Definition of Passive Data:« Back to Glossary Index
Passive data is collected without participation, and sometimes even awareness, of the people involved in the process itself. This kind of information has a lot of value, particularly for companies trying to do real assessments, troubleshooting or problem solving. Unfortunately, it’s not always easy to capture information without impacting the people and processes under observation.
Overview: What is passive data?
The defining quality of passive data is that it does not disrupt the object of study. This means the variables involved are allowed to emerge and fluctuate as they normally would within their normal range. This minimizes potential contamination of data due to the study and ensures it is relevant, accurate and actionable.
3 benefits of passive data
It’s hard to have too much information, as long as it’s accurate, curated and presented in a useful way. Passive data has a lot of real value and uses for companies depending on their immediate situation and their goals.
1. Consistent reporting
One of the biggest challenges in information collection is user participation for reactive data. It’s hard to compel people to leave feedback, responses or contact information. There’s also a risk that more intrusive data collection methods may start turning customers away altogether. Passive systems and methods are the key to reporting consistency.
2. Less subjectivity
Asking a user to rate their experience or provide feedback doesn’t always yield the most reliable information. People make mistakes, they have different tastes and they don’t always have perfect memories. That’s why relying on self-reporting as a primary information source can be disastrous.
3. Scalable strategies
Why is passive data important to understand?
It’s almost impossible to escape from passive data collection these days, whether you are on the internet, getting groceries or paying your taxes. Companies need to really understand it so they can start harnessing it for their own growth.
1. Know its limits
One of the most important reasons to understand passive data is to know its limits. It offers crucial and unbiased insight, but this doesn’t always tell the whole story. Hearing personal testimony and experiences from the people is important too, especially if you want to get context and really understand specific problems in the workplace.
2. Tailoring technique
The viability of passive data depends on the specific technology and methods being used. Since the bulk of data collection happens online, software programs are responsible for most of the tracking, storing and analyzing. You need to understand the implications of your technology and methods before you can use data effectively.
3. It’s the future
Data is the future in pretty much every industry. It unveils opportunities for internal improvement. It’s how companies predict market trends and find better ways to connect with customers. Passive data collection has the most potential for large-scale and non-intrusive data management.
An industry example of passive data
A popular grocery store recently renovated their website and upgraded it to allow for convenient and streamlined curbside pickup services. The store’s goal was to reduce foot traffic inside the building to cut down on crowding and demand for parking spaces, which are limited. Unfortunately, the newly launched website did not catch on with customers as hoped.
The store needs to figure out why customers don’t want to use the website. They start by issuing digital customer surveys, but responses are infrequent and lack details. The store then implements analytical tools on their site that gathered information about visitor activity, like how they spent time on each page and click activity. This passive information helps the store address bugs and confusing elements on their site to improve their online ordering.
3 best practices when thinking about passive data
Data is easy to misuse or under-use. Even following some basic best practices, leaders need to be careful and intentional about how they collect data and inform their decisions with it.
1. Don’t put all your weight on it
It’s always better to have an arsenal than one big secret weapon. Even if passive collection is the main course of your company’s data diet, it shouldn’t be the only item on the menu. Incorporate observational techniques and active collection into your strategy when possible to round it out.
2. Technology matters
Staying ahead of the curve on every new development isn’t a big deal for many companies. You don’t always need cutting edge tech to do well, unless it’s crucial to your industry. However, data collection is very dependent on technology. Companies that want to embrace passive collection should expect to make some changes to accommodate it.
3. Establish direction and purpose
Don’t just throw a net out to grab for any information you can find. Data collection should be set up with a purpose and strategy. You need to know what kind of information you want and what you will do with it when you have it.
Frequently Asked Questions (FAQ) about passive data
1. What’s passive vs active data?
Whether data is active or passive depends on if the subjects of the study are participants or not. Active data is collected with the participation of the subjects. Passive data is gathered without their involvement.
2. What is the most passive way to collect information?
Observational study is the baseline for passive collection. The best passive strategies have little to no impact on the people or system they are studying.
3. What is a passive data generator?
Any automated system that collects data without input or disruption to the user is a passive data generator. User internet activity, cookies and cameras are a few examples of data generators.
Be proactive about passive data
Companies need to be proactive in their passive data strategies. There are very few good reasons for missing out on the value that data offers. In the information era, businesses need to develop, implement and upgrade their collection methodology if they want to compete and survive in the years ahead. Strong data management practices are also the foundation for real innovation and virtually all lean management practices.« Back to Dictionary Index