Top 12 Analytics Trends of 2016

December 27, 2016

It’s almost the end of the year and a great deal has happened. In the world of data analytics, innovative use of the technology has helped transform how several industries utilize data.

Listed below are the top 12 analytics trends for the year 2016. It starts from simple data terms to complex concepts that showcases the possibility to create a machine with human intelligence.

1. Big Data

“The data volumes are exploding, more data has been created in the past two years than in the entire previous history of the human race.”1

Big data refers to the processes and tools used to handle large sets and complex data obtained from multiple sources. Traditional forms of information processing are not enough to store and analyze big data, garner insights, decision making and process automation.2

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One use of big data is it can help optimize the workforce. Using big data and talent analytics, HR departments can keep operating costs down as well as manage employee issues. The data helps them select better fitting hires, reduce turnover, better understand employee skills and determine the talent needed for the company to grow. Xerox was able to reduce attrition rate in their call centers by understanding the cause of turnover and find ways to effectively engage their employees.3

2. Data Streams

Data streams are continuously generated data from different sources which send data records simultaneously. This data is sequentially and incrementally processed, and used for a wide variety of analytics. Companies can make use of insights and information derived from the analysis to see aspects of their businesses and learn customer activities (e.g. service usage (for metering/billing), server activity, website clicks, and geo-location of devices, people, and physical goods).4

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Data streams can be useful in various businesses and industries. One example is in the telecommunications industry in a competitive market where they need to monitor any drop in service levels. Communications systems continuously generate large volumes of data that need to be quickly analyzed to detect ant error that could negatively affect customer satisfaction. 5

3. Analytics of Things

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Data as is will not be inherently valuable unless it offers insights that businesses can use to make better decisions. This holds for the newest generation of appliances and devices that provide extra functionality by accessing or being accessed through the Internet. These Internet of Things (IoT) devices generate various amounts of data, and the term Analytics of Things (AoT) means that data coming from these is only useful when analyzed. It also means that in order for connected devices to become smart enough so they can do intelligent actions, analytics that makes sense of the data they provide is necessary.6

One example of how AoT works is providing better traffic information. By using the geo-location information of the mobile devices of drivers, traffic information has become more accurate and near real-time.7

4. Robotic Process Automation

Robotic Process Automation (RPA) uses something called a “robot” or “bot”, a piece of software that captures and interprets IT applications to enable transaction processing, data manipulation and communication across various IT systems. Other than the financial benefits, making use of RPAs improves accuracy, timeliness and operational flexibility.8

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The presence of RPAs in various businesses and a number of industries has been increasing of late. One industry that is starting to see the use of RPA is that of  travel and tourism, where the average number of invoices processed in a day can easily reach or exceed 6,000. Each booking processes multiple transactions in various currencies and languages. A travel company turned to RPA in order to better handle the workload.9

5. Social Media Analytics

Social Media has become an important part in the marketing processes of businesses. To make better decisions, businesses make use of Social Media Analytics (SMA), to make better sense of  the data gathered from their social media channels.10

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Different types of businesses makes use of Social Media Analytics to promotes products or service and to better understand their customers. One company made use of SMA to discover trends, identify  problem areas and keep from having  brand crisis. They found out one of their products had 25% negative sentiment rating. By continually monitoring and using SMA, the company managed to keep the crisis under control and eventually found a way to recover from it.11

6. Data storytelling

Not everyone has the skill to understand data, but by data storytelling, it can be communicated to most anyone in simpler terms. What Data Storytelling does is give you the results along with data visualizations.12

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A good number of Wall Street Journal subscribers are people who need to make sense of large volumes of collected economic data and learn how to understand and react to key economic indicators. To meet the needs of this core business audience, the Wall Street Journal made use of storytelling.13

WSJ created a series of data visualizations and accompanying anecdotes from the gathered demographic data to help its subscribers understand macroeconomic trends. Through this, WSJ could  present this the information they want to learn and see.14

7. Predictive

Predictive analytics is about understanding the future and trying to forecast future outcomes. From analyzed data, businesses may acquire insights that can help them in their decisions. These analytics look for patterns from historical data Then, statistical models and algorithms are used to identify relationships between different sets of data.15

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Predictive Analytics can help you be one step ahead and fill in the gaps of your information.16

An information portal wanted to increase the response rate to their advertisements by finding out which promotions each visitor would probably respond to. After putting 291 models to work, they were able to provide a targeted ad based on the results. There was a 25% uptick in the rate of response which is roughly $1 million of ad revenue every 19 months.17

8. Prescriptive

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Prescriptive analytics advises possible actions and guides you to a solution. Before making a decision, businesses can get insights by making future actions and possible outcomes measurable . This kind of analytics makes use of various kinds of data – historical, transactional, real time, etc. – as well as  tools such as business rules, algorithms, machine learning and computational modelling procedures.18

The Aurora Health Care system reduced re-admission rates by a tenth by using prescriptive analytics. They were also able to save $6 million annually. Prescriptive analytics can also be used for better drug development, identifying patients for clinical trials that are the proper match, and more.19

9. Algorithm Economy

Algorithms are the building blocks of any application.20

In an “Algorithm Economy”, a term coined by Gartner, developers can produce, distribute and commercialize their code. By integrating algorithms, developers can build smarter apps in a more cost-efficient and time-efficient way.21

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According to Frank Buytendijk, Gartner vice president:

The algorithm economy is about the “density of contact” with “your business at the center of a network.” Whether you are talking a web of customers or a Web of millions of devices or “things,” your business is a network of algorithms interacting together. When possible you want to “give” these algorithms away. “It is the business reality of the 21st century.” 22

10. Deep Learning

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Deep learning focuses on trying to recreate the human method of learning for getting certain types of knowledge. Unlike machine learning algorithms which are quite straightforward, those for deep learning are “stacked” with hierarchies of increasing complexity and abstraction. 23

With the use of deep learning, it has become possible to track brand presence at events or locations, compare  brand performance with competitors and target advertising campaigns. Ditto Labs was able to build a detection system to identify the environment in which brand appears and if there is a smiling face in the photo. 24

11. Machine Learning

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Before being able to achieve Artificial Intelligence, machines must first be programmed to learn. Programmers are having a difficult time writing programs for machines to learn tasks such as object recognition, understanding concepts and comprehending speech because they also don’t know how it’s done by the human brain. 25

Programmers decided to try developing algorithms that machines can use to look at thousands of examples and the correct answers. In a different situation, the machine must be able to solve the same problem from the algorithm. 26

One example of using machine learning use is the application JuskShakeIt by Healint, which enables a user to alert contacts or caregivers that there is an emergency by shaking the phone with one hand.The program uses an algorithm to distinguish emergency shakes and everyday jostling. They are currently working on a model that can identify warning signs for chronic neurological conditions by analyzing the user’s’ cell phone accelerometer data.27

12. Artificial Intelligence

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Artificial Intelligence are machines with human intelligence. The general concept is that machine possess our senses, reason and ability to think. This concept hasn’t been realized but AI pioneers were able to achieve “Narrow AI”. Narrow AI are machines or technologies that are able to perform specific tasks like humans or maybe even better than humans. An example is the face recognition function on Facebook. A more complex AI machine is autonomous cars or self-driving cars. These is a work-in-progress concept but the goal is for cars to eventually be capable of reacting to traffic flow and other drivers.28



1 Bernard, M.(2015, September 30). Big Data: 20 Mind-Boggling Facts Everyone Must Read. Retrieved from

2 Big Data. In Gartner IT Glossary. Retrieved from

Kerr, J. (2016, January 18). Data Storytelling: Big Data’s Next Frontier. Retrieved from

3 Morgan, L. (2015, May 27). Big Data: 6 Real-Life Business Cases. Retrieved from

4 Streaming Data. In Amazon Web Services. Retrieved from

5 Hurwitz, J. Nugent, A. Halper, F. Kaufman, M. How to Use Data Streaming For Big Data. Retrieved from

6 Davenport, T. (2014, December 17). The Analytics of Things. Retrieved from

7 Franks, B. (October 15). How Analytics of Things (AoT) Will Help Us Analyze the IoT. Retrieved from

8 Robotic Process Automation. In Deloitte. Retrieved from

9 Driving efficiency for a travel industry giant. In create tomorrow. Retrieved from

10 Social Media Analytics. In techopedia. Retrieved from

11 Social Media Analytics helps companies meet business goals. In Expert System.  Retrieved from

12 Dykes, B. (2016, July 13). Data Storytelling: Separating Fiction From Facts. Retrieved from

13 Kaplan, A. (2016, January 06). 3 Powerful Examples of Visual, Data-driven Storytelling. Retrieved from

14 Ibid.

15 (2016, July 21). Descriptive, Predictive, and Prescriptive Analytics Explained. Retrieved from

16 Ibid.

17 Case Study: How Predictive Analytics generates $1million increased Revenue. Prediction Impact. Retrieved from

18 2016, July 21). Descriptive, Predictive, and Prescriptive Analytics Explained. Retrieved from

19 (2016, February 08). Types of Analytics: descriptive, predictive, prescriptive analytics. In DeZyre. Retrieved from

20 Oppenheimer, D. How the Algorithm Economy and Container are Changing The Apps. Retrieved from

21 Ibid.

22 Wagner, D. (2015, October 05). CIOs need to Welcome Age of the Algorithm , Gartner Says. Retrieved from

23 Deep Learning. In TechTarget. Retrieved from

24 Murnane, K. Thirteen Companies that Use Deep Learning To Produce Actionable Results. (2016, April 01). Retrieved from

25 Buduma, N. Deep Learning in a Nutshell – what it is, how it works, why care?. In KDnuggets. Retrieved from

26 Ibid.

27 Hamilton, L. (2014, January 06). Six Novel Machine Learning Applications. Retrieved from

28 Copeland, M. (2016, July 29). What’s the Difference between Artificial Intelligence, Machine Learning, and Deep Learning?. Retrieved from



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