Understanding how to differentiate between data, analytics, and insights and how they work.

Data. Analytics. Insights. These three are the most important food for the soul of business today. The current scenario is more transformational and technology-dependent, where data is known as the digital currency. Data is driving business intelligence through advanced analytics and by deriving intelligent insights. These three aspects are interconnected and they build off the process of generating insights from data. Without data, analytics cannot be performed and hence, insights cannot be interpreted. However, several times people confuse these terms, using them interchangeably. This might dissolve the actual significance of the terms and what they stand for. Hence, here is an effort to distinguish them and understand their meanings interdependent of each other.

Data: The Cornerstone of Business Intelligence

Business intelligence and digital transformation sound snazzy and stylish since it involves cutting-edge technologies like Artificial Intelligence and Data Science. In order to start a transformation, you need to lay the groundwork, and data does that job. Today, there is an abundance of data that are obtained from a wide variety of sources. A singular data point might seem not useful, but when it is presented collectively, it is possible to define patterns and meaning from them. Big data is the exponentially growing huge amount of data that has become the foundation of businesses across industries. This includes data from consumers, users, clients, general audience, internet, media, and many other data points.

Businesses collect data differently according to their niche and needs. Hence, data should have a context so that meanings can be derived. Business organizations should deploy smart infrastructures and experts to collect and process the unstructured, raw data. Maintaining a data flow and storing the huge loads of data have become great challenges these days.

Analytics: Discovering the Meaning

We discussed how singular data without a context can be senseless. Analytics steps in here. It enables us to find patterns and meanings from the huge datasets and makes it sensible. Disruptive technologies have made analytics more advanced to gain better business decisions. Unlocking the real value from data is not possible without analytics. Analytics experts help in processing the collected data and translating it into a comprehensible form. With analytics, an organization can understand how it is performing over time and deliver patterns of consumer behavior and market trends.  Arriving at important marketing and business decisions is impossible without analytics.

Let us take an example of a business trying to sell a product. For this, they have to first identify the demographic they are going to serve, then find their interests and behavior patterns, establish a strategy to get the product into the market, and later measure its impacts and effects. All this is possible by deploying analytics on the data collected. For online websites, measuring traffic is a significant step today and analytics completes this demand.

Insights: Taking Actions with Decisions

Let us continue from the same example. The same product that you were planning to launch has now been provided with analytical insights. It now shows what are the unique customer behaviors, the perfect time to launch your product in the market, and highlights the risk elements involved in the process. These are called insights, or analytics insights. They are the value derived from combining data and analytics. These are powerful comprehensions that can enhance business growth, customer traction, and predict risks.

Insights are like the edible form of food you get after you add the ingredients and cook them in specific directed conditions. Here, the ingredients are data and the preparation process is the analytics performed on them.

Data-Driven Business Intelligence

Data and analytics will seem invaluable if they cannot deliver meaningful insights. Disruptive technologies like AI and machine learning play a potential role in enabling data-driven business intelligence. Insights should be comprehensible and actionable, which can improve the business and accelerate its transformation. These data-driven insights are the reason behind banks being able to detect frauds, healthcare providers being able to diagnose from medical images, manufacturers being able to smartly manage the supply chains, etc.

Data intelligence is entering into new horizons with the help of disruptive technologies providing new avenues for businesses. Today’s customer-centric businesses are surviving on the data insights to understand and engage with their customers. Businesses need to find the meaning and decisions from data analytics and the insights they derive. As the great American mathematician, John Wilder Tukey has said, “The greatest value of a picture is when it forces us to notice what we never expected to see.”

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From personal assistants like Siri, to movie suggestions on Netflix, artificial intelligence (NYSE:AI) is rapidly becoming ubiquitous in everyday life. As this technology continues to advance in capability and prevalence, we sought to explore AI and several closely related subtopics: machine leaning, deep learning, and neural networks.

What are the Differences between Artificial Intelligence, Machine Learning, and Deep Learning?

While artificial intelligence (AI), machine learning (ML), and Deep Learning (NYSE:DL) are often used interchangeably, there are several key differences. One way to visualize the relationship is through a series of concentric circles. AI is the macro topic which encompasses the entire field of study, while ML is a subtopic within AI. DL is a further refinement of ML and represents the most cutting edge of AI applications that are being used today.

At a basic level, artificial intelligence is the concept of machines accomplishing tasks which have historically required human intelligence.1 AI can be broken down into two distinct fields:

Applied AI: Machines designed to complete very specifics tasks like navigating a vehicle, trading stocks, or playing chess – as IBM’s Deep Blue demonstrated in 1996 when it defeated chess grand master Gerry Kasparov.

General AI: Machines designed to complete any task which would normally require human intervention. The broad nature of General AI requires machines to “learn” as they encounter new tasks or situations. This need for a learned approach is what gave rise to modern Machine Learning.

Today, many firms at the cutting edge of AI are focusing on machine learning (ML). In simple terms, ML is the process of building machines which can access data, apply algorithms to this data, and then train themselves to deduce valuable insights based on these underlying datasets.

The key difference between ML and AI is that ML does not rely explicitly on the code of its creator. Rather, ML systems use computer code as a starting point and then gather data, information, and inputs which can be studied – similar to how a student might study for an exam. It is this relationship with big data that makes ML and the Internet of Things(connecting regular objects to the internet so they can collect data or be controlled remotely) so closely intertwined.

Currently, ML is typically used to recognize faces, voice commands, and objects, as well as to translate languages. It has been successfully implemented in chatbots, such as Siri (Apple), Cortana (Microsoft), and Alexa (Amazon). With the victory of Google’s “Deep Mind” over the Go world champion in 2016, ML is now increasingly becoming accepted as a useful tool for decision making in the corporate world.

Deep learning takes artificial intelligence a step further, by mimicking how the human brain works through the use of artificial neural networks. In an artificial neural network, each neuron is charged with providing a binary (yes/no) response to basic questions about a piece of data. By layering thousands (or millions) of these artificial neural networks, a Deep Learning machine is able to generate reliable outputs (recommendations or interactions) without changing the underlying coding.

Consider a very basic artificial neural network which is responsible for determining if a photo contains a banana or an apple. The network has three neurons which are responsible for answering:

1.  Is the object in the picture round?
2. Is the object in the picture yellow?
3.  Does the object in the picture have a stem?

The network would respond with no, yes, no for the photo of a banana and yes, no, yes for the photo of an apple. Using binary, the network would learn that a banana is 010 and an apple is 101. Extrapolate this concept across thousands of yes/no questions of exponential complexity and you have the bases of artificial neural networks and deep learning.

Apart from being used in image and voice recognition algorithms, companies are implementing deep learning to predict customer preferences, detect fraud and spam, fight malware, conduct life-saving diagnoses, and recognize handwriting. In many ways, the possibilities for this technology are endless.

What’s Ahead?

Gartner’s recent study projects that by the end of this decade, the average person will have more conversations with a virtual assistant or bot than with his or her immediate family.6Such penetration of artificial intelligence into our everyday lives will depend on further advances in the technology to become smarter, more capable, and easier to interact with. While many expect this progress to occur from advances in machine learning and deep learning, there are new techniques to being introduced as well.

It’s with this momentum in mind that we developed our Robotics and Artificial Intelligence ETF (NASDAQ:BOTZ). The fund seeks to invest in companies that can potentially benefit from increased adoption and utilization of robotics and artificial intelligence.

1. https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#35aa37802742

2. https://www.leverege.com/blogpost/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning

3. http://www.techrepublic.com/article/machine-learning-the-smart-persons-guide/

4. http://www.techrepublic.com/article/7-companies-that-used-machine-learning-to-solve-real-business-problems/

5. http://www.explainthatstuff.com/introduction-to-neural-networks.html

6. http://www.gartner.com/smarterwithgartner/gartner-predicts-a-virtual-world-of-exponential-change/

Investing involves risk, including the possible loss of principal. The investable universe of companies in which BOTZ may invest may be limited. The Fund invests in securities of companies engaged in Information Technology which can be affected by rapid product obsolescence, and intense industry competition. In addition to normal risks associated with investing, international investments may involve risk of capital loss from unfavorable fluctuation in currency values, from differences in generally accepted accounting principles or from social, economic or political instability in other nations. The fund is non-diversified which represents a heightened risk to investors.

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Carefully consider the Fund’s investment objectives, risk factors, charges and expenses before investing. This and additional information can be found in the Fund’s full or summary prospectus, which may be obtained by calling 1-888-GX-FUND-1 (1.888.493.8631), or by visiting globalxfunds.com. Read the prospectus carefully before investing.

Global X Management Company LLC serves as an advisor to Global X Funds. The Funds are distributed by SEI Investments Distribution Co. (SIDCO), which is not affiliated with Global X Management Company LLC. Global X Funds are not sponsored, endorsed, issued, sold or promoted by Solactive AG, FTSE, Standard & Poors, NASDAQ, Indxx, or MSCI nor do these companies make any representations regarding the advisability of investing in the Global X Funds. Neither SIDCO nor Global X is affiliated with Solactive AG, FTSE, Standard & Poors, NASDAQ, Indxx, or MSCI.

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