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According to Cisco Systems, Inc., annual global IP traffic surpassed the zettabyte mark (1 trillion gigabytes) in 2016. And all predictions point to global IP traffic surpassing 2 zettabytes by 2020. Such an enormous amount of data, when analyzed correctly, will provide valuable insights for businesses, consumers, government agencies, and research institutes.
As Big Data grows at this exponential rate, the human resources and technology used to aggregate, parse, and analyze data will be hard pressed to keep up. Furthermore, the advances in machine learning (artificial intelligence) algorithms, the Internet of Things (IoT), and cloud technology are rapidly expanding the field of business analytics as well, and the sheer amount of data still outweighs our ability to analyze it all.
If business intelligence and analytics are to benefit from all of the data being mined, corporate decision-making needs to begin favoring up-to-date analytics technology. Business executives and managers will need relevant, up-to-date Executive MBA training with a focus on analytics if they are going to have the knowledge and experience necessary to make the right data-based decisions for their companies in the future business environment.
Artificial Intelligence Grows
Modern artificial intelligence (AI), essentially a neural net tasked with accumulating data and learning from it, involves deep machine learning.
Bernard Marr, analytics author and consultant, defines modern AI in his Forbes article, “What Is The Difference Between Deep Learning, Machine Learning, and AI?” According to Marr, deep learning refers to massive data sets being fed through logical, neural networks. The networks ask questions of each data element that can be answered either as true or false, or as a numerical value. The derived value is then applied to the values of other data. Over time, learning takes place that will then be applied to further learning, and so on. Thus, the results of deep learning will evolve steadily over time, starting with simple tasks.
The initial benefits of AI algorithms are being seen in the business world in simpler administrative tasks, such as shift scheduling and report writing. Shift scheduling benefits from AI by using machine-learning algorithms to optimize the schedules of hundreds of workers while decreasing labor costs. Report-writing algorithms scour multiple data sources to self-populate blank fields in business reports, ultimately making managers more efficient at their jobs.
Analytics systems also organize customer data in easy-to-understand visualizations that free up managerial time for making decisions. Managers will spend less time trying to wrap their heads around complex numbers, allowing them to devote more time to the more humanistic considerations, such as the ethical ramifications of business decisions.
Simulations, search and discovery activities, and financial and investment research are other areas where AI can be extremely helpful as a type of unbiased counselor or team member.
“[A.I. systems allow] investment managers to ask investment-related questions in plain English, such as, ‘Which sectors and industries perform best three months before and after a rate hike?’ and receive an answer within minutes. Picture how such technologies could support individuals and teams of managers in assessing decision consequences and exploring scenarios,” say Vegard Kolbjørnsrud, Richard Amico, and Robert J. Thomas of next-generation investment analytics in their Harvard Business Review article, “How Artificial Intelligence Will Redefine Management.”
Business executives and managers need to embrace the spirit of creative thinking, imagination, experimentation, and design if they are going to stay on top of AI technology and use it to make their management efforts more efficient and beneficial. A new role, the manager-designer, may soon emerge for those capable of integrating multiple ideas in new ways because of the time-saving advantages of AI and analytics.
The Analytics Of The Internet Of Things
Executive or managers must be capable of effectively utilizing every possible resource available to them. The Internet of Things (IoT), which refers to things that connect and share data with a larger system via WiFi, Bluetooth, Near Field Communication (NFC), or wired connection, includes everything from smart TVs to security systems, toys, POS systems, HVAC thermostats, and warehouse inventory equipment. In the eyes of a practical, efficient manager or executive, the connectivity of IoT is an opportunity to collect useful data that, in turn, can improve business operations and marketing campaigns.
Making sense of the massive amounts of data provided by IoT, and using that data in a productive way, requires several different types of analytics:
• Descriptive analytics: analytics that keep track of usage data.
• Diagnostic analytics: analytics that attempt to determine why a particular incident took place.
• Predictive analytics: analytics that attempt to guess what will happen next based on current data.
• Prescriptive analytics: analytics that advise what should be done next to avoid or ensure a particular outcome.
With the massive amount of data, and these four types of IoT analytics to examine it, conducting business will eventually require more automation. “Given the need for human attention from other types of analytics, and the vast amounts of data that will be generated by the IoT, automation of decisions and actions is an obvious direction for the field,” claims analytics expert Tom Davenport in his Deloitte University Press blog post, “Five Types of Analytics of Things.”
“There will be way too few humans to make decisions on all the data and analyses coming from the IoT,” he says, “so we’re going to have to automate many processes involving it.”
Thus, the amount of analytics involved with IoT is staggering. For example, smart TVs generate endless streams of data on TV usage, volume settings, channels/programs watched, busiest times of day for TV usage, and casting (for instance, Chromecast or Amazon Fire Stick) behavior to name just a few.
As IoT grows, “edge analytics” are expected to become more necessary and present in decision-making. Edge analytics, or distributed analytics, refers to analytics that are completed locally (to data sources), rather than at larger, regional data warehouses. The advantage of edge analytics is that it can more evenly distribute the enormous workloads of analyzing IoT data.
Edge analytics will ultimately simplify the process of business executives customizing business services to local zones in real-time. For example, imagine a streaming service that sees a sudden surge in social media postings about a local high school football game. The service finds a live feed of the game and immediately pushes the live feed to local users.
Advancements In The Cloud
Edge analytics, however, only accounts for a small percentage of the overall cloud. The majority of the cloud continues to grow in size and scope as more enterprises turn to remote servers and data centers for storage and powerful analytics resources. Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and IBM Cloud currently lead the pack in the global public cloud market, which is experiencing a 22 percent annual growth rate.
Despite the cloud’s growth, the demand for enterprise cloud services (cloud servers for business clients who require massive data storage solutions with quick access) is outgrowing the supply.
Smaller local servers may remedy this problem by enabling edge analytics and custom services designed for specific businesses or industries. Larger centralized servers, on the other hand, have to accommodate every kind of business and industry with more generic services that have to fit a wide variety of potential needs. Because of this difference, business executives are beginning to consider the use of multiple cloud providers to meet all of their companies’ needs.
However, the practice of leveraging multiple cloud providers and services will get expensive quickly. To alleviate cost issues, executives and managers can take advantage of a variety of cost management tools (some available from cloud providers such as AWS) and carefully monitor usage and consumption through analytics available to them. In fact, the business world has recently instituted an executive position specifically tasked with cloud management, as well as analytics and information systems.
Executives who handle information systems are generally referred to as Chief Information Officers, or CIOs. Due to the constantly changing dynamics of business analytics, cloud technology, learning algorithms, and IoT, the job responsibilities of CIOs are challenging and constantly evolving.
“CIOs who initially elected to build private clouds may find themselves switching to public clouds as they realize just how time-consuming and costly the work will prove,” writes IDG News Service Senior Writer Clint Boulton in his CIO.com article, “6 Trends That Will Shape Cloud Computing In 2017.”
Washington State University And Analytics Preparedness
All industries, particularly marketing and finance, will be heavily influenced by business analytics technology in the near future. WSU’s online EMBA program exposes students to data analysis and information systems from an executive perspective. Students will study theories and strategies to better prepare themselves to weigh business analytics considerations in their future business decision-making activities.
