Reducing Big Data project failures with AI

Reducing Big Data project failures with AI

Two years ago,  Nick Heudecker, who is an analyst, estimated that the failure rate of big data projects was approximately 85 per cent. But as time passes by a growing number of experts are concluding that artificial intelligence has the potential to turn big data failing projects into success stories. But for that, we must know how to correctly use AI.

AI can be applied to big data projects whenever the standard predictive analytics incorporates many variables, making models cumbersome to optimize and slow to run. You must keep the following points in mind while applying the knowledge of AI to big data projects:

Choose the tasks you automate wisely

According to David Autor, an economist at MIT, artificial intelligence is automating routine cognitive processes very quickly, much like the industrial era machines automated by physical labour.

A study cited by white house administration, during the Obama era looked at the error rates of both machines and humans when it came to correctly reading radiology images. The study found that machines had a 7.5per cent error rate; a figure over double that of humans which stood at 3.5 per cent. However, when both machines and human efforts were combined the error rate dropped to only 0.5 per cent — a significantly lower figure than either party alone. 

Though clearly impressive, we’d have to consider the additional time required for both, as opposed to just one, reading the images, weighing up whether the additional time (and expense) is worth the reduction in error rates.  

Choose the tools for big data analytics carefully

There are different tools available for the tasks that you may want to perform, but each of these tools is different. You must first analyse your data to access which platforms or software to use. Once you are done doing that, these are some of the points that you may keep in mind while making a selection.

  • Ease of Use
  • Ease of Integration
  • Total Costs(Software/Hardware/Operational)
  • Technical Expertise Required
  • Data Permission
  • Data Preparation
  • Time to Deployment
  • Supported Data
  • Budget 

Ensure that security is designed into every facet of big data analytics

You must keep data from getting corrupted which means the error in computer data that may arise during reading, writing, or processing. You can use certain systems, processes, and procedures that keep the data inaccessible to others who may use it in harmful or unintended ways. These breaches in data can cause serious damage to the company. Therefore it is extremely important to take these measures.

Shifts humans to high-value tasks

With time, AI will be making decisions in industrial settings. One industry which has already begun to benefit from the use of AI is the financial sector. Already, the industry is investigating ways to automate decisions will be made responsible for tasks like approving loans, identifying corruption and financial crimes. This will shift the focus of humans from these boring tasks to higher-value tasks which can’t be accomplished by AI.

Artificial intelligence will change the world one way or the other, and it is highly important for companies to learn to keep up with the rest of the world. We hope this article helps you in learning about AI. 

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