The world has accumulated large volumes of big data, yet it’s difficult to draw conclusions from it. In some cases, information is poor-quality, highly diversified, and unrepresentative. In other cases, there is not enough high-quality data.
The third category has misinterpreted information. Additionally, collecting, storing, and processing big data consumes a lot of energy and many other resources to meet stringent regulatory requirements. As a result, the use of data becomes less efficient.
The current situation has led to a more selective approach to collecting, processing, and storing data, as well as an increased demand for specialists who can search for information on specific scenarios and predict their development.
What will happen next?
An entity will hold a monopoly on big data — a centralized data storage and processing system with information about everything and everyone, like Skynet.
Specialization in data processing will intensify. Instead of buying huge amounts of raw data, client companies will buy already identified signals relevant for a certain scenario — for example, signals that a person started traveling outside their neighborhood or downgraded in terms of stores where they make purchases. At the same time, computational techniques that increase data confidentiality will find wider adoption.
Death of cookies and other identifiers
Large ecosystems like Apple do not want to share data, so they restrict access to it.
Any information exchange requires secure channels.
Legal requirements limit companies' ability to work with data.
Synthetic data reflects patterns but does not contain personal information. The method is used when there is not enough available data for modeling. In this case, real information is anonymized and used to create additional, synthetic data.
The platform can generate the required volumes of anonymous data from generative models in just a few minutes while reducing the risks associated with poor-quality information.
The new generation of data processing technology helps to track and automate operations with incorrect information — for example, duplicate records or broken panels. Such products increase the quality and credibility of the information companies collect.
Targeted search and data processing can improve the business models of specialized fintech solutions. For example, to predict SaaS product sales, you need to score SaaS companies based on their cash flows.
Fraudulent transactions are rare events, so quantitative methods are not suitable for their analysis. More accurate, specialized data allows you to create solutions to prevent a specific type of fraud.