Data pooling: Get the most out of the data you collect

Harvard Business Review called “data scientist the sexiest job of the 21st century”. Unlocking data value inspires you to look forward to finding out more. Dissecting data is what makes your business services worth; allowing you to bring value to your customers and to stay competitive. The more you collect, the better. But is it really about data collection?

Flip the coin and you’ll see that the true potential of the data that you collect lies in its aggregation. Your data scientists will only be able to unlock its value when identifying rich data sources, aggregating them with others, and pooling large quantities of formless data.

With, we enable you to pool and analyze data meaningfully.

Data pooling enables you to combine data sets coming from different sources. 

Let your Data Scientists Deep Dive.

Your data scientists need data. A lot of it. They can only make discoveries while swimming in deep data pools. Let them deep-dive – ensure that their swimming pool is deep enough.

How to do so?

Let’s assume that you aim at developing a Digital Health platform, fed with aggregated personal data. So data pooling means that you can:

  • combine together data on one individual coming from multiple sources such as medical devices, specialist clinics, health records.
  • merge into one file multiple datasets from many patients coming from various countries or institutions. 

In both cases, this is what makes the data pool deeper and generates value for your data scientists. 

Data Pooling By Pryv

The wait is over: the icing on the cake.

Now you know why data pooling is indispensable to get valuable insight from data that you collect. Unlock your business value by ensuring that your data scientists are provided with enough data at a glance.

The best is yet to come- the Icing on the cake : does it for you. From data collection to data aggregation, data structure and understanding, Pryv enables you to get the most out of the data you collect. Let’s walk through an example to illustrate where stands and how we can help you.

It’s often that you are collecting data from different sources and that you need to share the whole or only part of the aggregated dataset with various third parties.

So let’s go through a basic use case, in which you are conducting a clinical trial involving hundreds of patients from different institutions. You are researching the impact of allergen exposure and nutrition diet on the sleep behavior of patients, and you’re having three different teams analyzing the collected data from different perspectives :

  • Team A needs to analyze personal information from every patient (such as age, gender, ethnicity, etc);
  • Team B is combining allergen exposure with sleep data to identify patterns;
  • Team C is looking at the relationship between nutrition and sleep.

Already getting a headache just thinking about how to structure your data efficiently to aggregate it and share only what each of the teams need? enables you to aggregate data from multiple sources and to share only what is needed.

Data Pooling By Pryv has developed a tool that solves it all for you. Our aggregator allows you to pool data from multiple sources at once, and to create appropriate and up-to-date datasets with data across multiple accounts for each team to work on.

How is that?

  • aggregator enables you to collect and aggregate any type of data from all your patients. Data from a smartphone, a smartwatch, a health record, any source of data, all in one place.
  • Our data model in “streams” and “events” structures your data, and allows your patients to grant your app/or your team access to the needed streams. (more on that in this article)
  • Each patient’s data subset gets aggregated into your own database and the appropriate datasets are exposed to your teams and your algorithms/apps.
Data Pooling By Pryv
  • Once datasets are allocated to each team by sharing the appropriate subsets of streams, they get automatically updated. It means that as soon as new data from any patient is collected, a webhook notifies the server that fetches the data and updates the dataset for the concerned team (more on webhooks in here).

Data Pooling By Pryv

All at once, you can now collect and aggregate data coming from different sources for one to thousands of patients, and create multiple data pools so that your data scientists can work more efficiently and get the most out of your collected data. All of that while respecting the data privacy of your patients, and ensuring a trusting relationship.

It’s as simple as that, so you have one less thing to worry about and one more to benefit from:

Ana @ Pryv