Maryville Online

Maryville Resources

Articles

Business Analytics: Looking Toward The Horizon

If the past decade could be summed up in two words, “Big Data” would probably be the most apropos response. According to International Data Corporation’s (IDC) 2016 press release, the revenues amassed by big data and business analytics worldwide will rise from around $130 billion in 2016 to more than $200 billion by 2020.

The data industry has proven to be incredibly useful in the world of business intelligence. The insights provided by business analytics are of enormous value to marketing departments and company executives. Technology has finally become powerful enough to make artificial intelligence a valuable, productive reality.

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.

The Internet of Things (IoT) will increase in scope and contribute beneficial, relevant data sets.

Standards of assigning monetary value to data will begin to emerge, resulting in standardized data monetization.

Curated crowd and crowdsourcing, or crowd-aided, analytics will become a major player in Big Data.

And finally, the demand for data scientists and people with a masters in business analytics or a masters in data analytics is going 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 behemoths. Self-driving cars, custom-designed medications based on individual genetics, and even video games 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 2016 Forbes article “What Is The Difference Between Deep Learning, Machine Learning and AI?”

IoT Revolution

The Internet of Things, better known as IoT, includes any type of device that connects to another device via a network of some sort. Among the IoT are fitness trackers (Fitbit), automobiles, smart TVs, security systems, HVAC thermostats, refrigerators, children’s toys, and even industrial and commercial devices such as POS systems, medical diagnostic devices, engines and turbines, and warehouse inventory equipment.

In the near future, we will see more devices, appliances, and tools developed to communicate data via WiFi, 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.

In the future, Big Data will 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 2016 Deloitte University Press blog post “Five Types of Analytics of Things,” referring to the difficulty of incorporating IoT data into Big Data’s purview. “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. In 2015, only ten zettabytes of data was produced. 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 gPress Managing Partner Gil Press in his 2017 Forbes article “6 Predictions For The $203 Billion Big Data Analytics Market.”

Crowdsourcing And Curated Crowd Analytics

Certain tasks in analytics require human input, which can range from simple to complex. Simple tasks can be outsourced via crowdsourcing 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 know-how 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. Curated crowds are used for search engine evaluation, local search result validation, query classification, spam identification, and countless other tasks,” claims crowd-based search solutions expert Ben Christenson in his Analytics Magazine article “Crowdsourcing – Using The Crowd: Curated Vs. Unknown.”

“These search engine providers gather high-quality data they can trust to accurately measure the success of their current algorithms,” Christenson says, “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.

Rising Demand For Data Analysts and Maryville University Online

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 of Science in Business Data Analytics program provides the training required to improve the outputs of marketing research departments and raise the quality of corporate decision-making. Career professionals who are looking to improve their position within their company will benefit from an MSBDA, especially if they work in a field where data science skills affect marketing data analysis.

Maryville’s online program uses traditional educating methods combined with cutting-edge online campus technology. Masters degree education can be completed while continuing full-time work. Contact Maryville University today for details.

Sources:
Double-Digit Growth Forecast For The Worldwide Big Data And Business Analytics Market Through 2020 Led By Banking And Manufacturing Investments, According To IDC – https://www.idc.com/getdoc.jsp?containerId=prUS41826116

What Is The Difference Between Deep Learning, Machine Learning and AI? – https://www.forbes.com/sites/bernardmarr/2016/12/08/what-is-the-difference-between-deep-learning-machine-learning-and-ai/#4b8581b426cf

Five Types Of Analytics Of Things – https://dupress.deloitte.com/dup-us-en/topics/analytics/five-types-of-analytics-of-things.html

Six Predictions For The $203 Billion Big Data Analytics Market – https://www.forbes.com/sites/gilpress/2017/01/20/6-predictions-for-the-203-billion-big-data-analytics-market/#3d53adf42083

Crowdsourcing – Using The Crowd: Curated Vs. Unknown – http://analytics-magazine.org/crowdsourcing-using-the-crowd-curated-vs-unknown/