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Introduction to Big Data Analytics

Hadoop, Data Science, Statistics & others

We can Define Big Data as Three Vs

Volume: The amount of data that is being generated every second. Every day organizations like social media, e-commerce businesses, and airlines collect a huge amount of data.

Variety: Data can take various forms, including structured data such as numeric data, unstructured data such as text, images, videos, financial transactions, etc., or semi-structured data like JSON or XML.

What are we Doing with this Big Data?

We can use this big data to process and draw some meaningful insights out of it. There are various frameworks available to process big data. The list below provides the popular framework that big data developers and analysts use widely.

Apache Hadoop: We can write map-reduce the program to process the data.

Spark: We can write a Spark program to process the data; we can also process a live data stream using Spark.

Apache Flink: This framework is also utilized for processing data streams.

And many more like Storm and Samza.

Big Data Analytics

Big Data analytics is collecting, organizing, and analyzing a large amount of data to uncover hidden patterns, correlations, and other meaningful insights. It helps an organization to understand the information in their data and use it to provide new opportunities to improve their business, leading to more efficient operations, higher profits, and happier customers.

To analyze such a large volume of data, Big Data analytics applications enable big data analysts, data scientists, predictive modelers, statisticians, and other analytical performers to analyze the growing importance of structured and unstructured data. Performing these tasks involves the utilization of specialized software tools and applications. Using these tools, one can perform various data operations such as data mining, text mining, predictive analysis, forecasting, etc. High-performance analytics relies on carrying out these processes individually as integral components. Using Big Data analytic tools and software enables an organization to process a large amount of data and provide meaningful insights that deliver better business decisions in the future.

Key Technologies Behind Big Data Analytics

Analytics comprises various technologies that help you get the most valued information from the data.

1. Hadoop 2. Data Mining

Once the data is stored in the data management system, you can use data mining techniques to discover the patterns for further analysis and answer complex business questions. Data mining removes all the repetitive and noisy data and points out only the relevant information used to accelerate the pace of making informed decisions.

3. Text Mining 4. Predictive Analytics

Predictive analytics uses data, statistical algorithms, and machine learning techniques to identify future outcomes based on historical data. It’s all about providing the best future outcomes so organizations can feel confident in their business decisions.

Benefits of Big Data Analytics

Big Data Analytics has been popular among various organizations. Organizations like the e-commerce, social media, healthcare, Banking, and Entertainment industries widely use analytics to understand multiple patterns, collect and utilize customer insights, fraud detection, monitor financial market activities, etc.

E-commerce industries like Amazon, Flipkart, Myntra, and many other online shopping sites use big data.

They collect customer data in several ways like

Collect information about the items searched by the customer.

Information regarding their preferences.

Information about the popularity of the products and many other data.

Using these kinds of data, organizations derive some patterns and provide the best customer service, like

We showcase the popular products being sold.

Show the products that are related to the products that a customer bought.

We ensure secure money transfers and actively detect any fraudulent transactions that occur.

Forecast the demand for the products and many more.

Conclusion

Big Data is a game-changer. Many organizations use more analytics to drive strategic actions and offer a better customer experience. A slight change in efficiency or the smallest savings can lead to a huge profit, which is why most organizations are moving towards big data.

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What Is Big Data And Why Does It Matter In 2023

blog / Data Science and Analytics What is Big Data? Let’s Analyze its Rise and Implications

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The big data market is valued at a whopping US $103 billion. Analyzing infinite data streams allows businesses to make informed, logical decisions. A holistic approach toward it entails combining sophisticated Artificial Intelligence (AI) and traditional analysis tools. So let’s delve into what is big data. 

At a time when over 97 per cent of companies around the world are investing in machine learning, one thing is certain: something deep is brewing in the seething data cauldrons of industries. But, we must first learn about its history and importance and walk the chronology to know how it came to be! 

Brief History 

Alan Turing invented the first massive data-processing machine at the helm of the Second World War in 1943 to decipher Nazi codes. 

The USA launched its first digital data center in 1965.

In 1997, Google launched its first domain, highlighting the climb of industries catering solely to collecting and processing data.

Roger Mougalas coined the term ‘Big data’ in 2005. That year also saw the invention of Hadoop, a Nutch-based open-source software that later merged with MapReduce (processes information parallelly on multiple nodes).

What Exactly is Big Data?

It can be defined as extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations. Big data is stored in a secure system but can be easily accessed and analyzed to help answer questions, provide valuable insights, and give confidence in making strategic business moves.

Define the Three V’s of Big Data

Volume:

This refers to the colossal amount of data in the servers of internet giants. It is one of the key concepts that depend on the number of users of a platform. For instance, Facebook has a

250-billion-image repository

which increases every single day.

Twitter handles about 500 – 700 million tweets

daily on average. Volume is a defining characteristic.

Velocity:

It means how quickly data comes to the existing servers. Taking the example of Meta, the

350 million images added to its servers each day

determine the velocity. Sensor efficiency for the Internet of Things(IoT) also depends on velocity, as the efficiency of devices depends on how much information is transmitted every second.

Variety:

Different kinds of information, such as PDFs, images, audio, and video, define the variety. Take examples of multimedia posts that use videos, audio, reels, and GIFs. They are all encrypted and unstructured. 

What are the 3 Types?

Structured Data:

It is tabular in the form of relational databases where all rows have an equal number of columns.

SQL (Structured Programming Query Language)

is used to process structured information.

Unstructured Data:

It can either mean there is no pre-defined model or there is no particular way in which large sets are organized. They usually include videos, audio, and binary files devoid of a specific structure.

Semi-structured Data:

Scoring midway between structured and unstructured, this kind of information has no relational databases but does follow a rudimentary structure. These sets comprise documents of JavaScript Object Notation, graph databases, and key-value stores, giving them a notion of structure. 

What are Some Use Cases?

It ensures strong predictive models that help industries identify hidden market trends and customer choices and streamline business operations with robust analytics. Understanding what it is, entails moving hand-in-hand with the tumultuous rise of AI and machine learning. These exterior analytical elements aid data scientists in rapid management & structuring. 

Identification of Risks: With its predictive algorithms, analytics help us evade different forms of unexpected threats and provide effective risk management solutions.

Innovation: Data from various customer bases enable distinguishing between what is desired and what is necessary. Keeping track of current marketing practices and fusing them with insights helps maneuver buying trends and track customer behavior.

Customer Retention & Acquisition: Observing consumer behavior is central to a customized shopping experience. Amazon has made the best use of customers’ digital footprints and practices laser targeting.

Streamlining Company Costs: Finally, storing and systematically computing reduces company costs and drives efficiency. 

Frequently Asked Questions  #1: What Industries are Known to Use Big Data Analytics?

Media & communications

Banks & security services

Transportation

Governance & administration

Education

Manufacturing

Retail and wholesale trade

#2: What are the Risks of Big Data?

With great power comes great responsibility. There are many risks that companies need to be aware of, including:

Malevolent usage of the data in organized crime

Data Usage Transparency by companies

High potential for breach of privacy of individuals

Unintentional damage by third-party sharing of private information

#3: Where Can Big Data be Stored?

Data warehouses and lakes. While the former can hold only structured data, the latter can hold all forms, including semi-structured information. Data stored in a data warehouse has been cleaned and processed, ready for strategic analysis, while data stored in a data lake may lack consistency and structure.

#4: How is Big Data Collected?

Online monitoring (caches & cookies)

Surveys & Interviews

Transactional tracking

Online forms

#5: What are the Four Big Data Models?

Storage

Mining

Analytics

Visualization

If you are looking for world-class online courses in analytics and data science, explore Emeritus’ portfolio. 

By Bishwadeep Mitra

Write to us at [email protected]

Big Data Survey: Big Data Growing Quickly

Big data has arrived as a key decision making tool in business – that’s the conclusion of a new survey of Big Data professionals conducted by QuinStreet, Inc., Datamation’s publisher.

The survey found that:

• 77 percent of respondents consider Big Data analytics a priority.

• 72 percent cite enhancing the speed and accuracy of business decisions as a top benefit of Big Data analytics.

• 71 percent of mid-sized and large companies have plans for, or are currently involved with, Big Data initiatives.

The graph of survey responses below reveals that transparency and speed are of key importance, with accurate decision making also seen as a highly important benefit. Note, too, that timely integration of data ranked well. Interestingly, some 61 percent see the value of automated decision making, perhaps suggests that human analysis of Big Data will become less of a default choice as tools grow more sophisticated.

Survey Reveals Big Data Vendors Still Emerging

Although the survey reveals keen interest in Big Data, it also shows that the sector isn’t fully mature. Big Data remains an emerging market sector. For instance, the role of vendors and the relative status of vendors is still every much up for grabs. When participants were asked which vendors they were working with (or planned to work with) to address Big Data analytics, a large chunk – 43 percent – said “none.” Surprisingly, only one vendor was selected by more than 10 percent of respondents.

Part of what’s holding back businesses is the big confusion surrounding Big Data. While IT professionals realize they need to get on board with Big Data, many are concerned about issues like project and management costs, along with issues involved with scaling infrastructure and overcoming data silos and application integration.

Will this be easy? Certainly not – particularly in light of the oceans of data that business now creates, from unstructured social media posts to data gleaned from always-on mobile apps. Survey respondents expect their data volumes to grow by 45 percent over the next two years – a stunning upward trajectory. Even more overwhelming, 10 percent of respondents forecast that their data volumes will double (or more) in that time period.

And this survey response is supported by research from IDC, which forecast that by 2023, the world will generate 50 times today’s information. Oddly, IDC also predicts that, given the evolution of Big Data tools, the IT staff needed to manage the tsunami of data will grow by less than 1.5 times. An optimistic staffing forecast, perhaps.

How Big Data Will Grow So Big

To be sure, the challenges of Big Data are numerous, including the need to scale to accommodate the sheer scope of data. Not surprisingly, the most popular survey responses to a question about how firms will scale for Big Data are “establish easy to use tools” and “increasing network bandwidth.”

It’s interesting to note that “building analytics internally” also scored high among respondents. This may reflect that (as noted above) many respondents have yet to settle on a Big Data vendor and so still expect to rely more heavily on internal resources.

In the years ahead, it’s reasonable to assume one other survey response about scaling will change: “migrating to cloud based storage.” While a mere 23 percent of respondents chose this, massive data volume will surely push this number higher in the years ahead.

About the survey: the QuinStreet Enterprise Big Data research study took place between October 22 and November 8, 2013, with 540 Big Data decision makers completing the survey. Only subscribers involved in Big Data purchasing decisions were allowed to take the online survey. The survey yields a margin of error of +/- 4.3 percent at a 95 percent confidence level.

Photo courtesy of Shutterstock.

What Is A Virtual Data Room?

A virtual data room is a software repository that securely separates your key sensitive files from the rest of your company’s assets. 

A virtual data room can be used at any time, but this security practice is most commonly applied when an organization participates in an audit, merger, or acquisition. 

See below to learn more about how a virtual data room works and how it can benefit your company in different transactional scenarios: 

See more: IT Ops Acquisitions in 2023: Continued Innovation Drives Investment

The virtual data room descends from an actual data room or a secure physical room where sensitive files and other materials are stored. Data rooms are locked rooms in the owner’s headquarters, or sometimes a secure room at a bank or other third-party location that ensures the security of the room’s contents from outsiders. You have to be an authorized visitor or receive special consent to visit the room, and even then, time limits and strict rules usually frame what you can do in the data room. 

As more companies have digitized their most secure data, a traditional data room became an inefficient way to store and share sensitive information. Virtual data room software has taken over as the preferred data room approach, allowing organizations to secure their digital materials in a virtual environment that requires a secure login for access. Much like a traditional data room, access permissions can be tailored to each user and revoked or deactivated at any time.

Virtual data room software holds on to many of the traditional features of a data room, but the digital format features more accountability and communication tools for users. Here are some of the security features that most virtual data rooms incorporate:

Every user granted access to the virtual data room has a unique login, or username and password, but they cannot change their login information on their own. This unique login ensures they only have the permissions they need, and their login can be turned off whenever necessary.

The owner of a virtual data room decides more than who has access. They also get to decide what they can access and how they can interact with it. Virtual data rooms allow you to manage and monitor version control, and you can also set different permissions for different users and files. For instance, some files can be viewed only, some can be downloaded, and some can be edited, depending on the user.

Virtual data rooms focus on security, and one of the top security features they offer is file tracking. The owner of the virtual data room can see when and how often different users login, which files they access, and if they make any changes or add anything else to the room. Especially during key business negotiations, it’s important to know who’s engaged and what they’re contributing to the room.

Most virtual data rooms allow you to upload several files at once, which is especially helpful if you have a large amount of secure data stored in different locations across your network. Make sure your documents are all identified and uploaded as soon as possible. This makes transactions more likely to proceed and external audits run smoother.

Not every virtual data room offers this feature, but many include chatting modules or ways to communicate with other users in the data room. Not only is this a great way to facilitate time-sensitive communications, it’s also a secure way to document conversations related to secure information.

See more: IT Planning During a Crisis

There are many scenarios when you need to keep your virtual data secure, and although there are other software options, like collaboration tools and file-sharing services, they don’t offer the same levels of security and control as a virtual data room. There are several high-profile instances when you should pick a virtual data room:

During the due diligence portion of the transaction, both the buyer and the seller need to learn key information about finances, real estate, assets, and other secure information that should not be made available to all members of the organizations. With a virtual data room, you can give access only to key team members in both the buying and selling organizations as well as any legal and financial team members that may need access. If the due diligence period ends or you decide to move on to a different potential buyer, you can easily deactivate access for the unneeded user logins.

When a company decides to go public, the transparency of company data to important stakeholders is crucial. In combination with the additional rules and regulations they will need to follow, keeping up with organized, clean, and secure documentation becomes a full-time task with traditional systems. Virtual data rooms help companies to identify, secure, and display their data to the IPO team.

Audits and investigations are difficult for most companies, because they have limited visibility on the requested documents. Identifying and sharing the materials takes excessive time and labor when the information is stored in disparate locations and systems. With a well-maintained virtual data room, your most important documents are readily retrievable.

See more: Why Acquisitions, Partnerships are Key to Simplifying IT Deployment

The virtual data room market was valued at $1.49 billion in 2023 and is projected to grow 14.7% annually from 2023 to 2027, according to Grand View Research. 

This growth is particularly happening among banks and legal firms engaging in secure transactions. Virtual data rooms are also growing their presence in the health industry, where organizations are searching for new ways to secure protected health information (PHI) in scenarios like biomedical trials. 

Virtual data rooms can benefit any industry and organization that needs a secure location for a specific set of sensitive data. 

If you’re interested in creating a virtual data room, here are some vendors that offer a solution:

Anserada

OneHub

Intralinks

Datasite

Citrix

iDeals

SecureDocs

See more: Top Virtual Data Room Providers

What Is A Customer Data Platform And What Is Use It?

A Customer Data Platform, also known as AI HTML3_, is a piece of HTML3_ programming HTML3_ that combines information from different apparatuses.

What is a Customer Data Platform (CDP)?

A Customer Data Platform (CDP), a piece of AI programming, is an AI program that blends data from different apparatuses to create a single concentrated client data set. It contains information on all touchpoints and associations with your administration or item. The CDP data protection database can be divided in a variety of ways. This allows for more targeted showcasing efforts and data protection.

Understanding what CDP programming does, the most effective way to make sense of this is as a visual demonstration. Say an organization is attempting to get a superior comprehension of their clients. The CDP will be used to collect information from social media platforms such as Facebook and email. The CDP will collect the data, then combine it into a client profile that can be used for other purposes, such as the Facebook promotions stage.

This cycle allows the organization to use division to better understand its audience and to make more targeted showcasing efforts. The organization could undoubtedly make a publicizing crowd in light of every individual who has visited a particular page on their site and furthermore the organization’s live talk include. Or on the other hand, they could rapidly fragment and view information on location guests who’ve deserted their trucks.

This is one way Drift can customize its showcasing efforts. Segment’s Personas are used to help with three tasks.

Personality goal – To unify client history across all gadgets and channels, into one client view for each client.

Quality and crowd-building – Synthesizes data into crowds and attributes for each client, including clients who have expressed expectations. This coordinates with generally speaking record movements.

Actuation- This pushes their client to different instruments in their stack in order to coordinate custom, continuous outbound information.

How to Use a Customer Data Platform?

1. Online to Offline Connection

Combine offline and online activities to build a customer profile. When customers enter a brick-and-mortar store, you can identify them from their online activities.

2. Customer Segmentation & Personalization

3. Predictive Customer Scoring

Enhance your customer profiles by using predictive data (probability to purchase, churn and visit, email open).

4. Smart Behavioral Retargeting & Looking Alike Advertising

Also read:

Top 10 Best Artificial Intelligence Software

5. Recommendations for Product

6. Conversion Rate Optimization and A/B Testing

You can quickly transform your pages’ appearance. Our smart website overlays (popups) or cart abandonment emails can help you increase your ROI. You can create different designs and see which one performs best with automation.

Also read:

Top 10 Trending Technologies You should know about it for Future Days

7. Omni-Channel Automation

8. Email Delivery Enhancement

Increase email opening rates. An AI-powered algorithm allows you to determine the best time to distribute each user’s email based on their email opening patterns and reach them at that optimal hour.

What Is Data Wrangling And How Does It Improve Data Analysis?

blog / Data Science and Analytics How Data Wrangling is Helping Businesses Make Better Decisions

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In 2023, most major organizations are led by data while making business decisions. As a result, data professionals are essential for businesses to function. At the same time, Gartner research recently found that organizations believe that poor-quality data is costing them an average of $15 million in losses annually. This combination of high dependency on data as well as uncertainty about data quality is making practices like data wrangling vital for businesses to function efficiently. Which brings us to the question: what is data wrangling and how does it help? Let’s explore. 

What is Data Wrangling?

It is the practice of removing errors from data sets or restructuring complex datasets to make them more suitable for analysis. Wrangling consists of cleaning, organizing, and transforming raw data into the desired format for analysts to use. It helps businesses use more complex data, faster, and more accurately. 

What are its Benefits?

It transforms raw data and makes it usable for businesses. Here are the key benefits of data wrangling: 

Data Consistency

It helps turn raw data into consistent data sets which businesses can use. For example, data collected from consumers is usually error-ridden. Data wrangling can help eliminate these human errors and make the data more uniform. 

Improved Insights

The consistency brought through wrangling often provides better insights about metadata. 

Cost Efficiency

Cleaning up and organizing data through wrangling reduces errors in the data, saves time for the person who will be using the data, and thus reduces costs for the company.

Importance of Data Wrangling

McKinsey has estimated that big data projects could account for a reduction of $300-450 billion in US healthcare spending. It is clear that data analysis has a significant impact on business practices. However, any analyses that businesses perform will only be as effective as the data informing them. To ensure accurate results, consistent, reliable data is necessary. Data wrangling proves to be essential to achieve this accuracy.

Best Practices for Data Wrangling

To ensure effective results, there are certain practices one should be aware of: 

Remember Your Objective

Think about the objective of the person who needs the data you are working with. By doing this, you will be focused on the data that they need. 

Choosing the Right Data

Selecting the right data is necessary. To ensure quality: 

Avoid duplicate data

Use the original source

Use recent data

Double Check

Humans are always capable of errors, even data wranglers. It is necessary to re-check the data once wrangling is complete. 

Steps to Perform Data Wrangling Step 1: Discovery

This process involves thinking about the desired results, understanding what kind of data is necessary to achieve the objectives, and collecting the desired data. 

Step 2: Organization

After the raw data is gathered, it needs to be structured into a less overwhelming and more organized form.

Step 3: Cleaning

After the data is structured, you can start cleaning it. This involves removing outliers, null, and duplicate data. 

Step 4: Enrichment

In this step, you review if you have gathered enough data. If a data set is too small, it may compromise the results of the analysis.

Step 5: Validation

Once enrichment is complete, you can apply validation rules to your data. Validation rules applied in iterations can confirm if your data is consistent.

Step 6: Publishing

The last step is data publishing. Here you prepare the data for future use. This includes making notes and documenting the entire process. 

Data Wrangling Examples Financial Insights

Data wrangling can be used to discover insights hidden in data, predict trends, and forecast markets. It helps in making informed investment decisions.

Improved Reporting

Creating reports with unstructured data can be a challenge. Data wrangling improves data quality and helps in reporting.

Understanding Customer Base

Customers exhibit different behaviors which can be reflected in the data they generate. Data wrangling can help identify common behavioral patterns.

Who Uses Data Wrangling?

Data analysts spend most of their time conducting data wrangling rather than data analysis. This is to ensure that the best results are delivered for businesses using the most accurate data. It is essential for businesses in nearly every industry. 

Frequently Asked Questions. 1. What are Popular Data Wrangling Tools?

OpenRefine

Tabula

Google DataPrep

Data wrangler

2. What’s the Difference Between Data Wrangling and Data Cleaning

The objective of data cleaning is to remove inaccurate data from the data set, whereas the objective of wrangling is to transform the data into a more usable format. 

3. How can Data Wrangling Improve Data Quality?

Data wrangling helps remove errors from the data set and also structures it in a more usable format. When the data is well structured and error-free, the subsequent data analysis is able to yield more accurate results which in turn end up in better business outcomes. 

As big data finds even greater acceptance in business, the need for data professionals is only going to be on the rise. Having learned what is data wrangling, if you are interested in going deeper into this field, explore the courses on data science and analytics on Emeritus. These are offered in collaboration with top universities and will help you in your career as a data professional.

By Tanish Pradhan 

Write to us at [email protected]

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