What Is Polywork?
Polywork is a networking platform for professionals who are engaged in different activities. These can be anything from a podcast to a side gig to even a startup venture. Its key differentiator was the opportunity feature, which enabled its users to post potential collaboration scenarios for others to see and engage with alongside the usual social announcement feed. However, lately, the platform has been reduced to a simple resume site, similar to about.me, coupled with a messenger feature.
Despite its questionable move, Polywork may have some potential as it's still a young company. To explore this potential, what better place to look than its data and its business impact?
Data Involved in that platform
The data involved in Polywork is many-fold. For starters, there is the data each user contributes to their profile. Once deployed, this data is fairly static, but a very solid basis for various events. After all, the users of Polywork are there to interact with each other (at least in principle), so there is a strong incentive to consume this data and act on it. This triggers a variety of interactions that generate even more data. From the time stamps to the interest signals (aka "boosts") to all the text in the comments.
Additionally, there are direct messages that can also be a data source, even if they are probably not read by the Polywork team, for privacy reasons. In any case, the platform may still record the who, when, and how, for each one of these messages. On top of that, if a user follows someone else, there is an additional signal that may gradually build a graph depicting the connections and other relationships among the users.
Beyond all this, there is also data related to when a user logs in, what device they use, how long they stay, what external links are clicked, what sites these links point to, etc. In other words, just from the passive activities of a user, there is a large variety of data to be collected, for all users, even the less vocal ones. Naturally, someone inside Polywork is bound to have access to additional data that is collected around the users, so the datasets available are bound to be richer.
But how is this data useful to Polywork? That’s a question every data professional looking at this is bound to ask.
Potential Data Products
Several data products can leverage this sort of data. By the term data products, we mean any kind of application or dashboard that provides useful insights or some kind of functionality that drives engagement and adds value to the end user. For platforms like Polywork that live off engagement, such products are not just nice-to-haves but an essential part of the user experience. Other similar platforms have thrived through the use of such products which help keep these platforms fresh and interesting.
Some examples of such data products are the following:
Naturally, all this is just scraping the surface of what’s possible. However, before a data product is developed it needs to be tied to a particular desired outcome from the business standpoint. Otherwise, it is just an intellectual exercise that may look good but not justify the resources it needs to come about. That’s why the business aspect of data needs to be taken into consideration, both before and after they are developed, as we’ll look at in a moment.
Business Impact of Those Products
The business impact of data products can be noteworthy, enough so to justify building and maintaining them. In this case, there is little doubt that at least some of the aforementioned data products can add business value. How much exactly would depend on how each data product is implemented, how it is used in the platform, and how it ties to the other features of Polywork.
Let’s look at the data products one by one and how they can add value to the Polywork business.
All in all, there is a lot of potential in Polywork and the data it accumulates through its features (or at least did so until recently). Naturally, this company is just an example of how data can be leveraged to improve the business and make the product more engaging. Materializing this potential may not be a simple task, but it’s something feasible and worth exploring. The only prerequisite in this endeavor is a solid understanding of data work and the ability to reason around it, aka data literacy. Feel free to contact us for more information on this subject.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.