Trending February 2024 # Big Data Survey: Big Data Growing Quickly # Suggested March 2024 # Top 2 Popular

You are reading the article Big Data Survey: Big Data Growing Quickly updated in February 2024 on the website Eastwest.edu.vn. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 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.

You're reading Big Data Survey: Big Data Growing Quickly

What Is Big Data Analytics?

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.

Recommend Articles

This has been a guide to Big Data Analytics. Here we have discussed basic concepts like what is Big data Analytics, its benefits, and the key technology behind Big data Analytics. You may also look at the following articles to learn more –

Why Distinction Matters In Big Data And Data Science?

Data has become a resource of interest globally, and harnessing its true potential is becoming important to organizations. According to IBM, 2.5 quintillion bytes of data is created every day. This means that data never sleeps. The increase in data requires the use of different tools and techniques to meaningfully extract insights. Let us first understand how the use of data is defined in the big data and data science industry. Defining data by Work The big data and data science industry terms and definitions overlap and interweave with one another in the analytics field.  However, these are still distinct and are used based on the nature of work. Data science comprises a number of disciplines. These include business intelligence, computer science, data engineering, and statistics, among others. Data science involves processes to collect, clean and analyze both structured and unstructured data. It makes use of the following: + Cleaning raw data to make it ready for analysis. + Finding patterns in the data and helping decision makers in day-to-day business problems. Data science involves discovering hidden patterns within the data through dependencies between different variables. It is used in different industries to make better decisions by understanding and improving the existing business models. On the other hand, Big Data analytics deals with the processing of a large volume of both structured or unstructured data which cannot be processed with the traditional methods. Big data is characterized by 3Vs: the volume, the variety and the velocity at which the data is processed. The key enablers for the growth of big-data are the increase of storage capacities, an increase of processing power, and the availability of huge amount of data. How is data analyzed? Big data and data science help organizations to understand their consumers, and identify new opportunities. Let’s understand how these are applied in real-world situations.

Hypothesis-based reasoning

: The hypothesis-based reasoning helps in formulating hypothesis about relationships between variables. It requires experimenting with data to test hypothesis and models.

Pattern-based reasoning

: The pattern-based reasoning helps to discover new relationships and the analytical path from the data. It involves drawing inferences based on probability. The conclusion reached from this technique is reasonable, probable and believable. On the contrary, big data analytics involves the following steps.

Data Integration

: Big data analytics starts with ingesting data from different sources. This is the first step towards the analysis. It requires integrating all types of structured, unstructured and semi-structured data. Examples include databases, mainframe, social media, file systems, SaaS applications, and XML.

Discovery

: The step involves understanding the data sets and how they relate to each other. The process consists of exploration and discovery of data.

Iteration

: Uncovering insights from data is an iterative process as the actual relationships are not known. Industry experts suggest small defined-projects to enable learning from the iterations. Classification and Prediction: Once the right data is collected, we go ahead for classifying and predicting the data. Classification models predict categorical data, and prediction models predict continuous data. Qualifications matter A critical component of any organization is its team. Both data science and big data require a diverse set of skills. Data scientist or big data analyst are the hottest job titles in the IT industry. Data scientists are highly educated with 88% have master’s degree and 46% have PhDs. They need to possess an in-depth knowledge of statistics with programming languages such as SAS, and R. Big data analysts must have technical knowledge along with the skills possessed by a data scientist. These include SQL databases and database querying languages, Python, Hadoop, Hive & Pig and cloud tools like Amazon S3. However, in both the fields, domain expertise significantly contributes to the understanding of where the problem lies and how the problems could be measured. Closing Thoughts Big data continues to occupy our day to day lives.  When properly infused and analyzed, big data analytics can provide unique insights hidden inside the data. Both data science and big data tools and techniques require a significant investment of time across an array of tasks. The dynamic nature of the field makes its necessary for organizations to understand both the terms. However, no matter, how many the differences are, one cannot be successful without the other.

How Vertical Markets Will Drive Big Data

Also see: Big Data Survey: Big Data Growing Quickly

Although the article is bit dated, this 2012 report from Gartner does a good job of defining the vertical market opportunities that existed in the emerging world of Big Data.  As you can see in the first graphic below, all vertical markets had some interest in Big Data in 2012, with Education and Transportation having the highest percentage, and Insurance and Banking having the lowest.  That always seemed a big backwards to me, but the core idea is that Big Data grew at different rates in each vertical industry because Big Data delivered different values for each vertical.  

Gartner’s take on Big Data opportunity by industry in 2012.

About the same time as the Gartner report, Reuters provided some data that predicted the rapid growth of Big Data, as shown in Figure 2.  For the most part, we’re on track with the Reuters numbers and should see the 45% annual growth that Reuters predicted, if you look at other analyst reports and take my own experience into consideration. 

In 2012, Reuters predicted the rapid growth of Big Data.  For the most part, we’re on track with these numbers.

More recent data includes an IDC study that predicts the market for Big Data to reach $16.1 billion in 2014, growing 6 times faster than the overall IT market. “IDC includes in this figure Infrastructure (servers, storage, etc., the largest and fastest growing segment at 45% of the market), services (29%) and software (24%).” 

Most interesting was one prediction that the adoption of analytics-as-a-service will accelerate, and “ready-made analytics in the cloud” will make the use of Big Data more attractive and compelling.  This would include the ability to define common vertical analytics that will be sold as part of Big Data technology, using cloud-based platforms as the way to distribute these analytics to end user organizations.  That’s my assertion here, and seems to be an emerging and rapidly growing pattern.

Finally, IDC predicts that cloud infrastructure will be the fastest-growing sub-segment of the Big Data market, with a 2013-2024 CAGR of close to 50%.  This means that cloud is driving Big Data, and vice versa.  We’ve seen this happen for the last few years now.

So, where am I going with all of this?  Moving forward, factors that will drive Big Data adoption include:

·  The use of public cloud-based platforms.  This will remove much of the risk of adopting Big Data systems, even amongst the most frugal vertical industries.

·  Vertical market-focused features within Big Data systems, which will make the use of Big Data much more valuable in specific verticals.  Despite what the Gartner report said in 2012, the majority of adopters now appear to be banks and other financial services firms, as being the largest users, with healthcare showing the highest percentage of growth. 

·  The ability to leverage cloud platforms as a single point of distribution for vertically-oriented features and facilities.  This includes schemas to support specific vertical industries, as well as pre-built analytical services that focus on specific vertical processes and analytical requirements.

I’ve already encountered examples of all the above factors.  There are now analytical services that focus on providing information about quality of care in the healthcare vertical.  These services leverage local patient care data along with industry data provided by other data providers that allows healthcare providers to determine true quality of care information, as well as determine how their organization stacks up against other healthcare providers.  This is all part of a canned report, and data set, specifically built for the healthcare vertical and is just one of hundreds of pre-built cloud-delivered analytics that are offered within vertical markets. 

Most IT shops don’t think in these terms yet.  However, end users continue to discover pre-existing and well-defined analytical services that exist in clouds, services that will finally allow them to get at the information they need.  Much like the drive of “shadow IT” around cloud usage in the last few years, the pressure from end users toward pre-built and vertically-oriented Big Data analytics will force IT to provide access to these services.  As a result, more reinvestment will go back into developing these services by the Big Data and cloud technology providers, and these tools will become even more valuable and useful. 

This is happening now.  Big Data continues to get, well, bigger, and cloud computing seems to be driving its use.  The growth of specific vertical market features provided by Big Data systems, and typically delivered and enabled by the public cloud, will be what drives the use of Big Data going forward.  The business case is just too compelling, and technology just too easy to leverage and deploy to make any other prediction.

Photo courtesy of Shutterstock.

Training Review – A Big Data Course With A ‘Big’ Difference From Jigsaw Academy

Big Data has emerged as one of the fastest growing fields in recent times and every business is looking to leverage Big Data to get ahead of the competition. It’s thus not surprising that the demand for skilled Big Data professionals is huge and far exceeds the current supply. So much so, that those with Big Data skills command high salaries (about 25- 50% higher than IT and other fields). It has become the field of choice for many IT professionals looking to fast track their career.

There were no courses in Big Data analytics, until even 18 months ago. However, today we are seeing a flood of courses on offer and many can’t decide, which is the right course for them.

I recently heard that Jigsaw Academy has launched its Big Data analytics course. What caught my attention was the fact that they claim to have India’s first globally recognized certification in Big Data. Now this is something I wanted to check out for real.

The first thing I did was check out the website – specifically the Big Data course page

Credibility of the certification

Jigsaw Academy has tied up with Wiley, a US based publication house, for the Big Data course. All of us, especially the engineers amongst us who swear by Resnick and Halliday know Wiley books. But what is less well known is the fact that Wiley also has a number of certifications, especially in IT.

What was interesting for me was that this is the first time Wiley has tied up with an Indian organization to come up with a joint certification – the Wiley-Jigsaw Big Data specialist certification.

The Wiley-Jigsaw certification certainly looks like the most credible certification available on Big data and should improve job and career prospects for the people, who undertake it.

Course Outline

Keeping the brands aside, let us look at the content of the course.

Comparing the Jigsaw course with the Edureka course, I found a huge overlap between the two. Both the courses cover hadoop, mapreduce, hive, pig and other popular Big Data technologies. However, Jigsaw’s course has an analytics orientation, while the Edureka course has a more technology orientation. It comes out clearly that Jigsaw has made a significant effort to make it more readable to a novice. Edureka, on the other hand, seems to be targeting IT professionals who are already familiar with technical terminologies.

Jigsaw Academy’s Big Data course has an edge when it comes to the analytics piece – they have modules on R and its integration with hadoop –  RHadoop and RMR packages. Also, their case studies cover both structured and unstructured data. If their past case studies are anything to go by, students are in for some great hands-on experience.

This difference stands out, when I compare this course from Jigsaw with other courses as well. Jigsaw’s Big Data course is the first one, I have seen in the market, that has clear focus on analytics.

Student Experience

After certification credibility and course outline, another important evaluation criterion is overall student experience.

I asked Jigsaw for access to one of their Big Data modules. I came away very impressed by the quality of their videos and assignments. Their videos are superior to anything I have seen, including the MIT and Coursera videos. You can have a look at a sample video here.

I also managed to catch hold of two students, who were part of the pilot batch run by Jigsaw in May. Both students were very appreciative of the course.

Umang Chugh, an MBA graduate from AIM Manila, said “I had previously done some analytics courses from Jigsaw and I had really liked them. So when they invited me to be a part of the pilot for Big Data I jumped at it. They have maintained the level of quality we all know them for. Even though Big Data is an intimidating area for a newbie like me, Jigsaw managed to make the course simple and interesting.”

Yatin Gupta is another IT professional who enrolled for the pilot batch.

“What I liked best about the course is the support from the mentors, who are knowledgeable and very helpful. I also liked the case studies and the way they have set up their Big Data lab. It is very easy for a beginner to get started.”

The Wiley Difference

Finally, I wanted to explore the Wiley component of the course. Jigsaw has several analytics courses that are offered independently. What was the reason for the tie up with Wiley? What does Wiley add to the offering?

Wiley has pulled in Big Data experts from around the world to create some of the content for the course. Students receive books (e-copies as well as physical copies) of the course material.

“Wiley has the ability to reach some of the best Big Data experts for building the content. We have the expertise to deliver a great online learning experience. I think together we have the ability to create a world class course in Big Data analytics.” Says Sarita Digumarti, the COO of Jigsaw Academy.

“This is the first time we will be offering physical study material in the form of books to our students. And we could think of no one better than Wiley to partner with for this” she said.

What I did not like

One thing I did not like about the course is that it assumes its students are familiar with analytics and the R tool. I would have liked the R module to be included in the course itself.

We feel that a combination of our Data Science course (where people learn analytics with R) and the Big Data course is the ideal combination for anyone looking to enter into this field. Combining both the courses into one would make the course very long. We deliberately ensure that all our courses are less than 6 months.

The price point of the course is surely something I am concerned about. At Rs. 42,000 this is among the most expensive courses in the market currently.

“Quality comes at a price” was the short answer from Sarita Digumarti when I quizzed her about the course fee. “Our courses have always been premium priced and we feel this kind of pricing is essential for us to be able to continue to deliver the quality of the student experience we aim for.”

Currently, Jigsaw academy is offering an introductory discount of over 15% on their course. This translates to a saving of Rs. 7000 and makes this course very attractive  (Update: This offer was valid till 31st August 2014 and has now expired. But you can still get some great discounts with offers on their site)

Is the course worth investing your time and money in?

Let’s look at the positives first – It offers a globally recognized certification, it offers Big Data with analytics perspective, it delivers a good student experience, the quality of the videos is top class and it leverages on Wiley’s 100+ years of experience in creating great content.

On the flip side, the course is pricey and it needs to be combined with their Data science course to get the full benefit.

Personally, if I can afford to spend Rs. 42,000 on a course, this is the course I would go for.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

Related

Tips To Build A Career In Big Data

Big data is the big daddy of business. While organizations struggle hard to thrive in this competitive world, big data is the tool which helps them to do so. The term “big data” refers to a large volume of data that is readily available. But the question here is: Is all the data useful? Our answer is absolutely no, as you have to yield the output from those huge chunk of data. This is where a big data specialist comes into the picture. Growing consistently from the year 2000, it is currently the buzz of the technology market. More and more professionals are required in this field to do effective management of “big data”. By doing a big data certification from a reliable institute, you can build a roaring career in this field of high importance. What Roles are Most Suited for You? Once you get the 1. Learn the Essential Tools Beforehand– Like any other field, big data also put forward a special requisition to learn a certain set of tools like SPSS, SAS, R, Python, and SQL. If you want to firm your feet in big data industry, you should gain mastery of them beforehand. Rely on trusted sources only as various pirated versions are also available in the market which will add nothing but frustration. 2. Learn the Trade-Trickery– Once you learn the basic tools, it’s time to learn the tracks about how the real-time data world works. To learn this, you have two options open for you: either you can acquire this knowledge from someone senior with the respective field or take the professional help, we would suggest you go with the latter one as a professional help is always the preferred one. A thorough and extensive knowledge that the professional sources will provide holds the utmost importance. It is better to join big data training from reliable resources. 3. Have an Eagle’s Eye for The Opportunities– This is the most crucial step as professionals find it hard to recognize the beneficial opportunities. In most of the cases, they are there right in front of you but in a different role. Start with your current organization as this is the best way to start towards a great career. As you are already working with them and gained their trust, it is the ideal platform to showcase your big data skills. All the organizations are in constant need of such professionals. As it would be your first encounter with the real-time data world, try to stick to the basic and deliver the best. Try to figure out the best possible way to leverage your skills in the organization and grab the opportunities the moment you notice them. 4. Maintain A Work-Portfolio– Maintaining a record of work experiences in the big data will help you to keep a record of the work done. Plus, it will be readily available to display to the top management when the appraisal season is going on. In addition, highlight them in your CV as well. If your organization is not very supportive to notice and nurture your data analytics skills, try to look out for the better option as opportunities are copious to the right candidate.

Big data is the big daddy of business. While organizations struggle hard to thrive in this competitive world, big data is the tool which helps them to do so. The term “big data” refers to a large volume of data that is readily available. But the question here is: Is all the data useful? Our answer is absolutely no, as you have to yield the output from those huge chunk of data. This is where a big data specialist comes into the picture. Growing consistently from the year 2000, it is currently the buzz of the technology market. More and more professionals are required in this field to do effective management of “big data”. By doing a big data certification from a reliable institute, you can build a roaring career in this field of high chúng tôi you get the big data training , you are suitable to join the industry as an expert modeler, programmer, solutions expert, and analytics salesperson. Other than that, we have come up with a rundown of tips that will help you build a remarkable career in big data:– Like any other field, big data also put forward a special requisition to learn a certain set of tools like SPSS, SAS, R, Python, and SQL. If you want to firm your feet in big data industry, you should gain mastery of them beforehand. Rely on trusted sources only as various pirated versions are also available in the market which will add nothing but frustration.– Once you learn the basic tools, it’s time to learn the tracks about how the real-time data world works. To learn this, you have two options open for you: either you can acquire this knowledge from someone senior with the respective field or take the professional help, we would suggest you go with the latter one as a professional help is always the preferred one. A thorough and extensive knowledge that the professional sources will provide holds the utmost importance. It is better to join big data training from reliable resources.– This is the most crucial step as professionals find it hard to recognize the beneficial opportunities. In most of the cases, they are there right in front of you but in a different role. Start with your current organization as this is the best way to start towards a great career. As you are already working with them and gained their trust, it is the ideal platform to showcase your big data skills. All the organizations are in constant need of such professionals. As it would be your first encounter with the real-time data world, try to stick to the basic and deliver the best. Try to figure out the best possible way to leverage your skills in the organization and grab the opportunities the moment you notice them.– Maintaining a record of work experiences in the big data will help you to keep a record of the work done. Plus, it will be readily available to display to the top management when the appraisal season is going on. In addition, highlight them in your CV as well. If your organization is not very supportive to notice and nurture your data analytics skills, try to look out for the better option as opportunities are copious to the right candidate.– There is no end to learning. So, became an active member of both real and virtual big data communities. Join them, read the blogs and articles, give your inputs, take suggestions. It will help you to be updated with the latest trend and technologies that are present around you.

Update the detailed information about Big Data Survey: Big Data Growing Quickly on the Eastwest.edu.vn website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!