Data matching is a machine learning function that, through algorithms and data analysis, allows platforms like Netflix, Spotify or Amazon to make more personal recommendations according to a person’s preferences.
Data matching is playing an important role in the new remote work and freelancing systems as well.
Good job matching is what will allow independent professionals to find more and better projects that are a perfect fit for their skills and specialisation.
Job matching obviously has a virtual dimension. The digital footprint a person generates will not only determine the type of clients and projects that come to them, but it will also continue to have a strong human component.
That is, an individual’s professional algorithm is complemented by the active effort taken to leave a fantastic impression in the right places.
Examples of data matching in everyday life
Although people may not realise it, they are influenced by thousands of algorithms designed to bring to their screens the services, content and products best suited to their needs and lifestyle.
An explanation of the complex data analysis of algorithms and machine learning is beyond the scope of this article.
However, people seem to know intuitively that behind the notifications and personalised recommendations that come every day, there is a type of artificial intelligence that seems to know them better than they know themselves.
For example, those who use entertainment platforms like Netflix, Spotify or YouTube may remember that at the beginning a lot of time was spent actively searching for series, music or videos they were interested in.
But when was the last time that happened? After using these apps for some time, they know one so well one just needs the recommendations made at the beginning.
The content that shows up in someone’s social media newsfeed follows a similar principle.
The posts, news and advertisements that get through are not random, they respond to an algorithm that puts in front of the individual what is most likely to interest them and improve the user experience.
Data matching not only saves a lot of time searching and choosing, it is a tool in terms of macro and micro economy.
How do data matching applications apply to work life?
A freelancer is a provider of independent services who needs to find and be found by one’s target audience.
A successful professional matching could be defined as the construction of a digital and life algorithm that would put one in front of projects that are increasingly more in line with one’s career goals.
Unlike traditional matching, which is primarily based on a user profile and online consumption patterns, data matching as an independent professional has at least two dimensions.
One is the digital footprint and the passive data available, such as the contacts made for each project and the objective quality of one’s work. What one claims to offer and what is actually offered are both factors.
Three essential guidelines for professional matching
1. Become specialised
According to Workana content leader Natalia Werner, a fundamental part of good professional matching is specialisation.
Freelancers are not looking for just “any client” or “any project” — they want to find a project they love, that lines right up with what they are passionate about.
To achieve this, a clear vision is needed and knowing how to communicate what makes an individual different, and ensuring that they stay in a constant state of professional growth to become the best specialists in their field.
2. Leave a human and digital footprint
A digital footprint is everything that leaves a record of one’s online activity. For a freelancer, a professional digital footprint is essential for presence, relevance and authority and should occupy a good amount of one’s attention.
An individual’s profile on platforms such as Workana, their professional profile on social media and the key words of any professional website they have, should be crafted precisely so a client looking for a specialist can find them as easily as possible.
But the work does not stop there. The goal is not only data matching, but results from that matching, which builds client trust. The goal is to consolidate one’s professional algorithm by rehires and permanent recommendations from each satisfied client.
3. Actively seek out your passion
Most people start out in the freelancing world with very general jobs and projects. But, little by little, they start to home in on what they like the most or what they are best at.
Reaching the long-term objective that each of the projects committed to, is exactly what an individual wants, implies constant work and effort to change the “same old projects” to more specialised and better paid projects that they are happier with.
These three recommendations are not isolated, but rather come together to build an algorithm that works behind the scenes to maximise one’s data matching so one can get the best projects.
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