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The Relationship Between AI and Data




Some may not realize it, but data and artificial intelligence have long been intertwined, with one contributing to the other’s development and vice versa. And together, both have advanced technology in ways one can never imagine.


First, AI has been able to achieve significant strides in recent years thanks largely to the data it has been able to pull up to perform tasks. In return, AI has made it possible for data to be collected at a much faster and more automated pace, gathering greater insights than before, among other benefits which we shall detail a bit later.


And as companies are looking to maximize the potential of their big data assets, the use of machine learning algorithms for big data is the next logical step. Machine learning systems use data-driven algorithms and statistical models to analyze and find patterns in data. In turn, big data provides the raw material from which machine learning systems can derive insights. Many organizations are now realizing the benefit of combining big data and machine learning.


Machine learning, in particular, provides considerable value to big data applications by deriving higher-level insights from big data. Machine learning systems have the ability to learn and adapt over time without following explicit instructions or programmed code. This, combined with deep learning capabilities, has enabled companies to have systems learn on their big data, improving decision-making, business intelligence, and predictive analysis over time.


How AI benefits big data


AI, coupled with big data, is impacting businesses across a variety of sectors and industries. some of the benefits include the following:


360-degree view of the customer - Companies used to move data into and out of data warehouses and create static reports that take a long time to generate and even longer to modify. Now, smart organizations are utilizing distributed, automated, and intelligent analytics tools that sit on top of data lakes designed to collect and synthesize data from disparate sources at once. This is transforming the way companies understand their customers.


Improved forecasting and price optimization - Traditionally, companies base their estimate of the current year's sales on data from the prior year. However, factors such as changing trends and unforeseen events such as a global pandemic and inflation have made it more difficult to do with traditional approaches. Big data is giving organizations the power to spot patterns and trends early and know how those trends will impact future performance. Companies using big data and AI-based approaches, especially in retail, are able to improve seasonal forecasting, reducing errors by as much as 50%.


Improved customer acquisition and retention - With big data and AI, organizations have a better handle on what their customers are interested in, how products and services are being used, and reasons why customers stop purchasing or using their offerings. Through big data applications, companies can more accurately identify what customers are really looking for and observe their behavioral patterns, which they can apply to improve products, generate better conversions, improve brand loyalty, spot trends earlier, or find additional ways to improve overall customer satisfaction.


Cybersecurity and fraud prevention - Big data systems have the power to comb through very large quantities of data from transactional or log data, databases, and files to identify, prevent, detect, and mitigate potential fraudulent behavior. These systems can also combine a variety of data types including both internal and external data to alert companies to cybersecurity threats that haven't yet shown up in their own systems.


Identifying and mitigating potential risks - Big data provides early visibility to potential risks, helping to quantify the exposure to risks and potential losses and expedite changes. Big data-powered models are also helping organizations identify and address customer and market risks as well as challenges emerging from unpredicted events such as natural disasters. Companies can digest information from disparate data sources and synthesize the information to provide greater situational awareness and understanding of how to allocate people or resources to deal with emerging threats.


The envisioned future and immediate challenges


In the future, it is expected that businesses will leverage machine learning, big data, visualization tools, and analytics to enhance their decision-making processes by analyzing raw data. The availability of big data plays a crucial role in enabling personalized experiences. It is evident that companies lacking the integration of big data and AI will struggle to fulfill their digital transformation requirements and risk falling behind.


It is therefore critical for companies to continue building on their capabilities by combining the power of machine learning, big data, visualization tools, and analytics to help their businesses. This will benefit them not only in the relationship between more sound decision-making through the analysis of raw data. But also to deliver a personalized experience for their clients and customers which will serve to boost their bottom line.

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