrating data as a key element in product roadmaps. Today, I am going to speak to you about data is an integral part of product management. Let's get started. I would first like to get us started with, what is product management? Product management is really a very strategic function that drives every step of product lifecycle from ideation to development, to go to market, to continue support of product. Product management is a very strategic function that defines the very existence of a product. The reason for why the product exists, the why, why does the product exist? At a high level, following steps are involved in product management. Number one, defining the problem. Number two, quantifying the opportunity. Number three, researching the potential solutions. Number four, building a minimal viable product, commonly known as an MVP. Number five, creating a feedback loop. What is the role of data in product management? In today's digital era, where technology is more dominating than ever before, users are constantly adopting new habits coupled with changing mindsets. Data is integral to understanding the problem and delivering data-driven solutions with business metrics that are centered around the return on investment. Without the influence of data, the product is quite often at a risk of being blindsided and we end up building something that may not offer sufficient value to users and ultimately may be at a risk of either being misused or manipulated. Following are some of the critical areas in product management where data plays a crucial role. Number one, measure the right things. Number two, being patient and avoiding vanity metrics. Number three, running tests. Number four, qualitative and quantitative data. Number five, product analytics tools. Number six, key results. We are gonna walk through all of the six critical areas in product management where data plays a crucial role in depth over the next few slides. So let's get it started. Let's begin with measuring the right things. You can imagine the product teams are like ships in an open water where metrics are really the driving compass that helps them understand the desired outcome. So it's very essential to define metrics that aligns with the top organizational goals. It's also essential to choose the right metrics that should be very well aligned to customer success and business results. At times, there may be a focus group of metrics for a specific product area. Guess what? This might exclude some other important areas of product which aren't getting measured but are probably a nice to have. So it's important to evaluate and reevaluate measured metrics to align with the overall business goals to avoid the product teams risk going in a wrong direction. Moving on, it's very essential being patient and avoiding vanity metrics. Let's get a little bit deeper into vanity metrics first. Separating actionable metrics, aligning up with overall business goals is essential. Product teams quite often track way too much data. For example, knowing when a user stopped the intro video during the onboarding process, isn't that useful to align with the overall business goal of increasing the number of users signing up for the product? Rather, a useful metric to track here could be the number of users abandoning the sign-up experience. We do need to keep an eye out for vanity metrics that are being measured as nice to have but doesn't quite correlate to overall product success or customer success. Being patient is very critical as well. Product improvements where quite often take time to show visible effects. For example, if number of calls dropped the first day after releasing a new feature, adding more variables without allowing a pre-chosen duration for the key metric to actually evolve and stabilize will not fix it or guide the product leaders in the right direction or to even make the right decision and could often increase the engineering investment without enough ROI. So being patient is very critical. We need to allow those key metrics to stabilize over a given period of time. And those time periods have to be defined and identified early on when we are defining the product roadmap. Let's be patient. We need to understand that as humans are complex, behaviors are unique. Very small elements like weather, holidays, home team winning football could affect a user's product usability. So let's be patient. Running tests, product teams usually begin with A-B tests. What is A-B test? It's very simple. It's basically comparing two versions of a feature to understand which one would perform better. Engagement and outcomes with each feature is measured and the right option is chosen. Let's ensure the sample size being used for running experiments or running tests isn't too small to be able to achieve statistical significance. If it is too small, we might again get blindsided on not being able to choose the right option while we are running A-B tests. And we might be risking increasing the engineering investments without good enough outcomes here. Qualitative and quantitative data. It is all about data these days. It's essential to review both qualitative and quantitative data. Sometimes qualitative data such as focus groups, user interviews, et cetera, in conjunction with some of the very important quantitative metrics could change the product roadmap compared to what a quantitative metric alone would have suggested. For example, just looking at the number of monthly active users added to your product compared to in conjunction with the group of users that is being focused on the group of users that is really signing up for my product and the other group of users where the growth may be a little bit slow. If I combine both qualitative and quantitative data sets, the perspectives are very different and the data that is shared with our team through that process is very different and hence that influence the product roadmap very differently compared to if I'm just being focused on the quantitative metric alone. We would like to create products that not only solves users pains but also make sure that the user loves the product. Cognitive behavior and emotional connection plays a deep role and very hard to measure. Key results are very commonly used terms in a lot of the product teams, small or big, larger small organizations, key results are very critical. Key results are basically a mechanism to define the outcome and success of a product which is intended to solve the problems of a certain focused set of user personas. For example, a key result to a data storage product could be number of monthly users upgraded to additional storage or a number of new users signing up into the product to store data. This would help us as the product team understand the engagement of new users with the product and hence this would help us drive the product roadmap towards success, customer success and solve the user's pain points appropriately. So how does a product manager really use data here? Ultimately, what we're really talking about is data is a very integral part of product management. In the early 2000s, if we remember, companies like LinkedIn, Netflix, Uber had a problem. Teams across the organizations were working with lots and lots of data at scale. Data was powering up their product roadmap, fooling executive level decision making and informing their paid marketing campaigns. Internal and external data was flowing in and out of the company. As a result, the product manager would often use data in influencing the product roadmap and asking some of these questions. What data exists? Who really needs this data? Where is the data flowing to and from? What purpose does this data serve? Is there a way to make it easier to work with or access this data? Is this data even compliant and actionable? How can we make data useful to more people at the company and make it faster to access as well? So what are the specific outcomes that are usually delivered by data? Increased data accessibility. Surface the data where people needed and when they needed instead of just randomly surfacing data everywhere. Let's increase the data democratization. Let's make it easier for people to manipulate the data with the right access and permissions involved. Let's increase the return on investment on data and let's make it faster. Let's enable our users to gather quicker insights from the data. Let's provide more precise insights like including experimentation platforms, the A-B tests. What are some of the essential features a product driven by data should absolutely have? Reliability and observability. Acceptable downtown for a software as a service product is a discussion of how many nines? Is it a 99.9% availability? Is it a four nine availability, five nines availability? Scalability, the data product should scale elastically as the organization and the demand grows because guess what? These past three years with the pandemic we have learned that resources are not unlimited but with the limited set of resources we still need to allow our data product to scale as the organization and the demand grows and growing the organization and the demand is a healthy culture of the organization but we need to allow the product to scale with the limited set of resources. So let's make it available when it really needs to be available to the people who really need to use it. Extensibility, while the data product has likely been built from an integration of different solutions, it needs to maintain the ability to easily integrate with APIs and we were subtle enough to be ingested in all the different ways end users like to consume the data. Usability, some of the great SaaS products focus on providing a great end-to-end user experience. They are easy to learn, fun to use and they are quicker to get work done. We get faster insights from these data products. Security and compliance, data leads are very expensive and painful and they are often subjected to regulatory fines. So it's important to bake in security and compliance early on into the product roadmap because we know that the product is gonna grow for a certain set of users we are targeting. So let's bake in security and compliance early on into our product roadmap. Release discipline and roadmap. SaaS products continuously evolve and improve. The roadmaps are built at least a year into the future, sometimes even a couple years with a very strong quality assurance process for updates. Thank you so much. I hope you enjoyed the presentation and learning about how data is an integral part of product management. If you would like to connect more with me or share your experiences about data and product management in general, please feel free to connect with me on LinkedIn. Thank you very much.