When I first started to build products I used a mixture of intuition and user feedback to guide my decisions. As I became increasingly sophisticated in my approach, I realized I was missing a key component: data. Data can be analyzed, inspected, cleansed, transformed, and modeled. This transformation, which takes data and helps designers and builders understand useful information, is what is referred to as data analytics. Data analytics, when studied deeply, is like adding a large monitor to your small computer screen: it enables you to see things with a clarity and resolution you did not previously have access to.
Data analytics is everywhere in the modern world: it helps us to stay informed with regards to the technology we use, how software is built, and the ways in which products are developed.
When designing a product, a builder first needs to decide what to build and most importantly, why. What problem is being solved? How will the product help a person or group of people better do some activity?
How builders use customer insights and data analytics?
Builders start with the customer and then work backwards. Data is an incredible tool in this process. By working vigorously to earn and keep customer trust, a builder can pay attention to how their product is used and by whom.
In the early stages of product development, there are four specific questions that data can unlock. These general questions include:
Data can be categorical (location, gender, etc.) or even numerical (active users, number of customers, and so on). Some data is discrete (it could be the number of job applicants applying to a job) and other data is continuous (with an infinite number of possible outcomes). Before going about analyzing data, let’s take a moment to understand the types of data we have. In the four questions mentioned above some of the outputs, or answers, are categorical while some are numerical. Knowing the types of data at your disposal will help you better understand the foundations of the data choices and insights you can glean.
Data is in many respects democratic. It can be used by anyone, learned anywhere, and deployed in a multitude of ways. From community college nursing programs, to applied manufacturing, to fraud detection in software, data is a tool that every field and every profession can leverage to extract meaning.
The Evolution of Products And Data
As your product evolves, there will be new forms of data that your users and the product itself produce. Naturally, this type of data will depend on the type of product you are designing. For example, if you are building a website, this website can’t produce much useful data prior to launch. There are no users and no site traffic. But as you release it and get users you can start to measure a host of variables to make better design decisions. You can look at how long people stay on the site, what your sources of traffic are, and how people navigate to various pages within your site index. You can collect this quantitative data to make better-informed decisions and improve your site’s design, speed, and layout.
Now imagine a different design situation. Think about a software that is more timely and expensive to build - in other words the fixed costs of starting are higher. Some examples include an invoice software or enterprise employee benefits tools. If you are building either of these, you will likely need to invest more time in collecting qualitative data from users such as how did they hear about the software, what did they expect to see upon first logging in, and so on.
After releasing a working version of your product to the public (or beta users) you need to have relentlessly high standards to collect and analyze feedback to make future versions - or iterations - better. Builders who use data transformation technology to continually raise the bar to drive high-quality products, services, and processes. These builders ensure that defects do not get sent down the line and that problems that are fixed stay fixed. They use data to confirm these improvements.
Scaling Your Product with Data Insights
When your product evolves, and your user base grows, you will want to consistently invest time in more scale data collection and analysis to make better product decisions.
Of the different types of data that you can leverage, here are three that are particularly powerful for those with a relentless focus on users.
Conclusion: Data analytics is a tool. Deploy it at the right time for best results.
Unique insights can be gleaned by looking at the shape of your data - how dispersed it is, where it lies, and how it is shaped. Data is, at its core, just facts and statistics collected together for reference or analysis. Data can be quiet: without your analysis it tells no story at all.
The goal of a builder is to use data to unlock value. There is a famous old quip in Silicon Valley: “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” Data can be a powerful ally regardless if you are building a tool, a widget, or a complex piece of business process automation software. How you use and interpret it is your call. But as your product evolves through different stages of the lifecycle - from startup to growth to hyper-growth - different types of data will be useful to you.
As a final thought experiment, imagine the last time you gave a friend or family member a personalized gift. Was it well received? It likely was! Why? Because you had useful data that could guide your purchasing decision which greatly increased the odds that your gift would be well received. This mentality can be applied to leveraging data for business and product outcomes.
Remember to look at data as a statement of fact. Facts can lead you to different conclusions but the guiding principle remains: by looking at trends and real behavior you can better understand and accurately predict your users or clients. Through a deeper understanding of real people and their behavior, you can build better tools and resources to help them. Hence, data analytics is a study that leads to better product design decisions and ultimately, happier and better-served users.
