Anatomy of Data and Data Analytics
The words “data” or “analytics” probably meant nothing to product companies over 100 years ago. There was likely little to no data available for product analysis, and decision-making wasn’t driven by data at that time. Additionally, collecting data was probably a time-consuming and expensive task. In short, the product world was simple without data.
Now, things have changed. Products and product-driven companies have grown tremendously in customer base, business, and the solutions they offer. The only way to demystify them is through the use of data and data analytics. The computing revolution that began in the late 1960s has fueled the growth of data and data analytics, uncovering the magical powers of mathematics and its application in everything we do today. The mantra is now:
Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway. — Geoffrey Moore, Author of Crossing the Chasm
If you search for the term “data analytics” on Google, you’ll find a flood of information and various analysis tools for collecting, analyzing, and summarizing data. There is a substantial online community called “Kaggle” for passionate data experts who discuss all sorts of data and products worldwide. I recommend you check that out.
So, how do you get into the world of data and data analytics if you are new to product management? What’s the starting point? Before diving into technology-based tools, I insist that you understand the anatomy of data and data analytics. Without this, it will be challenging for you to even prepare simple metrics. Let’s delve into this topic from the perspective of how data is described, collected, and analyzed.
Definition of Data
Data is simply factual information collected from various sources, either in physical or digital format. According to the Merriam-Webster dictionary, data is defined as factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation. In a more technical sense, data is a collection of factual information in qualitative or quantitative formats. Data can be descriptive and quantifiable, and we’ll explore these in detail below.
Qualitative data is about qualities and characteristics. It is non-numerical data generally collected by observing, labeling, listening, and describing the qualities of an object. Here is a list of methods to generate qualitative data:
- Image processing
- Symbol translations
- Listening to audio tapes
- Watching video recordings
- Observational notes
- Written notes and documents
Examples of qualitative data include colors, likes vs. dislikes, smells, shapes and sizes, aesthetics, sounds, tastes, and textures. Qualitative data is commonly used in product management, informing decisions through user feedback, forum discussions, tradeshows, conferences, and internal team interactions.
Quantitative data is about “how much” or “how many,” and it is statistical, measurable, and conclusive in nature. It involves quantifying product performance, user experience, or the product’s success from a business perspective. Understanding and working with quantitative data is a highly essential skill for a product manager. Examples of how to collect quantitative data include metrics, tests, experiments, surveys, and market research.
Quantitative data comes in two types — discrete data and continuous data. “Discrete data” cannot be broken down into an infinite number of pieces but can be split into smaller groups. Examples include user login counts. “Continuous data” can be broken down into an infinite number of pieces, as seen in the time spent by a user per session.
Now that we’ve classified data descriptively, let’s explore how it is classified from a collection standpoint.
Structured data is organized data in a standardized format, easily readable and searchable. It is usually kept in tables or spreadsheets, with each data point representing a row and attributes in columns. Structured data is often categorized as “quantitative data.” Benefits of structured data include easy dataset creation, compatibility with relational databases and SQL querying, and the ability to join, map, create, and update datasets. Examples of structured data include names and dates, ERP system data, product user data, identification numbers, and bank statements.
Contrary to structured data, unstructured data is not organized and is not presented in an easily readable and searchable format. Unstructured data, growing at 55–65% each year, comes in various formats such as emails, SMS, social media posts, images, text, audio and video files, and graphs. It is categorized as qualitative data. The growth of unstructured data is attributed to digital content creation and sharing since the inception of the internet.
Unstructured data is generally stored using modern methods like NoSQL (non-relational databases) or Data Lakes technologies. Examples of unstructured data formats include JSON, a key-value-based data storage technique. Although challenging to analyze, businesses dealing with unstructured data find innovative ways to gain predictive and proactive insights.
To visually present these two data types, I found a great infographic created by Three Graces Legal. The creator of this graphic clearly demonstrated the difference between structured and unstructured data in the word cloud of examples.
Before concluding this article, let’s understand a bit about data analytics.
What is Data Analytics?
The word analytics comes from the Greek word “analytika,” meaning “science of analysis.” In modern days, data analytics is the use of processes and technology, typically software applications, to extract valuable insights from datasets. Big data analytics has become an essential part of modern businesses, guiding key decisions through the analysis of data and predicting problems before encountering them.
Data-driven companies commonly perform four types of data analytics:
- Answers questions like How? When? What happened?
- Utilizes historical measurements or data.
- Example: Showing the trend in the number of user sign-ups since product launch.
- Answers the question, “Why something happened?”
- Explores the mechanics behind observations, looking for patterns in the data.
- Techniques include sensitivity analysis, principal component analysis, regression, and hypothesis testing.
- Answers the question, “What will happen?”
- Uses historical data to forecast future events.
- Involves machine-learning algorithms, classification, regression models, and data mining.
- Provides specific recommendations based on extremely complex data.
- Uses advanced analytical techniques such as artificial intelligence, machine learning, and neural networks.
- Examples include Bayes classifier and ID3 algorithm.
In summary, understanding the concepts of data and data analytics is instrumental in product management. This knowledge will undoubtedly benefit those entering product management with minimal exposure to data and data analytics.
Thanks for reading!
For more information on this topic, there is a wealth of information available on the internet. The content covered here is curated from the same source specifically for product managers as a beginner’s guide. For more information, I highly recommend checking these sites: