Blockchain is the Great Equalizer for Predictive Analytics
Data science has taken an enormous stride forward in a relatively brief period of time. The field of data analytics, specifically, has widened with the integration of computer technology, and more recently with the use of machine learning and AI algorithms. The field is now a major component for several industries, including all types of business, healthcare and NGOs. Moreover, with the introduction of predictive analytics, the field’s status as a vital area of study is being further solidified.
Predictive analytics uses historic data and complex algorithms to make predictions of trends, actions and outcomes, and it remains largely the purview of highly trained data scientists. Despite advances in the field, the barriers erected around it by academia and business have remained largely untouched. For laypeople, predictive analytics remains a buzzword. Until recently, it took a degree in statistics, data science and maybe computer programming to fully succeed when using analytics suites.
Even so, new technological forces are slowly tearing down the existing barriers. Blockchain-based startups have leveraged the technology’s many advantages to create predictive analytics suites that resolve two of the largest issues limiting regular users’ ability to enter the field: technological capability and accessibility.
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.
