A Guide To Data Analytics And Artificial Intelligence

Data analytics and artificial intelligence hold the heart of today’s world of automation and mechanization. Data, the tiniest bit of information, holds the capacity to unleash new insights that can be used to impact millions of lives. Before jumping straight away into the comparison and its relation with Artificial intelligence, we have to take a look at its definition first.

A guide to data analytics and artificial intelligence

Data analytics is a systemic process of analyzing a vast amount of data to fetch trends and patterns of information they contain. The patterns, trends, and correlations are then used for making meaningful insights to create some actionable decisions.

On the other hand, artificial intelligence is the imitation of human intelligence in systems and machines that are programmed in such a way that they can perform tasks that require human intelligence. The term, Artificial Intelligence is also exerted on any machine that portrays the human traits of learning and problem-solving.

It is the need of the moment for businesses to implement both data analytics and artificial intelligence to enhance their possibilities.

By bringing them together, companies will be able to improve their business performances and will also be able to stay in the top position in competing with the relevant companies. In order to get the whole picture of this very discussion, we need to dig deep into each subject in a detailed manner.

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What is Data Analytics?

Most companies are gathering enormous amounts of data all the time, but the fact that it is in its authentic raw form, which does not contain any meaning naturally. It’s now the job of data analytics to process and analyze the raw form of data into meaningful insights, which are further going to be used in making smart business decisions.

There is a particular method that is normally followed by the data analyst. First, the analyst will check the raw data in order to organize it. And then comes the process of analysis to extract some coherent intelligible patterns and information. After interpreting the pattern and data, the analyst will present the findings in the shape of suggestions and recommendations to the company, which will then decide the next step.

Sometimes data analytics can be seen as a branch of business intelligence, which is being used as a specific problem-solving tool within an organization. However, data analytics and data science are two different fields of discipline, each having a very diverse impact on the organization.

The major difference between the two is that while a data analyst will search for the answer to a particular challenge or question that is already known by the business, the data scientist determines the questions that should be asked by the companies. Normally data scientists design data modeling, run custom analyses, and write algorithms for new applications.

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Types of Data Analytics

There are mainly four types of data analytics – Descriptive Analysis, Diagnostic Analysis, Predictive Analysis, and Prescriptive Analysis. Let’s take a look at each and every type. Descriptive analytics is a straightforward and ground-level kind of analysis that asks the simple question of what has already happened in the past.

There are many techniques that are being used in descriptive analytics, among them two important ones are data aggregation and data mining. When a data analyst collects the data and organizes it in a meaningful form that is called the aggregation of data.

And when the data is mined to draw out some fruitful and logical pattern and implications that process is called data mining. It is important to note that descriptive analytics does not try to establish cause-effect relationships, it just describes what happened.

A good example of descriptive data analytics is Google Analytics, which gives us all the information about who is visiting the website in a given time period. Hubspot is also a prominent example, showing how many people open a particular email campaign.

 

Diagnostic analytics digs deeper into the data to understand the reason behind why something has happened. The sole purpose of diagnostic analytics revolves around finding anomalies in big data sets. For example, if descriptive analytics reports present that there was a drop in sales of a certain product by 25% in one particular month, then it is the diagnostic analyst’s responsibility to search for the why factor behind this sudden drop.

To guess the root cause the analyst will go through the additional data files which may offer some good logical insights behind the anomaly. It enables businesses to decide their future marketing strategy. Diagnostic analytics employs a group of different techniques to run the operations which include probability, statistics, time series analysis, filtering, and regression analysis.

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Predictive analytics makes future predictions by using past trends and data in combination with machine learning, deep learning, statistics, and data mining techniques. Data analysts here look at the data in search of risks and opportunities.

Predictive analytics is normally associated with big data and data science. There are some popular productive models that are used to make future predictions which include classification models, clustering models, and time-series models.

Classification models come under the supervision of machine learning. It categorizes the data and describes the relationship between classifications. It can also answer binary questions like yes, no, true, false, etc.  The classification model is used in fraud detection and in the case of credit risk evolution.

Clustering models are normally used in e-commerce sites. By using clustering models, they separate customers into related groups based on similar features and launch particular marketing strategies for each and every group. Time-series models draw insights by analyzing data in a specific time gap like daily, weekly, and monthly.

Then they process the data for trends, repetitive behavior of the customer, and for seasonality. So, Predictive Analytics is all about making predictions of the future. It is greatly used in the banking sector, health care, and in human resources. Prescriptive analytics keeps in mind what happened and why that has happened and what might happen, only to decide what to do now at the moment and also to decide what should be the next step.

It is the most difficult type of analytics which involves complex algorithms, machine learning, computational modeling, and statistical methods. Basically, the prescriptive model determines all the possible ways of decision-making that a company could take. The best application of it is as we can see in Google Maps.

In Google Maps, when we ask for a direction from place A to place B, it will present before you all the possible ways of transportation with a momentary traffic description to find out the best route. In very much the same possible way, prescriptive models work to find out the best route and solution for the company. So it’s quite prominent that it plays a major role in data analytics.

 

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What is Artificial Intelligence?

Artificial Intelligence is a computer science branch in which machines are trained to perform and work on learning, decision-making, and logical reasoning that needs human intelligence without human interventions. Artificial intelligence first emerged as a discipline in 1956 and the term was coined by John McCarthy.

But the idea of putting human intelligence in machines goes to Alan Turing who conducted a test of calculating machine intelligence in 1950. With the advancing technology previous benchmarks and definitions of artificial intelligence have become outdated. As an example, the ability to calculate basic functions and recognize a text through optical characters is now considered an inherent computer function.

Now the basic principle of artificial intelligence is to implement human intelligence machines in such a way that they can execute tasks from the most simple to the most complex. The aim of artificial intelligence is to mimic human cognitive activity.

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Types of Artificial Intelligence

Artificial intelligence can be distinguished into two main categories, these are reactive or weak Artificial Intelligence and strong artificial intelligence. Weak artificial intelligence is the most basic type of artificial intelligence. It embodies a system that is programmed to carry out one definite job.

It provides a predictable output based on the input it receives. Example of weak artificial intelligence includes Deep Blue, video games, the chess-playing IBM supercomputer, personal assistants like Amazon’s Alexa and Apple’s Siri, the spam filter in Gmail, and the Netflix recommendation engine.

A strong artificial intelligence system is one that carries out tasks considered to be more complex and complicated. Strong artificial intelligence uses pre-programmed historical and observational data to handle problem-solving situations. Self-driving cars are the most prominent example of this.

 

A strong artificial intelligence system in autonomous vehicles observes the speed, distance, and direction of other cars on the road and then adjusts itself as per the condition. This understanding of the data and interpreting them in the right way makes the self-driving car the safest option on the road.

The information with which autonomous vehicles work does not get saved in the car’s permanent memory.  Another application of this kind of artificial intelligence can also be found in Hospital operation rooms. There are also two other concepts of artificial intelligence, one is Theory of mind Artificial Intelligence and the other is Self-aware artificial intelligence.

Theory of mind artificial intelligence is fully adaptive and it has a tremendous ability in the decision-making arena and is also able to retain past experiences. Theory of mind artificial intelligence is able to understand emotions and adjust its own behavior according to those emotions while interacting with humans.

Advanced chatbots are the perfect example of this as it already passed the Turing test, fooling a human into believing that artificial intelligence is also a human but the only limitation is that it is not self-aware. As the name suggests, Self-aware artificial intelligence is aware of its own existence and emotions along with other emotions around it.

They will have awareness, consciousness, and intelligence very much similar to humans. All the while it is still in the realm of science fiction. Artificial intelligence brings drastic changes in every sphere of our lives. It is now being used in various fields from mobile phones to diagnosing illness in agriculture and farming to autonomous vehicles and is also providing efficient high performing accurate work in the system.

Currently, artificial intelligence is being used in the following fields virtual assistants or chatbots,  retail, shopping in the fashion industry, autonomous flying in agriculture, livestock and inventory management in healthcare and medical imaging analysis, security, and surveillance.

 

In farming, autonomous tractors and drones are being used to monitor the health conditions of the crop, Artificial intelligence-based face recognition and biometric systems help to keep the record of human beings. Security cameras which are powered by artificial intelligence are being used to keep the city and its Habitat safe.

In the Healthcare system, artificial intelligence plays a very dignified role from diagnosis of the disease to analyzing the medical image and performing difficult surgery. Artificial intelligence is also helping scientists to explore the medicinal field in order to help people. Although it is incorporated in diverse fields and further there is so much left to penetrate.

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Scope of Data Analytics

The two pillars of modernization that transformed today’s world are data analytics and artificial intelligence. Two booming technologies massively changed the professional world. To draw a comparative study between data analytics versus artificial intelligence, we need to discuss its benefits first and then how it linked with each other and their differences. There are countless benefits of Data Analytics. Some of them are noted here.

Product Customization: When it comes to product customization, data analytics help in a great way.  With different techniques of Data Analytics, we are able to gain some thoughtful and proper insights into customers and their trends, patterns, needs, and requirements. This understanding of our audiences enables companies to market their product more effectively and businesses can adjust their targeting criteria by manually or by automation. And the improvement in the targeting results leads to less ad waste.

Personalized Experiences: By analyzing the behavioral pattern of the audiences, Data Analytics can provide exact information about customers’ interests, communication preferences, concerns, and many more. This allows the company to tailor their Customer services and provide personalized experiences to the audiences. It will also help in building stronger bonds between them. Having a vast amount of data also helps in creating a stronger balance among the customer service team, sales team, and marketing team.

Forecasting: Data Analytics helps businesses to predict future events so that the companies would be better equipped to deal with the challenges in the market.

Competitive Knowledge: Data Analytics gives a competitive knowledge about how the other companies are working and what things are trending in the market. With this knowledge of market situations businesses would be able to make effective customer-oriented services and run proper content-driven ads.

The ultimate aim of any organization is to gain an ample amount of profit. With the help of various procedures of data analytics, they can surely achieve that target. It also helps in making a perfect analysis of SWOT (Strengths, weaknesses, threats, and opportunities) and also works on those lacking areas.

 

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Scope Of Artificial Intelligence

Now it’s time to look at a few advantages of Artificial Intelligence. Small and big both types of businesses are now realizing the growing importance of artificial intelligence to achieve both long and short-term goals. It has the power to dramatically alter the business infrastructure.

Nowadays market dynamics are changing rapidly and businesses that adopt the most innovative and flexible strategies and tools like artificial intelligence will remarkably gain financial benefits. In this age of digitalization, there are four major tasks in any business- customer support, marketing, finance, and administration.

Customer support is the ability of businesses to respond to the needs of the customer. These kinds of tasks include help desk tickets and a digital outreach program that addresses and helps in fostering business-client relationships. These tasks can easily be automated with the help of artificial intelligence through the application of virtual chatbots, multi-channel communications, and customer intent production.

Thankful Ushur, Netomi, and Jit Bit Helpdesk are some popular software regarding this. Business marketing revolves around the fact that it can attract the consumer’s attention and sell the products. By implementing artificial intelligence here, the task of managing social media, customer research, e-commerce notifications, and website analytics can be done swiftly and effortlessly.

In business administration and finance applications like Shoe Box, Gusto is used and Freshbook automates the payroll process with receipts and invoices. Tools like Microsoft Dropbox and Google Suite maintain employee-to-employee communication and file storage.

By optimizing consumer experiences Businesses can stand out from the competition. It can be achieved by implementing artificial intelligence-assisted conversation, giving data-driven customized products, and content recommendations, trying personality-based marketing, etc.

 

Artificial Intelligence-supported business models help businesses to retain customers by tracking their inflection points. Artificial intelligence can provide cyber security to businesses. Businesses are normally prone to hacking and cyber security attacks. Various predictive forms of artificial intelligence, neural networks, expert systems, and strong pattern recognition are very well suited to defend against cyber security attacks.

These are very efficient in solving complex cybersecurity problems. Sophos, Crowdstrike, Vade Secure, Darktrace, and Vectra Artificial Intelligence are some prominent software in this domain. The aforementioned benefits of artificial intelligence help to run businesses more skillfully and efficiently.

While these may result in some employees losing their jobs and getting replaced by autonomous technologies, it may be a good opportunity for the whole business to earn maximum profit and reduce overall costs.

Difference Between Data Analytics and Artificial Intelligence

Artificial intelligence vs Data Analytics is a much-discussed topic in the Modern era, although they belong to the same domain and they are related to each other in a certain way, there are some prominent differences between the two. The key difference between Data Analytics and artificial intelligence lies in the fact that how they use data.

Data Analytics uses the raw data to gain some logical insight and patterns to make a business’s decision-making process easier. On the other hand, artificial intelligence is there to emulate intelligent behavior in machines. Artificial intelligence is a machine that thinks and acts like humans.

Data Analytics seeks patterns and trends from data to make decisions while artificial intelligence trains its algorithm through data so that it can function independently. Artificial intelligence is about giving autonomy to the data model while in Data Analytics statistics are used to build models.

Artificial intelligence builds models to emulate cognitive intelligence in machines to do work without human intervention. While Data Analytics is not involved in this high level of scientific processing in comparison to AI. Data analytics detect patterns in data to predict the future, which is a very suitable task to perform by a human. While Artificial Intelligence makes predictions and assumptions that are beyond human capacity.

Comparison Table

Artificial IntelligenceData Analytics
Include machine learning.Includes diverse data operations.
Artificial intelligence correlates and combines large amounts of data through complex processing and intelligent algorithms and helps computers in automated learning.Data analytics works in the sequence of gathering, organizing, and analyzing.
Logic and decision trees are used in Artificial IntelligenceData analytics works with raw, unstructured data.
Tools used here are Keras and Scikit Learn.Tools used here are MATLAB, SAS2
Siri, Alexa, and Chatbot are some applications.In banking, it is used in fraud detection and also used in healthcare.

 

Relation Between Data Analytics and Artificial Intelligence

There are numerous differences between Data Analytics and Artificial Intelligence but in the modernized era, they are linked closely with each other. If both are brought together in a single thread by the businesses then it will act as a game changer. Business efficiency will see rapid growth.

Data analytics and artificial intelligence possess a Synergistic kind of relationship. Artificial intelligence itself needs an enormous amount of data to improve its decision-making function. In other ways, Data Analytics relies on artificial intelligence for better decision-making abilities.

Artificial intelligence is able to assist in all phases of Data Analytics like aggregation in storage and in the collection of diverse types of data from different sources. Artificial Intelligence can also help in pattern management, content management, goal management, decision management, and risk management.

Artificial Intelligence can even keep the ability to find out an unnoticed pattern that is normally missed by the human data scientist. It can also speed up the process of Data Analytics by using its algorithm. Business Intelligence can be improved by the application of artificial intelligence in data science.

It will ditch the guess-based approach of business. With both, businesses will be able to make the right predictions, recognize buying patterns, etc.

FAQs

1. Will artificial intelligence take over job opportunities?

The demand for skilled Artificial intelligence specialists is growing day by day. There are many positions left in the subfields of artificial intelligence like deep learning, machine learning, etc. So it is a myth, a hypothetical scenario. It will pave the way for many future job opportunities.

2. What are the cons of Artificial Intelligence?

Constructing a model of Artificial Intelligence is very time-consuming and costly. Constructing a deep learning model in a normal computer without GPU is impossible. Also, the maintenance cost of it is really high.

3. Will artificial intelligence replace data analytics?

No. It is never going to happen. Both Data Analytics and Artificial Intelligence are linked with each other intrinsically and they complement each other and elevate each other’s efficiency.

Conclusion

A certain amount of differences exist between Data Analytics and Artificial Intelligence but they are also linked intrinsically. If any businesses want to maximize their financial profits and market competency then both Data Analytics and Artificial Intelligence have to be implemented in a coherent way. Although Artificial intelligence possesses little threat of replacing humans in certain areas it can’t be ignored in the era of digitalization. And lastly, it is a myth because Artificial intelligence is also dependent on humans for its continuous modification.