What is data enrichment with AI: 3 scenarios and a case for proxies
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The best way to start this article is to repeat that data is the new gold. Online harvesters and scrapers work, day and night, to extract countless datasets on the basis of geo targeted proxies provided by trusted proxy websites. It happens for a reason. Those businesses that find legit ways to grab, process, and structure enormous flows of info from different sources, are lucky to make informed decisions and build insightful projections. Such a factor gives them potent competitive leverage. But what if in this race for success regular amounts of data are not enough? The only option is to rely on even more data, accurate and granular down to the ground truth, to outcompete rivals. That is when data enrichment comes to the rescue.
The phenomenon of data enrichment refers to enhancing already gathered files with relevant extra details stemming from additional poles. Companies vote for this opportunity with money: around 2030, this segment will reach $3.5 billion in comparison with $1.7 billion in 2021. This growth is not surprising.
Teams win a lot with proper data enrichment, as it reveals lucrative horizons:
- Laser-precise marketing campaigns.
- Facilitated lead scoring.
- Comprehension of insurance and lending risks at the stage of initial underwriting.
- Improved customer experience, and more use cases across the economy.
Now let’s examine how data enrichment magic happens.
First, primary datasets are generated. It might be in-house privately-owned data, or publicly available data to collect via geo targeted proxies and trusted proxy websites. Then, supplements arrive.
Previously, possible enhancement points were limited to two options:
- Either amalgamating data from in-house storage with your own auxiliary data from other internal systems.
- Or trying to find an external data range to mix it with the existing first-party one.
There is a third way now, AI-fueled data enrichment. It is not about smart recognition and automated comparisons anymore. The progress is beyond that.
Having a sufficient amount of facts at its disposal to identify patterns, artificial intelligence can, literally, close gaps and propose missing pieces. That is, it creates data that hasn’t existed before, but that is universally close to truth. One can denote this phenomenon as ML-predictions.
Say, an advanced algorithm trains on an extended dataset in a particular field. Its task is to assess all specific regularities and trends. While doing so, it recognizes the hidden multi-layer logic behind heterogeneous big numbers.
Assume, it is subsequently fed a flawed data range after that. Modern solutions are capable of working with it across three dimensions:
- In case there are absent pieces in the dataset, AI augments it with lost values.
- As long as the range is not long enough, artificial intelligence can continue it, foreseeing the most likely scenarios.
- Also, an up-to-date cybermind is already able to take a separate data unit from the set and, with a high degree of certainty, add extra properties to it.
Imagine a payment transaction record. Normally, it contains the sender, the receiver, and time of payment. But if it is a line from a large cluster of transactions, AI can propose who payers are in social terms, how often they make resembling transactions, and what are their spending habits.
The key conclusion to draw from data enrichment techniques based on AI is as follows: soon, there will be no secrets left in this world. A single puzzle will suffice to get an entire picture.
To live in this world, ever greater data ranges will be required to train AI. Thus, proxies for data collection remain a must. Dexodata, as a trusted proxy website, offers geo targeted proxies to extract your volume of data and train your computer brain. A free trial is available for newcomers.