Big Data and Artificial Intelligence: How They Work Together

The world was entrenched in big data before it even realized that big data existed. By the time the term was coined, big data had accumulated a massive amount of stored information that, if analyzed properly, could reveal valuable insights into the industry to which that particular data belonged.

IT professionals and computer scientists quickly realized the job of sifting through all of that data, parsing it (converting it into a format more easily understood by a computer), and analyzing it to improve business decision-making processes was too much for human minds to tackle. Artificially intelligent algorithms would have to be written to accomplish the enormous task of deriving insight out of complex data.

Data professionals and those with a master’s in business analytics or data analytics are expected to be in demand as corporations broaden their big data and artificial intelligence capabilities in the coming years. The objective is to catch up to, and leverage, the amount of data being produced by all of our computers, mobile smartphones and tablets, and Internet of Things (IoT) devices.

Big data is most assuredly here to stay at this point, and AI (artificial intelligence) will be in high demand for the foreseeable future. Data and AI are merging into a synergistic relationship, where AI is useless without data, and mastering data is insurmountable without AI.

By combining the two disciplines, we can begin to see and predict upcoming trends in business, technology, commerce, entertainment, and everything in between.

The internet now provides a level of concrete information about consumer habits, likes and dislikes, activities, and personal preferences that was impossible a decade ago. Social media accounts and online profiles, social activity, product reviews, tagged interests, “liked” and shared content, loyalty/rewards apps and programs, and CRM (customer relationship management) systems all add potentially insightful data to the big data pool.

Regardless of the industry, one of AI’s greatest assets is its learning ability. Its capacity to recognize data trends is only useful if it can adapt to changes and fluctuations in those trends. Through identifying outliers in the data, AI knows what pieces of customer feedback are considered significant and can adjust as necessary.

AI’s ability to expertly work with data analytics is the primary reason why artificial intelligence and big data are now seemingly inseparable. AI machine learning and deep learning are pulling from every data input and using those inputs to generate new rules for future business analytics. Problems arise, however, when the data being used is not good data.

According to Forbes, the most recent research indicates that a combination of AI and big data can automate nearly 80% of all physical work, 70% of data processing work, and 64% of data collection tasks. This suggests that the two concepts have the potential to tremendously affect the workplace, in addition to their contributions to marketing and business efforts.

Fulfillment and supply chain operations, for instance, are both particularly reliant on data, so they’re turning to the developments within AI to provide real-time insights on customer feedback. Through this, businesses can form their finances, strategies, and marketing around the flow of new information.

Essentially, there must be an agreed-upon methodology to data collection (mining) and data structure before running the data through a machine learning or deep learning algorithm. This is where professionals with degrees in business data analytics come in. They will be highly prized by companies that are serious about getting the most out of their data analytics.

AI and big data can work together to achieve more. First, data is fed into the AI engine, making the AI smarter. Next, less human intervention is needed for the AI to run properly. And finally, the less AI needs people to run it, the closer society comes to realizing the full potential of this ongoing AI/big data cycle.

That evolution will require the involvement of human beings who are trained in data analytics and AI algorithm programming.

According to software company XenonStack, the ultimate goals of AI are as follows:

For these AI fields to mature, their AI algorithms will require massive amounts of data. Natural language processing, for example, will not be possible without millions of samplings of human speech, recorded and broken down into a format that AI engines can more easily process.

Big data will continue to grow larger as AI becomes a more viable option for automating more tasks — and AI will become a larger field as more data is available for learning and analysis.

The demand for business analytics experts lies at the heart of Maryville University’s online Master of Science in Business Data Analytics degree. Graduates of this online program can gain the skills to enter the workforce as statisticians, data scientists, data analysts, or actuaries.

At Maryville University, students can learn how to handle data sets, orchestrate multiple infrastructures, monetize data, and make decisions based on valuable analytics insights. Graduates will be exposed to the training and knowledge to combine business operational data with the latest analytical tools, making them invaluable to employers.

Recommended Reading

Big Data and Data Mining: The Role Data Mining Plays in Big Data

The Future of Data Science and Important Skills for Data Scientists

Key Careers in Data: Comparing Data Analyst vs. Data Scientist vs. Data Engineer Professions

Sources

Forbes, “Utilizing AI and Big Data to Reduce Costs and Increase Profits in Departments Across an Organization”

Forbes, “The 4 Biggest Trends in Big Data and Analytics Right for 2021”

Information Age, “Top 10 Data and Analytics Trends Revealed by Gartner”

Xenonstack, “Artificial Intelligence Overview and Applications”