Trending December 2023 # Applications Of Data Analytics In Hospitality # Suggested January 2024 # Top 18 Popular

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Most hospital industry players find it hard to attract new customers and convince them to come back again. It is important to develop ways to stand out from your competitors when working in a competitive market like the hospitality sector.

Client analytic solutions have proven to be beneficial recently since they detect problem areas and develop the best solution. Data analytics application in the hospitality industry has proven to increase efficiency, profitability, and productivity.

Data analytics assists companies to receive real-time insights that inform them where improvement is required, among others. Most companies in the hospitality sector have incorporated a data analytics platform to stay ahead of their rivals.

Below we discuss the applications of data analytics in hospitality.

1. Unified Client Experience

Most customers use several gadgets when booking, browsing, and knowing more about hotels. This makes it essential to have a mobile-friendly app or website and make sure the customer can shift from one platform to the other easily.

The customer’s data should be readily accessible despite the booking method or the gadget used during the reservation. Companies that create a multi-platform, seamless customer experience not only enhance their booking experience but also encourage their customers to return.

2. Consolidates Date from Various Channels

Customers enjoy various ways to book rooms and other services, from discount websites to travel agents and direct bookings. It is essential to ensure your enterprise has relevant information concerning the customer’s reservation to provide the best service. This data can also be important for analytics.

3. Targeted Discounts and Marketing

Targeted marketing is an important tool. Remember, not all guests are looking for the exact thing, and you might share information they are not concerned about by sending them the same promotions.

However, customer analytic solutions assist companies in sending every individual promotion they are interested in, which causes an improved conversion rate. Companies also use these analytics to target their website’s visitors, not just those on the email list.

4. Predictive Analysis

Predictive analysis is an important tool in most industries. This tool is the most suitable course of action for a company’s future projects, instead of simply determining how much a certain project has been successful.

These tools enable businesses to test various options before determining which one has a high chance of succeeding. Consider investing in robust analytics since it saves you significant money and time.

Why Synthetic Data And Deepfakes Are The Future Of Data Analytics?

Synthetic data can help test exceptions in software design or software response when scaling.

It’s impossible to understand what’s going on in the enterprise technology space without first understanding data and how data is driving innovation.

What is synthetic data?

Synthetic data is data that you can create at any scale, whenever and wherever you need it. Crucially, synthetic data mirrors the balance and composition of real data, making it ideal for fueling machine learning models. What makes synthetic data special is that data scientists, developers, and engineers are in complete control. There’s no need to put your faith in unreliable, incomplete data, or struggle to find enough data for machine learning at the scale you need. Just create it for yourself.

What is Deepfake?

Deepfake technology is used in synthetic media to create falsified content, replace or synthesizing faces, and speech, and manipulate emotions. It is used to digitally imitate an action by a person that he or she did not commit.

Advantages of deepfakes:

Bringing Back the Loved Ones! Deepfakes have a lot of potential users in the movie industry. You can bring back a decedent actor or actress. It can be debated from an ethical perspective, but it is possible and super easy if we do not think about ethics! And also, probably way cheaper than other options.

Chance of Getting Education from its Masters

Just imagine a world where you can get physics classes from Albert Einstein anytime, anywhere! Deepfake makes impossible things possible. Learning topics from its masters is a way motivational tool. You can increase the efficiency, but it still has a very long way to go.

Can Synthetic Data bring the best in Artificial Intelligence (AI) and Data Analytics?

In this technology-driven world, the need for training data is constantly increasing. Synthetic data can help meet these demands. For an AI and data analytics system, there is no ‘real’ or ‘synthetic’; there’s only data that we feed it to understand. Synthetic data creation platforms for AI training can generate the thousands of high-quality images needed in a couple of days instead of months. And because the data is computer-generated through this method, there are no privacy concerns. At the same time, biases that exist in real-world visual data can be easily tackled and eliminated. Furthermore, these computer-generated datasets come automatically labeled and can deliberately include rare but crucial corner cases, even better than real-world data. According to Gartner, 60 percent of the data used for AI and data analytics projects will be synthetic by 2024. By 2030, synthetic data and deepfakes will have completely overtaken real data in AI models.

Use Cases for Synthetic Data

There are a number of business use cases where one or more of these techniques apply, including:

Software testing: Synthetic data can help test exceptions in software design or software response when scaling.

User-behavior: Private, non-shareable user data can be simulated and used to create vector-based recommendation systems and see how they respond to scaling.

Marketing: By using multi-agent systems, it is possible to simulate individual user behavior and have a better estimate of how marketing campaigns will perform in their customer reach.

Art: By using GAN neural networks, AI is capable of generating art that is highly appreciated by the collector community.

Simulate production data: Synthetic data can be used in a production environment for testing purposes, from the resilience of data pipelines to strict policy compliance. The data can be modeled depending on the needs of each individual.

6 Ways To Get Better Data In Google Analytics

How to use filters in Google Analytics to get more accurate data

In this article, we will explain how to receive more accurate data using filters.

For a start, keep in mind that filters in GA are designed to help you customize the data seen/viewed in GA, according to the purpose of the report you need to create. You can

Exclude data;

Include data;

Change data;

Search & replace data;

First Things First: How to Add a Filter

Filters are added only within the “View” menu and here you can create many views if required. A view of the website is basically a copy of the GA data with different settings applied, e.g. you can set access rules, determine the goals for your website, etc. For further information about what the view is and how to add a view check out this material.

Now, log into your GA account, select a “View” menu, and press the “Filter” button.

Select a filter type thoughtfully.

Correct: exclude IP 193.88.222.1

Incorrect: qwerty12345

Before you apply any changes, make sure to consider these details:

Don’t delete or add filters to your original view; experiment with copies of your website views because when you delete a view that particular historical perspective of data is gone.

Google Analytics applies filters exclusively to data that would be added after creating the respective filter, e.g. you cannot filter the data you already have.

Filters are applied in a particular order, in which they are set, so you should specify your selection carefully. It’s important to consider what filters to apply first, second, and so on.

Anatomy of GA Filtering: How to Get More Out of Your Data

Let us analyze the following example. There is a company X that works in e-commerce in Canada. It has an online store blog with lots of posts, and an active forum. They can ship their products within Canada only, but their website is visited by people from different countries, mainly the U.S. and the U.K.

Advice #1: Measure traffic from the local audience.

The visitors from the countries other than Canada don’t convert into sales. So, it makes sense to set the filter “country” like on the image below.

Advice #2: Exclude traffic from the company employees.

Advice #3: Combine data in a page view report.

Since Google is case sensitive, it’s possible to get data as shown in the following table:

Page Pageviews

/page1.html 5 → /page1.html 11

/Page1.html 4

/PAGE1.html 2

To analyze the obtained results with ease, apply the following filter in the view.

Advice #4: Clean the data out of spam.

We recommend this tool developed by Simo Ahava (Google Developer Expert for GA), which allows you to identify and remove the fake hits to the website. They have an in-built database sufficient to accomplish most of these tasks. Here you can find the helpful spam filter tool.

Advice #5: Ensure to exclude data resulted from incorrect traffic.

As you probably know from using GA, you have to add a Tracking Code to your website or mobile application before you start receiving GA data, as shown in the example below:

The issue is that anybody can copy your code (accidentally or specifically) and add it on their site or application, so your Analytics will collect information from there. Let’s consider the aforementioned in our sample case and exclude such traffic to collect better data.

Advice #6: Divide commercial and informational traffic.

If you have a blog or forum on your site, you can exclude this kind of traffic by creating a filter as it is shown in the image below. Company X should definitely consider this option because they have a popular forum, which is also used by their employees.

Take Back to Work

GA offers a wide functionality and it is sensitive to details. So, a vital task during creating filters is to apply the correct settings, or else GA either will not perform any actions or will measure your data incorrectly. Follow these pro tips to win your way:

Identify and set the filter parameters with your KPIs in mind.

Discover the potential of predefined filters before you get to custom options.

Select the filter order before you start applying anything, especially if you consider using custom filters.

Hire a GA expert if you lack time to configure all the required settings, as receiving wrong data won’t allow you to make the right decisions.

Introduction To Data Mining And Its Applications

This article was published as a part of the Data Science Blogathon

Overview

Learn the basic concept of Data mining

Understand the Applications of Data Mining

Prerequisites

Basic understanding of Python

Basic knowledge of DataBase

Welcome guys!

Here I am going to give you a brief understanding of the basic concepts of Data Mining. We know that everywhere there is data in a various format which is to be stored in a database. According to the scale of data, we can choose a proper database. So there are popular databases that we know such as PostgreSQL, NoSQL, MongoDB, Microsoft SQL Server, and many more.

In this article, you will be getting an idea of Data Mining.

So Let’s move on…

What is Data Mining:-

“Data Mining”, that mines the data. In simple words, it is defined as finding hidden insights(information) from the database, extract patterns from the data.

There are different algorithms for different tasks. The function of these algorithms is to fit the model. These algorithms identify the characteristics of data. There are 2 types of models.

1)Predictive model

2)Descriptive model

Basic Data Mining Tasks

Under this section, we are going to see some of the mining functions/tasks.

1)Classification

This term comes under supervised learning. Classification algorithms require that the classes should be defined based on variables. Characteristics of data define which class belongs to. Pattern recognition is one of the types of classification problems in which input(pattern) is classified into different classes based on its similarity of defined classes.

2)Prediction

In real life, we often see predicting future things/values/or else based on past data and present data. Prediction is also a type of classification task. According to the type of application, for example, predicting flood where dependant variables are the water level of the river, its humidity, raining scale, and so on are the attributes.

3)Regression

Regression is a statistical technique that is used to determine the relationship between variables(x) and dependant variables(y). There are few types of regression as Linear, Logistic, etc. Linear Regression is used in continuous values(0,1,1.5,….so on) and Logistic Regression is used where there is the possibility of only two events such as pass/fail, true/false, yes/no, etc.

4) Time Series Analysis

In time series analysis, a variable changes its value according to time. It means analysis goes under the identifying patterns of data over a period of time. It can be seasonal variation, irregular variation, secular trend, and cyclical fluctuation. For example, annual rainfall, stock market price, etc.

5) Clustering

Clustering is the same as classification i.e it groups the data. Clustering comes under unsupervised machine learning. It is a process of partitioning the data into groups based on similar kinds of data.

6) Summarization

Summarization is nothing but characterization or generalization. It retrieves meaningful information from data. It also gives a summary of numeric variables such as mean, mode, median, etc.

7) Association Rules

It’s the main task of Data Mining. It helps in finding appropriate patterns and meaningful insights from the database. Association Rule is a model which extracts types of data associations. For example, Market Basket Analysis where association rules are applied to the database to know that which items are purchased together by the customer.

8) Sequence Discovery

It is also called sequential analysis. It is used to discover or find the sequential pattern in data.

Sequential Pattern means the pattern which is purely based on a sequence of time. These patterns are similar to found association rules in database or events are related but its relationship is based only on “Time”.

Up to this point, we have seen all the basic functions or tasks of Data Mining. Let’s go-ahead to know more about Data Mining…

Data Mining VS KDD(Knowledge Discovery in Database)

Data Mining: Process of use of algorithms to extract meaningful information and patterns derived from the KDD process. It is a step involved in KDD.

KDD: It is a significant process of identifying meaningful information and patterns in Data. The input is given to this process is data and output gives useful information from data.

KDD process consists 5 steps:

1)Selection: Need to obtain data from various data sources, databases.

2)Preprocessing: This process of cleaning data in terms of any incorrect data, missing values, erroneous data.

3)Transformation: Data from various sources must be converted, encoded into some format for preprocessing.

4)Data Mining: In this process, algorithms are applied to transformed data to achieve desired output/results.

5)Interpretation/evaluation: Has to perform some visualizations to present data mining results which are very important.

Data Mining Applications 1) E-Commerce

E-commerce is one of the real-life applications of it. E-commerce companies are like Amazon, Flipkart, Myntra, etc. They use data mining techniques to see the functionality of every product in such a way that “which product is viewed most by the customer also what they also liked other”.

2) Retailing

It is another application of data mining from the retail market. Retailers find the pattern of “Freshness, Frequency, Monetary(In terms of Currency)”. Retailers keep the track of sales of products, transactions.

3) Education Tools for Data Mining

– KNIME

-WEKA

-ORANGE

Data Mining Algorithms

K-means clustering

Support vector machines

Apriori

KNN

Naive Bayes

CART and many more…

These are few algorithms.

*Now I am going to give you information about the required libraries below.

– Apriori:

from apyori import apriori

– K-means clustering:

from kneed import KneeLocator from sklearn.datasets import make_blobs from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn.preprocessing import StandardScaler

– Support Vector Machines:

from sklearn import svm

-Naive Bayes:

from sklearn.naive_bayes import GaussianNB

-CART:

from chúng tôi import DecisionTreeRegressor

-KNN:

from sklearn.neighbors import KNeighborsClassifier

So here are few libraries that to be installed while performing the algorithm.

Conclusion

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

Prominent Applications Of Natural Language Processing In Communication

Emailing Filters

The amount of mails anyone receives is overwhelming. While some e-mails are business-related, others are only sent for promotional purposes. With the help of

Smart Assistants

“Hey Siri”! This term is the trademark of Apple’s iPhone. With such an invasive voice recognition technology, this phrase is popular amongst human beings, whether they are an iPhone user or not. But have we ever paused for a moment, how Apple’s Siri, or Amazon’s Alexa, answers all our questions with precision? Or how they comprehend human language with a prompt response? The answer to these questions is the integration of

Search Results

While promoting company profiles, articles, blogs or even making a website, the emphasis is given to the search results. Also, many job applications demand search results or search engine optimization to be the priority. And while this word has created the buzz for increasing customer’s engagement and is a key to marketing, it becomes imperative to know the “What” of this application. The “What” of this application is the integration of NLP into the system, which surfaces relevant results based on the behaviors and semantics of language it is trained on. An example of this would be Google’s Search option, which understands the query of the user based on the few words that they have typed.

Predictive Text

Autocorrect! We use simple words numerous times in a day while using any device. We take this simple application for granted without even realizing the science behind such application. The predictive analytics of NLP is the reason behind the devices’ familiarity with autocorrect. They predict things based on the semantics they are trained in and will either finish the word or suggest a relevant one. Moreover, they allow the user to customize their language preferences and learn from them.

Language Translation

Natural Language Processing makes the semantics of human language comprehendible to the systems, devices, and machines. Communication is the key to progress! This phrase has been regaled innumerable times, to establish a successful business. Be it an employee-employer relationship or owner-client/customer relationship, communication is the driving force behind successful decision making. Over the years, communication has been enthralled with innovations in technology. From the discovery of telephone to the integration of voice recognition technique into Amazon’s Alexa, the technology has revolutionized how humans communicate. Moreover, the key to good communication is accomplished by understanding the complexities of the language. Language is an inherent behavior by living organisms and includes semantics such as words, signs or images. A human being, while reaching adolescence becomes well-versed about understanding the different aspects of communication. But modern technology-driven devices require an immense amount of learning and training before they can understand the semantics of the language. With Artificial Intelligence technologies and Machine Learning models, this task becomes easier. Specifically, the AI technology, Natural Language Processing , is integrated into every system of communication, to make the task feasible. In this article, we will observe the prominent applications of Natural Language Processing (NLP) in chúng tôi amount of mails anyone receives is overwhelming. While some e-mails are business-related, others are only sent for promotional purposes. With the help of natural language processing (NLP), these emails can be categorised as primary, social or promotions. Additionally, with the adaptation of NLP, spam filters can be integrated into the system depending upon the semantics of the language. This is already used with Gmail, where it makes the inbox manageable.“Hey Siri”! This term is the trademark of Apple’s iPhone. With such an invasive voice recognition technology, this phrase is popular amongst human beings, whether they are an iPhone user or not. But have we ever paused for a moment, how Apple’s Siri, or Amazon’s Alexa, answers all our questions with precision? Or how they comprehend human language with a prompt response? The answer to these questions is the integration of Natural Language processing in Apple’s Siri and Amazon’s Alexa. With the help of NLP, the applications like Alexa and Siri picks up the contextual clues with the ML model that assists them in answering the questions with promptness and precision. Tech experts suggest that in the distant future, NLP will be the world where humans will live in.While promoting company profiles, articles, blogs or even making a website, the emphasis is given to the search results. Also, many job applications demand search results or search engine optimization to be the priority. And while this word has created the buzz for increasing customer’s engagement and is a key to marketing, it becomes imperative to know the “What” of this application. The “What” of this application is the integration of NLP into the system, which surfaces relevant results based on the behaviors and semantics of language it is trained on. An example of this would be Google’s Search option, which understands the query of the user based on the few words that they have typed.Autocorrect! We use simple words numerous times in a day while using any device. We take this simple application for granted without even realizing the science behind such application. The predictive analytics of NLP is the reason behind the devices’ familiarity with autocorrect. They predict things based on the semantics they are trained in and will either finish the word or suggest a relevant one. Moreover, they allow the user to customize their language preferences and learn from chúng tôi businesses do not need a Spanish Thesaurus to translate what the client is saying in Spanish into English. By integrating NLP into devices or applications, the online translators automatically translate the language accurately. An example of this would be Google’s Keyboard in mobile which makes the translation task easier.

What Is Big Data? Why Big Data Analytics Is Important?

What is Big Data? Why Big Data Analytics Is Important? Data is Indispensable. What is Big Data?

Is it a product?

Is it a set of tools?

Is it a data set that is used by big businesses only?

How big businesses deal with big data repositories?

What is the size of this data?

What is big data analytics?

What is the difference between big data and Hadoop?

These and several other questions come to mind when we look for the answer to what is big data? Ok, the last question might not be what you ask, but others are a possibility.

Hence, here we will define what is it, what is its purpose or value and why we use this large volume of data.

Big Data refers to a massive volume of both structured and unstructured data that overpowers businesses on a day to day basis. But it’s not the size of data that matters, what matters is how it is used and processed. It can be analyzed using big data analytics to make better strategic decisions for businesses to move.

According to Gartner:

Importance of Big Data

The best way to understand a thing is to know its history.

Data has been around for years; but the concept gained momentum in the early 2000s and since then businesses started to collect information, run big data analytics to uncover details for future use. Thereby, giving organizations the ability to work quickly and stay agile.

This was the time when Doug Laney defined this data as the three Vs (volume, velocity, and variety):

Volume: is the amount of data moved from Gigabytes to terabytes and beyond.

Velocity: The speed of data processing is velocity.

Variety: data comes in different types from structured to unstructured. Structured data is usually numeric while unstructured – text, documents, email, video, audio, financial transactions, etc.

Where these three Vs made understanding big data easy, they even made clear that handling this large volume of data using the traditional framework won’t be easy. This was the time when Hadoop came into existence and certain questions like:

What is Hadoop?

Is Hadoop another name of big data?

Is Hadoop different than big data?

All these came into existence.

So, let’s begin answering them.

Big Data and Hadoop

Let’s take restaurant analogy as an example to understand the relationship between big data and Hadoop

Tom recently opened a restaurant with a chef where he receives 2 orders per day he can easily handle these orders, just like RDBMS. But with time Tom thought of expanding the business and hence to engage more customers he started taking online orders. Because of this change the rate at which he was receiving orders increased and now instead of 2 he started receiving 10 orders per hour. This same thing happened with data. With the introduction of various sources like smartphones, social media, etc data growth became huge but due to a sudden change handling large orders/data isn’t easy. Hence a need for a different kind of strategy to cope up with this problem arise.

Likewise, to tackle the data problem huge datasets, multiple processing units were installed but this wasn’t effective either as the centralized storage unit became the bottleneck. This means if the centralized unit goes down the whole system gets compromised. Hence, there was a need to look for a better solution for both data and restaurant.

Tom came with an efficient solution, he divided the chefs into two hierarchies, i.e. junior and head chef and assigned each junior chef with a food shelf. Say for example the dish is pasta sauce. Now, according to Tom’s plan, one junior chef will prepare pasta and the other junior chef will prepare the sauce. Moving ahead they will hand over both pasta and sauce to the head chef, where the head chef will prepare the pasta sauce after combining both the ingredients, the final order will be delivered. This solution worked perfectly for Tom’s restaurant and for Big Data this is done by Hadoop.

Hadoop is an open-source software framework that is used to store and process data in a distributed manner on large clusters of commodity hardware. Hadoop stores the data in a distributed fashion with replications, to provide fault tolerance and give a final result without facing bottleneck problem. Now, you must have got an idea of how Hadoop solves the problem of Big Data i.e.

Storing huge amount of data.

Storing data in various formats: unstructured, semi-structured and structured.

The processing speed of data.

So does this mean both Big Data and Hadoop are same?

We cannot say that, as there are differences between both.

What is the difference between Big Data and Hadoop?

Big data is nothing more than a concept that represents a large amount of data whereas Apache Hadoop is used to handle this large amount of data.

It is complex with many meanings whereas Apache Hadoop is a program that achieves a set of goals and objectives.

This large volume of data is a collection of various records, with multiple formats while Apache Hadoop handles different formats of data.

Hadoop is a processing machine and big data is the raw material.

Now that we know what this data is, how Hadoop and big data work. It’s time to know how companies are benefiting from this data.

How Companies are Benefiting from Big Data?

A few examples to explain how this large data helps companies gain an extra edge:

Coca Cola and Big Data

Coca-Cola is a company that needs no introduction. For centuries now, this company has been a leader in consumer-packaged goods. All its products are distributed globally. One thing that makes Coca Cola win is data. But how?

Coca Cola and Big data:

Using the collected data and analyzing it via big data analytics Coca Cola is able to decide on the following factors:

Selection of right ingredient mix to produce juice products

Supply of products in restaurants, retail, etc

Social media campaign to understand buyer behavior, loyalty program

Creating digital service centers for procurement and HR process

Netflix and Big Data

To stay ahead of other video streaming services Netflix constantly analyses trends and makes sure people get what they look for on Netflix. They look for data in:

Most viewed programs

Trends, shows customers consume and wait for

Devices used by customers to watch its programs

What viewers like binge-watching, watching in parts, back to back or a complete series.

For many video streaming and entertainment companies, big data analytics is the key to retain subscribers, secure revenues, and understand the type of content viewers like based on geographical locations. This voluminous data not only gives Netflix this ability but even helps other video streaming services to understand what viewers want and how Netflix and others can deliver it.

Alongside there are companies that store following data that helps big data analytics to give accurate results like:

Tweets saved on Twitter’s servers

Information stored from tracking car rides by Google

Local and national election results

Treatments took and the name of the hospital

Types of the credit card used, and purchases made at different places

What, when people watch on Netflix, Amazon Prime, IPTV, etc and for how long

Hmm, so this is how companies know about our behavior and they design services for us.

What is Big Data Analytics?

The process of studying and examining large data sets to understand patterns and get insights is called big data analytics. It involves an algorithmic and mathematical process to derive meaningful correlation. The focus of data analytics is to derive conclusions that are based on what researchers know.

Importance of big data analytics

Ideally, big data handle predictions/forecasts of the vast data collected from various sources. This helps businesses make better decisions. Some of the fields where data is used are machine learning, artificial intelligence, robotics, healthcare, virtual reality, and various other sections. Hence, we need to keep data clutter-free and organized.

This provides organizations with a chance to change and grow. And this is why big data analytics is becoming popular and is of utmost importance. Based on its nature we can divide it into 4 different parts:

In addition to this, large data also play an important role in these following fields:

Identification of new opportunities

Data harnessing in organizations

Earning higher profits & efficient operations

Effective marketing

Better customer service

Now, that we know in what all fields data plays an important role. It’s time to understand how big data and its 4 different parts work.

Big Data Analytics and Data Sciences

Data Sciences, on the other hand, is an umbrella term that includes scientific methods to process data. Data Sciences combine multiple areas like mathematics, data cleansing, etc to prepare and align big data.

Due to the complexities involved data sciences is quite challenging but with the unprecedented growth of information generated globally concept of voluminous data is also evolving. Hence the field of data sciences that involve big data is inseparable. Data encompasses, structured, unstructured information whereas data sciences is a more focused approach that involves specific scientific areas.

Businesses and Big Data Analytics

Due to the rise in demand use of tools to analyze data is increasing as they help organizations find new opportunities and gain new insights to run their business efficiently.

Real-time Benefits of Big Data Analytics

Data over the years has seen enormous growth due to which data usage has increased in industries ranging from:

Banking

Healthcare

Energy

Technology

Consumer

Manufacturing

All in all, Data analytics has become an essential part of companies today.

Job Opportunities and big data analytics

Data is almost everywhere hence there is an urgent need to collect and preserve whatever data is being generated. This is why big data analytics is in the frontiers of IT and had become crucial in improving businesses and making decisions. Professionals skilled in analyzing data have got an ocean of opportunities. As they are the ones who can bridge the gap between traditional and new business analytics techniques that help businesses grow.

Benefits of Big Data Analytics

Cost Reduction

Better Decision Making

New product and services

Fraud detection

Better sales insights

Understanding market conditions

Data Accuracy

Improved Pricing

How big data analytics work and its key technologies

Here are the biggest players:

Machine Learning: Machine learning, trains a machine to learn and analyze bigger, more complex data to deliver faster and accurate results. Using a machine learning subset of AI organizations can identify profitable opportunities – avoiding unknown risks.

Data management: With data constantly flowing in and out of the organization we need to know if it is of high quality and can be reliably analyzed. Once the data is reliable a master data management program is used to get the organization on the same page and analyze data.

Data mining: Data mining technology helps analyze hidden patterns of data so that it can be used in further analysis to get an answer for complex business questions. Using data mining algorithm businesses can make better decisions and can even pinpoint problem areas to increase revenue by cutting costs. Data mining is also known as data discovery and knowledge discovery.

In-memory analytics: This business intelligence (BI) methodology is used to solve complex business problems. By analyzing data from RAM computer’s system memory query response time can be shortened and faster business decisions can be made. This technology even eliminates the overhead of storing data aggregate tables or indexing data, resulting in faster response time. Not only this in-memory analytics even helps the organization to run iterative and interactive big data analytics.

Predictive analytics: Predictive analytics is the method of extracting information from existing data to determine and predict future outcomes and trends. techniques like data mining, modeling, machine learning, AI are used to analyze current data to make future predictions. Predictive analytics allows organizations to become proactive, foresee future, anticipate the outcome, etc. Moreover, it goes further and suggests actions to benefit from the prediction and also provide a decision to benefit its predictions and implications.

Text mining: Text mining also referred to as text data mining is the process of deriving high-quality information from unstructured text data. With text mining technology, you uncover insights you hadn’t noticed before. Text mining uses machine learning and is more practical for data scientists and other users to develop big data platforms and help analyze data to discover new topics.

Big data analytics challenges and ways they can be solved

A huge amount of data is produced every minute hence it is becoming a challenging job to store, manage, utilize and analyze it. Even large businesses struggle with data management and storage to make a huge amount of data usage. This problem cannot be solved by simply storing data that is the reason organizations need to identify challenges and work towards resolving them:

Improper understanding and acceptance of big data

Meaningful insights via big data analytics

Data storage and quality

Security and privacy of data

Collection of meaningful data in real-time: Skill shortage

Data synching

Visual representation of data

Confusion in data management

Structuring large data

Information extraction from data

Organizational Benefits of Big Data

Big Data is not useful to organize data, but it even brings a multitude of benefits for the enterprises. The top five are:

Understand market trends: Using large data and big data analytics, enterprises can easily, forecast market trends, predict customer preferences, evaluate product effectiveness, customer preferences, and gain foresight into customer behavior. These insights in return help understand purchasing patterns, buying patterns, preference and more. Such beforehand information helps in ding planning and managing things.

Understand customer needs: Big Data analytics helps companies understand and plan better customer satisfaction. Thereby impacting the growth of a business. 24*7 support, complaint resolution, consistent feedback collection, etc.

Improving the company’s reputation: Big data helps deal with false rumors, provides better service customer needs and maintains company image. Using big data analytics tools, you can analyze both negative and positive emotions that help understand customer needs and expectations.

Promotes cost-saving measures: The initial costs of deploying Big Data is high, yet the returns and gainful insights more than you pay. Big Data can be used to store data more effectively.

Makes data available: Modern tools in Big Data can in actual-time presence required portions of data anytime in a structured and easily readable format.

Sectors where Big Data is used:

Retail & E-Commerce

Finance Services

Telecommunications

Conclusion

With this, we can conclude that there is no specific definition of what is big data but still we all will agree that a large voluminous amount of data is big data. Also, with time the importance of big data analytics is increasing as it helps enhance knowledge and come to a profitable conclusion.

If you are keen to benefit from big data, then using Hadoop will surely help. As it is a method that knows how to manage big data and make it comprehensible.

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Preeti Seth

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