Analytics: Wie die Blockchain bei Predictive Analytics helfen kann

Wie kann die Blockchain bei Predictive Analytic helfen? Schauen wir es uns an.

The success of predictive analytics depends on the size of the dataset being examined. Smaller sets can still produce results, but their accuracy will be limited to high-level assumptions due to a lack of testable points. This problem may seem irrelevant, but in a field that demands precision and actionable insights, it presents a serious limitation for most companies that aren’t on the enterprise level.
The issue is that while data is available, aggregating large enough datasets to significantly increase confidence in predictions isn’t always possible. More important than data amounts, though, are the resources necessary to scrub datasets into usable groups.
Both problems mean that companies require either a dedicated team of data scientists to parse through these sets, or a software suite powerful enough to do so rapidly. For most small and medium-sized businesses, this usually means settling for subpar software, or forgoing it entirely. However, blockchain and its associated capabilities deliver more agile solutions that distribute the technological load instead of centralizing it.
Applications like Golem and Conduit, blockchain-based super computers that derive their processing power from users’ spare computing space, give companies a cheaper and easily accessible solution for their limitations. Now, anyone can access the computational power needed for in-depth predictive analytics without having to purchase expensive components.
Even with technological barriers removed, predictive analytics remains a heavily fenced field. The problem is that users who have little experience in data science or analytics are lost as to where and how to begin using these tools.
Predictive analytics is a complex area of study, requiring understanding of machine learning, advanced statistical analysis and computer programming. Even businesses that use it must employ business intelligence suites or have staff capable of handling the heavier lifting required to generate insights from data sets.
The issue that remains is centralization. This is due to knowledge being the restricting factor and technology not providing a more accessible way to manipulate and explore data sets enough to make accurate predictions. This two-headed problem means that users cannot easily interact with predictive analytics suites. Even if they can, making queries that produce positive results is a complex process. To ask questions, data scientists must be aware of different parameters, variables and best practices for posing queries.
Once again, however, blockchain solutions provide potential answers that could be gamechangers in the field. One of the biggest difficulties regular users experience when dealing with massive data sets is finding the right questions to ask. For data scientists, this usually means finding the right combination of formulae and algorithmic queries to produce results. However, for non-experts, it generally means asking a question in natural language that can provide the same outputs.

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