The next ten years will see continued growth in the data and analytics industry. Faster, more advanced iterative machine learning algorithms (artificial intelligence) will be used for deep learning on massive datasets. In fact, PricewaterhouseCoopers (PWC) predicts A.I. will lead to an increase of up to 26% in GDP for local economies by 2030.
Curated crowd and crowdsourcing, or crowd-aided, analytics will become a major player in big data. In the years and decades to come, the demand for data scientists with a master’s in business analytics or a master’s in data analytics is expected to skyrocket.
A.I. Is Here
The term “deep learning” refers to a subset of machine learning (which is, in turn, a subset of artificial intelligence) that focuses on neural networks. Massive amounts of data are fed through these neural networks, and each element of data is assigned a numeric or true/false value and classified accordingly.
Deep learning is incredibly versatile and has been used by Google, Amazon, and several other internet giants. Self-driving cars, custom-designed medications based on individual genetics, video games, and virtual reality will drive the science of deep learning forward in the years to come.
Google Images uses deep learning to make sense of its enormous, constantly growing image datasets. “With datasets as comprehensive as these, and logical networks sophisticated enough to handle their classification, it becomes trivial for a computer to take an image and state with a high probability of accuracy what it represents for a human,” writes big data expert Bernard Marr in his Forbes article titled “What Is the Difference Between Deep Learning, Machine Learning and AI?”
The Internet of Things (IoT) includes any type of device that connects to another device via a network of some sort. The list of IoT devices includes:
- Fitness trackers (Fitbit)
- Smart TVs
- Security systems
- HVAC thermostats
- Toys and electronics
- Industrial or commercial devices such as POS systems, medical diagnostic devices, engines, turbines, and warehouse inventory equipment
In the near future, we will see more devices, appliances, and tools developed to communicate data via Wi-Fi, Bluetooth, and near field communication (NFC). The data mined from these devices can contribute complex, precise insights to market research analysts and business decision-makers.
Big data will soon optimize the potential of the IoT through descriptive analytics (how devices are currently being used), diagnostic analytics (why did an action take place), predictive analytics (what will happen next, or what will go wrong), prescriptive analytics (what should be done next), and automation of decisions (can this process be automated).
“We’ve got our work cut out for us on IoT decision automation,” says analytics expert Tom Davenport in his Deloitte University Press blog post “Five Types of Analytics of Things.” “We can’t link together our industrial, transportation, energy, and other systems successfully until we’ve figured out the dynamics of complex automated networks.”
The Monetization of Data
Where there is supply and demand, there is inherent value. The supply of data has grown steadily since the dawn of the internet. The demand for data is an unavoidable part of doing business, especially in large corporations.
Now that nearly everyone is connected in some way (even people who don’t own a computer or a smartphone have credit or debit cards, bank accounts, insurance plans, and purchase records), consumer and business data is being produced en masse.
IDC predicts that the world will be producing 180 zettabytes (that’s 180 trillion gigabytes) of data per year by 2025. All of that data, and the insights based on the analysis of that data, are valuable.
“Raw data and various value-added content will be bought and sold either via marketplaces or in bilateral transactions and enterprises will begin to develop methods for valuing their data,” writes Press Managing Partner Gil Press in his 2017 Forbes article “6 Predictions for the $203 Billion Big Data Analytics Market.”
Garbage In, Garbage Out
Perhaps one of the oldest principles of computer science deals with the fact that as computers are defined to perform within certain parameters, that which is output is often a direct reflection of the original programming. In 2016, a Microsoft A.I. chatbot mimicked racist and sexist language based upon conversations gleaned from Twitter users.
This underscores a potentially dangerous precedent: A.I. has shown the potential to discriminate against humans. Time magazine recently detailed how computer algorithms and facial analysis software showed a preference for lighter skin over darker skin. Whether intentional or otherwise, this presents an unacceptable standard that must be addressed in a clear and direct effort.
The result of this virtual bias can have a resounding impact on a large segment of the population, ranging from housing or job applications being denied as well as other forms of racial profiling that intersect the criminal justice system. However, the Harvard Business Review notes that pending legislation — in addition to self-regulation by tech companies — can level the playing field and prevent such issues.
Crowdsourcing and Curated Crowd Analytics
Certain tasks in analytics require human input, which can range from simple to complex. Simple tasks can be crowdsourced, while the more complicated ones require curated crowds.
Curated crowds are composed of a relatively small number of analysts. Participants meet a required level of computer aptitude and are held to stricter guidelines. Curated crowds are expensive because they work full-time hours.
“Major worldwide search engine providers have been using curated crowd solutions for years,” claims crowd-based search solutions expert Ben Christenson in his Analytics magazine article “Crowdsourcing – Using the crowd: curated vs. Unknown.” “Curated crowds are used for search engine evaluation, local search result validation, query classification, spam identification, and countless other tasks.”
Christenson also notes, “these search engine providers gather high-quality data they can trust to accurately measure the success of their current algorithms, compare their search engine against competitors, and test out new iterations before launching.”
When tasks are simple, though, crowdsourcing becomes an option. A data analytics company can offer thousands of participants a small fee (sometimes as low as a few cents for each response) on an online crowdsourcing marketplace. A crowdsourced task could be as simple as determining which pictures look like a cat and which ones don’t. The answers help a neural net learn how to identify pictures of cats without human input.
In the future, both curated crowds and crowdsourcing will be increasingly necessary because the massive amounts of data produced will require human input to hone deep-learning algorithms to learn faster and deliver more pertinent results.
Prepare to Meet the Rising Demand for Data Analysts
As raw data increases exponentially over the next decade and beyond, the need for data scientists and business analytics experts will also increase. Students graduating from college in the next few years with training in analytics, data warehousing, data mining, visualization, and machine learning will be in high demand in nearly every industry.
Maryville University’s online master’s in business data analytics (MSBDA) program uses traditional educating methods combined with a cutting-edge online campus technology. It also provides the training needed to help improve results for marketing research departments and elevate the quality of corporate decision-making.
Career professionals who are looking to improve their position in their company can also benefit from an MSBDA, especially if they work in a field where data science skills affect marketing data analysis through strategic insight.
Your master’s degree can be completed while continuing full-time work. Get started today on the next phase of your education and career.