The world was already 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 data that, if analyzed properly, would reveal valuable insights into the industry to which that particular data belonged.
IT professionals and computer scientists quickly realized that the job of sifting through all of that data, parsing it (converting it into a format more easily understood by a computer), and analyzing all of it for purposes of improving 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 chaos.
Data professionals and those with a masters in business analytics or a masters in data analytics are expected to be in demand as corporations broaden their data analytics and AI capabilities in the coming years to catch up to the amount of data being produced by all of our computers, mobile smartphones and tablets, and Internet of Things (IoT) devices.
How AI Is Used In Big Data
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.
“Using data from multiple sources, AI can build a store of knowledge that will ultimately enable accurate predictions about you as a consumer that are based not just on what you buy, but on how much time you spend in a particular part of a site or store, what you look at while you’re there, what you do buy compared with what you don’t – and a host of other bits of data that AI can synthesize and add to, ultimately getting to know you and what you want very, very well,” according to Umbel in its white paper, “AI Meets Big Data.”
AI’s ability to work so well with data analytics is the primary reason why AI and Big Data are now seemingly inseparable. AI machine learning and deep learning are learning 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.
“The primary challenge [for AI] is and will always be the data,” explains Forrester Research analyst Brandon Purcell in tech writer David Weldon’s interview, “Artificial Intelligences: Fulfilling The Failed Promise Of Big Data” on Information-Management.com.
“Data is the lifeblood of AI. An AI system needs to learn from data in order to be able to fulfill its function. Unfortunately, organizations struggle to integrate data from multiple sources to create a single source of truth on their customers. AI will not solve these data issues – it will only make them more pronounced.”
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. Professionals with degrees in business data analytics will be highly prized by companies that are serious about getting the most out of their data analytics.
The Melding of AI and Big Data
Big Data is most assuredly here to stay at this point, and because Big Data isn’t going away anytime soon, AI 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 data is insurmountable without AI.
“There are vast amounts of enterprise data in various organizational silos as well as public domain data sources,” says AI and cyber security reporter Nick Ismail in his Information-Age.com article, “Access To Data Will Be The Key Enabler As Artificial Intelligence Comes Of Age.”
“Making connections between these data sets enables a holistic view of a complex problem, from which new AI-driven insights can be identified.”
AI is becoming a cyclical, ongoing process with Big Data, Ismail explains. 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, the closer society comes to realizing the full potential of this ongoing AI/Big Data cycle.
But before AI and Big Data can truly evolve to the level we’ve seen in (some of the more sensible, less apocalyptic) science-fiction stories, several other technologies will need to evolve first, and that evolution will require the involvement of human beings trained in data analytics and AI algorithm programming. According to XenonStack’s Hackernoon.com post, “Overview of Artifical Intelligence And Role Of Natural Language Processing In Big Data,” the following are the ultimate goals of AI:
- Automated learning and scheduling
- Machine learning
- Natural language processing (ability to understand human speech as it is spoken)
- Computer vision (ability to extract accurate information from an image or series of images)
- General intelligence
For these AI fields to mature, the 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 is going to continue to grow larger as AI becomes a viable option for automating more tasks, and AI will become a bigger field as more data is available for learning and analysis.
Maryville University’s Master Degree In Business Data Analytics
The demand for business analytics experts lies at the heart of Maryville University’s online Master’s of Science in Business Data Analytics degree. Graduates of this online degree 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 they will need to combine business operational data with the latest analytical tools, making them invaluable to employers.