Merging data analytics with AI for more accurate business forecasts

Why using AI for data analytics is key to developing more informed and intelligent reviews and analyses

4Jul

The ongoing proliferation of Big Data means that across almost every external or internal business touchpoint, there are insights to be gleaned, results to be captured and trends to be followed. As such, it can be overwhelming for even the most robust organization to accurately acquire, organize and analyze it all. In fact, new research shows that 85% of Big Data projects fail, chiefly because applying modern analytical practices to existing infrastructure and getting all teams on board with the process is a greater challenge than many companies are equipped to support.

Thanks to new digital tools such as customer relationship management (CRM) systems, leaders are able to better sort through the influx, though optimizing it remains a challenge. That’s where artificial intelligence (AI) comes in. Due to its ability to replicate human reasoning patterns and behavior, this technology has proven a valuable addition to traditional data analytics programs and is changing how analysts around the world draw conclusions from the information they daily intercept.

How AI stands to improve modern data analytics

Two of the major downfalls of manual data analytics is that the task can quickly become exhausting and mundane. Sorting through customer feedback for hours might be interesting at first, but after reviewing the 100th client survey, an employee might be feeling more than a little burnt out. Yet, AI technology plays an instrumental role here, as it’s able to analyze data continuously, without growing tired or bored in the process. That means that the last survey is analyzed with the same level of vigor and attention to detail as the first.

Moreover, AI technology is also better equipped to handle significant amounts of data. It does so by strengthening its intelligence as it “learns” the process. So, the more data you feed the machines, essentially the smarter they become, understanding cause and effect patterns to more accurately and quickly deliver results.


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Benefits for the B2C and B2B communities

So far, the implementation of AI in data analytics has centered chiefly on B2C companies, primarily due to the amount of customer information that is relayed through these channels. For instance, machine learning applications are currently used to perform customer support and troubleshooting functions. They can understand the buying patterns and preferences of existing employees and help guide them toward similar products, remediate any issues with their current purchases, and deliver personalized marketing messages tailored to their needs.

The benefits of this integration within the B2C sphere are wide-reaching. Employees are less stressed and overworked as some tasks are outsourced to AI machines, customers receive the answers they need quicker, and companies can experience a reduction in customer service costs as a result.

Yet, there is a case for AI-driven data analytics within the B2B community as well. The need to understand partner preferences and deliver on expectations is a top initiative across myriad B2B industries and this robust level of information gathering and processing can only be strengthened by the addition of AI practices including predictive modeling and machine learning.

What to expect from AI-driven analytics moving forward

As more companies realize the power and potential of AI to transform their existing data analytics programs, the future is ripe for its application across almost every industry. Ultimately, a better-informed workforce means a more risk-aware and cost-effective organization. It also means these forward-facing leaders are able to deliver a more consistent and rewarding customer or partner experience, and that’s the kind of service that drives the positive, actionable feedback this cycle depends upon. 

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