Big Data and Artificial Intelligence (AI) are common conversation topics in IT circles nowadays. Initially, these buzzwords were just a couple of the many doing the rounds and adding to the confusion in organizations trying to stay relevant in a world where technology is changing as fast as worker expectations. Big Data and AI are often treated as separate entities, but are they truly different? They each deserve a spotlight in conversation but building a bridge between these two concepts can be more beneficial to businesses’ instead of constructing a wall that separates the two.

Let’s try and define what Big Data and AI represent. Big Data is simply, as it sounds, large amounts of data. This includes structured and unstructured data that is, as Gartner[1] puts it, high-volume, high-velocity and/or high-variety information assets. This data is unactionable, unless this information is processed to reveal valuable insights related to behaviour, patterns and trends that inform business decisions. Traditional techniques are unable to compute such large amounts of data to deliver understandable and actionable informational insights. This is where AI comes in. Artificial Intelligence (without getting into the mechanics of Machine Learning, Deep Learning or Neural Networks) is a combination of algorithms that enable machines to process large amounts of data that produce information that is relevant in a business’s context. AI allows machines to “think” like humans, act on the data and react to the context that is added as an input to produce results that business leaders can understand and act on. This structured information that is (sometimes) predictive in nature, reveals trends, patterns and behaviours that can then be used to develop actionable and tactical tasks to meet business objectives.

A survey conducted by NewVantage Partners of C-level executives, about Big Data and AI[2], found that 76.5% of these executives feel that AI and Big Data are becoming closely interconnected. Organizations can use available data to improve AI-related initiatives that empower business decisions. So, why are Big Data and AI treated as separate entities, when they seem to be natural enablers of each others’ capabilities? Most probably, because they are different tools with varying complexities, which allow businesses to achieve diverse insights to achieve their company vision.

To better understand the correlation between Big Data and AI, it is important to first understand how they are different. Alan Morrison, senior research fellow with consulting giant PwC, in a Datamation[3] article, highlights that Big Data is the raw input of information that needs to be structured and cleaned, whereas AI is the output of understandable and usable information resulting from processed data. This is a major differentiator that makes these two entities innately different.

Another significant difference between Big Data and AI is the type of computing style used to process information. The type of computing to process Big Data is old school, where the system looks for certain types of information or defined insights. Big Data machines cannot act or react to the results that these data and insights produce. For example, online ads that follow you around on various digital channels, based on your search behaviour. Whereas, AI is a form of computing that allows machines to be programmed such that they can intelligently act and respond to the results that structured information produces. The updates to code and upgrades to machines allow for these systems to “learn” and modify their behaviour through “learning”, which allows these systems to modify their reactions and results depending on changes found within informational data sets or patterns. A machine with AI can analyze, interpret and provide solutions to address identified challenges, based on its interpretation. These machines can then store the results of what works best to address certain types of challenges, while continuing to fine tune solutions to address other challenges that are analyzed to have different patterns. For example, self-driving cars that study human behaviour and road conditions to try and make decisions related to appropriate driving methods and driver safety.

Also, Big Data and AI have differences in use. Big Data is used to find insightful patterns about consumer habits and behaviour. This is the same technology Spotify or Netflix use to serve music genres or movie content that is aligned to what a person has consumed previously. Similar people selecting to listen to or watch similar content will be served similar recommendations of programs. AI, on the other hand, is a decision-making tool that processes information faster and with reduced errors compared to humans. It equips leaders with information and learnings, based on previous patterns and results, to help design the next steps in business growth with a higher success probability.

Understanding the differences between Big Data and AI allow us to better comprehend how these two entities can work together to achieve defined business objectives. Big Data and AI complement each other because of one primary reason – AI needs Big Data to learn!

AI requires data, especially when machine learning is involved, so that it can structure and analyze the information to create patterns that it can understand and recognize. Big Data needs to be cleaned, de-duped and unnecessary information removed before AI can come into play. This “clean” data can then be converted into flexible learning algorithms that is fed into machines. Then, the machine is “trained” on how to process the data fed into it for desired responses, output and results. After the initial training process, the machine gathers, organizes and analyzes the new “clean” incoming data on its own and continues to “learn” and adjust its behaviour based on identified data changes. For example, Amazon’s Alexa, Microsoft’s Cortana, Apple’s Siri and Alphabet’s Google Assistant – all use voice recognition to record what a person is requesting and, based on pre-fed algorithms and responses, finds the most appropriate response to fulfill the request. These programs keep learning the needs of their user and become faster in response times based on analysis of repetitive behaviour and requests. AI allows machines to continuously learn from new data it continuously collects. Forbes[4] has listed 27 impressive examples of AI and Machine Learning that are practiced today.

Businesses can use machine learning processed information to define actionable tactics that align with their business goals. With the world of IT changing so fast, read about the 3 ways CIOs can drive growth and innovation, here.


[1] Gartner –