Big Data Stats for the Big Future Ahead

Your cat’s birthday is in a few days and you are looking online for a toy to get her. You search online for a bit, and then you log into Facebook. Suddenly, every ad you encounter is about feline entertainment.

Coincidence?

Not in the slightest.

You were targeted, courtesy to big data.

(I wonder what you will see on your wall after spending some time on Hosting Tribunal.)

As machine learning advances, more and more information about your interests and searches gets collected and analyzed. To accommodate the growing volume of data, hosting services need to constantly grow and adapt as well.

But why is big data so important?

Fascinating Big Data Stats

  • Data volumes have skyrocketed. More data was generated in the last two years than in the entire human history before that.
  • Since 2012, big data has created 8 million jobs in the US alone and 6 million more worldwide.
  • Big data needs as much computing power as you can throw at it. That’s why engineers aspire to reach the processing capability of the human brain for their CPUs in the next decade!
  • Big data holds the key to an amazing future. It reveals patterns and connections that significantly improve our lives. Secure self-driving cars, more effective medical treatments, even reliable weather forecast that will allow farmers to get better yields!
  • The driving force behind big data is the “data-fication” of information. For example, in the past, you would just go for a walk. Today you know it was 10,435 steps long and you burned 450 calories because of it.
  • IT services will earn the biggest share of the BDA revenues in 2019. The estimated profit is $77.5 billion! Right behind it are hardware purchases ($23.7 billion), and business services ($20.7 billion). Big data stats show that software-wise, BDA revenues will go as high as $67.2 billion this year.

(Sources: Forbes, DisruptorDaily, TowardsDataScience, TechTarget, ExplainingComputers, Kenneth Cukier)

Need for Big Data

  • The big data growth we’ve been witnessing is only natural. We constantly generate data. On Google alone, we submit 40,000 search queries per second. That amounts to 1.2 trillion searches yearly!
  • Each minute, 300 new hours of video show up on YouTube. That’s why there’s more than 1 billion gigabytes (1 exabyte) of data on its servers!
  • People share more than 100 terabytes of data on Facebook daily. Every minute, users send 31 million messages and view 2.7 million videos.
  • Big data usage statistics indicate people take about 80% of photos on their smartphones. Considering that only this year over 1.4 billion devices will be shipped worldwide, we can only expect this percentage to grow.
  • Smart devices (for example, fitness trackers, sensors, Amazon Echo) produce 5 quintillion bytes of data daily. In 5 years, we can expect for the number of these gadgets to be more than 50 billion!
  • Big data stats indicate that more than 30% of data will be uploaded to the cloud by next year.
  • Moving to a cloud can improve a business’s agility (by 29%) and shorten payback times by 30%.
  • Huge companies like Google use shared computing to satisfy their customers’ needs. About 1,000 computers are involved in answering every query.
  • In fact, the most popular open source for distributed computing – Hadoop, has a compound annual growth rate of 58% and will surpass $1 billion by 2020.

(Sources: Forbes, DisruptorDaily, Quora, Merchdope, Cisco, Wikibon, NewGenApps)

Big Data and Analytics

  • Surprisingly, 99.5% of collected data never gets used or analysed. So much potential wasted!
  • Less than 50% of the structured data collected from IoT is used in decision making.
  • Predictive analytics are becoming more and more crucial for success. 79% of executives believe that failing to embrace big data will lead to bankruptcy. This explains why 83% of companies invest in big data projects.
  • Fortune 1000 companies can gain more than $65 million additional net income, only by increasing their data accessibility with 10%.
  • Healthcare could also vastly benefit from big data analytics adoption. As much as $300 billion can be saved yearly!
  • Companies that harness big data’s full power could increase their operation margins by up to 60%!

(Sources: Cisco, Wikibon, Baseline, McKinsey, Gartner, Forbes)

Big Data by Geography

Global

The statistics show that revenue generated from big data is evergrowing. In 2015, it was responsible for $122 billion of profits. It’s expected to generate $189.1 billion in 2019 and $274.3 billion in 2022!

US

The US are by far the largest country market. The BDA revenues this year are expected to reach $100 billion. To put that into perspective, the other 4 countries in the top 5 will altogether generate $35 billion. The future of big data in the US looks bright indeed!

UK

UK is the third largest BDA market, after the US and Japan. It will generate $9.2 billion this year. However, the predictions for 2020 indicate that big data and IoT will be worth almost $420 billion for the UK economy!

Big data global usage

Big Data Adoption Rate

The big data stats indicate that more and more people realize BDA’s huge potential. The country with the fastest adoption growth rate is Argentina (with 20.8% CAGR). After that comes Vietnam (with 19.8% CAGR), Philippines (19.5% CAGR), and Indonesia (19.4% CAGR).

(Sources: Statista, Outlook Series, BusinessWire, TechUK,    Zoomdata)

Big Data Growth Trends

  • The amount of data created each year is growing faster than ever before. By 2020, every human on the planet will be creating 1.7 megabytes of information… each second!
  • In only a year, the accumulated world data will grow to 44 zettabytes (that’s 44 trillion gigabytes)! For comparison, today it’s about 4.4 zettabytes.
  • The revenues generated by BDA worldwide were $42 billion in 2018. In 2027, they’re projected to increase to $103 billion with a CAGR of 10.5% until then!
  • Hadoop is the most popular big data processing software. Its market is expanding fast and anticipated to hold a CARG of 53.7% for the period of 2015 to 2022!
  • The Chinese big data market is one of the fastest growing worldwide with a CAGR of 31.72%. By 2020, the revenue is projected to reach ¥57.8 billion – that’s $9 billion! In 2014, they were only at ¥8.4 billion, or $1.2 billion.
  • Statistics show big data adoption can increase retail sales by 3% to 4%. As more and more companies harness the power of BDA, the need for tools to process the information rises as well. big data software is projected to grow at a CAGR of 12.6%, reaching $46 billion in 2027.
  • By 2020, the IoT is projected to generate over $300 billion annually. The market will grow at a 28.5% CAGR.

(Sources: Forbes, Forbes, EMC, Wikibon, Statista, MarketWatch, Statista, BCG, Statista, Analytics Insight)

Big Industries Using Big Data

Big data is useful across the board but certain industries benefit from it much more than others.

Healthcare

  • Consulting firm McKinsey estimated that big data analytics adoption can save up to 17% of healthcare costs. In 2013 that amounted to $493 billion dollars in reductions!

Banking

  • Modern customers look for a highly personalized experience. In fact, 84% of executives, surveyed by Oracle, agreed to this. 81% of them believe the solution lies in IT cloud development.
  • Adoption of big data in the field will bring up to 18% increase in revenue. For a $1 billion company, this would come up to $180 million a year!
  • The American Express Company has already jumped on the BDA train. By analyzing over a hundred variables, they can now accurately predict 24% of the accounts that will close within 4 months.

Media

  • With more than 70 million active users, Miniclip is one of the largest gaming websites. To retain customers and increase revenue, the company uses big data. Analyzing the collected information helps determine which games will be more successful.
  • Statistics prove Miniclip’s migration to Amazon Web Services (AWS), a cloud platform specialized in collecting and processing big data, was a very smart move. New game deployment now takes 4 hours, where it used to take 4-5 weeks!
  • By moving to AWS, Miniclip saved $100,000 for new load balancers.
  • The website now has availability in the five 9s. Latency was cut in half – from 4.5 seconds to 2 seconds. Time to market was decreased by the staggering 97%!
  • The entertainment giant Netflix is another one of the companies using big data. The analysis of the massive amounts of data collected from their 100 million subscribers, has allowed them to predict each customer’s interest.
  • Big data influences 80% of all movies and shows watched on Netflix.
  • Back in 2009, the company offered a million dollars for the whoever comes up with the best prediction algorithm. This move (and the winning algorithm) have been saving Netflix $1 billion a year from customer retention!

Retail

  • Naturally, the amazing stats about big data didn’t go unnoticed by Amazon. The vast amounts of data were why they created AWS – their own cloud computing platform.
  • Amazon creates an individual “360-degree view” profile of each customer. They group you with others with similar interests to recommend products you’ll like.
  • Before 2016, the company hardly had any profit. After the introduction of AWS, Amazon’s income skyrocketed. In 2017 they earned $3 billion, and in 2018 – $10.1 billion.
  • Starbucks wouldn’t have been the coffeehouse chain we know, had they ignored the statistics about big data analytics! Their business has been constantly growing thanks to their smart information gathering.
  • The Starbucks mobile app has more than 17 million users, the reward program – 13 million. One-third of the purchases are made online. Using the information customers shared there, they learn more about purchasing habits.
  • The strategy is working well – Starbucks will have 37,000 stores worldwide by 2021!
  • Personalization and engagement are working their magic. In 2017, 18% of customers accounted for 36% of the sales!

Energy and Utilities

  • The worth of big data got its fair share of attention by the energy industry as well. General Electric vastly increased their efficiency by using information from sensors on turbines and engines.
  • The company estimates big data can boost US productivity by 1.5% YoY. Those numbers stack up nicely in the long run!

(Sources: DisruptorDaily, IDC, TexasAMA, CIO, DestinationCRM, Medium, Eastern Peak, Oracle, Amazon, Miniclip, InsideBigData, DataFloq, CNBC, Forbes, TechHQ)

Industries That Are Moving Fast Towards Big Data

Big data reaches far.

Medicine

  • Physicians can monitor their patients closer than ever before. Data collected from wearable trackers provides valuable insight – something, that would be impossible with the usual brief visits.
  • Big data allows hospitals to create statistics about the effectiveness of different treatments and drugs. This not only improves healthcare but can also greatly reduce costs.
  • Data can lead to significant improvements in ER treatment. After a hospital used the information they collected, the length of stay was reduced by 40% and the effectiveness improved by 50%.
  • Local public health can also benefit from big data. It helps city inspectors to prioritize high-risk establishments and catch violations before they become a hazard.

Construction

  • Construction companies are now able to better estimate their price quotes. By analyzing big data and using the industry stats in every country, they can track material-based expenses.
  • Knowing how long a project will take is also much easier when companies can compare it to similar work in the past.
  • After switching to the BDA interface, 98% of sales representatives reported a huge improvement in time, needed to calculate costs.

Transportation

  • Public transport in London uses big data to provide commuters with personalized details and information about delays.
  • Trains’ condition is monitored by a variety of sensors. One hundred trains can create up to 200 billion data points yearly. This improves safety in previously unthinkable ways.

(Sources: Towards Data Science, big data – Made Simple, ScienceDirect, Bernard Marr)

Industries That Are Investing in Big Data

Many industries are intrigued by big data facts. The ones investing most in it are:

  • Banking
  • Manufacturing
  • Professional Services
  • Federal Government

These four industries combined accounted for nearly 50% of the worldwide BDA revenue in 2018 – $81 billion.

Their total investment in 2022 will be $129 billion, giving them the largest opportunity.

The industries, expecting the fastest revenue growth are :

  • Retail – 13.5% CAGR
  • Banking – 13.2% CAGR
  • Professional services – 12.9% CAGR

43% of organizations are changing their structures to take advantage of the big data market.

(Sources: Forbes, DestinationCRM, Gartner)

Popular Big Data Access Methods

Where can you find the biggest data?

Amazon Web Services (AWS) S3

  • AWS S3 is Amazon’s storage service. Its stability is in the 11 9’s – 99.999999999%!
  • Its simple interface and reliable service make AWS S3 one of the most liked big data tools.
  • One of the key factors of Amazon’s success and the reason behind the creation of AWS S3, was big data! Actually, it’s the company’s main source of income, making up 53% of the total revenue.
  • Millions of companies around the globe use AWS S3. Some of the more popular ones include:
    • NASA – particularly images received from the Curiosity rover.
    • Netflix – the company transferred to AWS S3 in 2015.
    • Nokia – they went for this platform to improve scalability.
    • Samsung – the Printing Apps Center was launched on the platform.
    • Slack – they’ve been using AWS S3 since 2009.
    • Adobe – LiveCycle Forms and Connect are two products that run on AWS.
    • Airbnb – their entire database is on the platform.

Spark SQL

  • Spark SQL can read data from both semi-structured and structured data. It also includes columnar storage, code generation and cost-based optimizer.
  • It can connect to Spark programs and external tools like Tableau.
  • Spark SQL simplifies working with structured datasets – it provides DataFrame abstraction in Java, Scala and Python.
  • Some of the companies using this program to manage big data are:
    • UC Berkeley AMPLab
    • Alibaba Taobao
    • Autodesk
    • eBay Inc.
    • IBM Almaden
    • NASA JPL – Deep Space Network
    • Shopify
    • TripAdvisor
    • Yahoo!

Hive

  • Apache Hive simplifies reading, writing and managing large datasets in distributed storage.
  • This big data tool is used mostly in the United States, in companies working with Computer Software. They commonly have over $1 billion in revenue and between 50 and 200 employees. Some examples are:
    • Facebook Inc
    • Hortonworks Inc
    • Qubole
    • Castle Global, Inc.
    • Groupon, Inc.

HDFS

  • The primary data storage of Hadoop applications is the HDFS (Hadoop Distributed File System).
  • HDFS was originally created as a part of the Apache Nutch web search engine project.
  • It’s highly fault-tolerant – a big difference from other distributed file systems.
  • HDFS can run on low-cost hardware.
  • These advantages have convinced many companies to integrate it into their systems. These include:
    • Talentburst
    • Unity Technologies, Inc.
    • Intel
    • Indeed, Inc.
    • Microsoft

(Sources: Zoomdata, CNBC, Amazon, TechRepublic, Network World, Enlyft, Apache, DZone, Apache, Enlyft, Apache)

Most-Adopted Big Data Analytics

Big data is only as useful as your ability to read it. Its potential and effectiveness are facts that more and more companies are realizing. In fact, the BDA in enterprises rose from 17% in 2015 to 59% in 2018!

Big data adoption reached a 36% CAGR. So which tools do companies employ to analyze data?

Apache Spark MLib

  • MLib began as a part of Apache Spark. This is why it’s updated with each new Spark release.
  • The algorithms MLib uses are very high-quality – the results are more accurate than the one-pass approximations on MapReduce.
  • MLib runs fast, thanks to Spark’s strong>iterative computation. For comparison, it’s 100 times speedier than MapReduce!
  • Users are encouraged to help the project grow. They can suggest patches directly to Apache.

TensorFlow

  • TensorFlow is one of the most-adopted big data analytics in enterprises today.
  • Not only does it have an extensive choice of libraries and tools, it’s also fully open source.
  • It makes model building easy, thanks to its intuitive high-level APIs.
  • Users are able to train and deploy machine learning models in the browser, cloud and even on-device.

(Sources: Forbes, Apache, TensorFlow)

Big Data Tools

To harvest big data you need a giant harvester.

Apache Hadoop

  • Hadoop is the software product that always gets mentioned when the topic of BDA arises. It doesn’t require much hardware-wise and can run both on-prem and in the cloud.
  • Hadoop is famous for its huge-scale data processing. It’s an open-source framework and can provide storage for any type of data.
  • Some of the better known features are:
    • HDFS
    • MapReduce
    • YARN
    • Hadoop Libraries

Apache Cassandra

  • Apache Cassandra is well-known for being a very scalable and resilient database. It’s also relatively easy to learn and configure.
  • It’s being used by huge companies like Facebook, Netflix, Twitter and Cisco.
  • Cassandra can handle heavy workloads thanks to its architecture.
  • The stats point to it being is one of the most reliable big data softwares.
  • Apache Cassandra also offers capabilities that no other NoSQL or relational database can. These include:
    • Exceptional linear scalability
    • High fault tolerance
    • Simplicity of operations
    • Built-in high-availability

MongoDB

  • MongoDB is an open source NoSQL database. It’s compatible with a variety of programming languages.
  • This tool is best for working with semi or unstructured data sets or ones that frequently change.
  • MongoDB is also great for data storage from CMS, product catalogs or mobile apps.
  • Some of MongoDB’s capabilities are:
    • Storage of any type of data
    • Cloud-native deployment
    • Flexibility of configuration
    • Database partitioning

Neo4j

  • Neo4j is an open source graph database.
  • The tool performs well even under a heavy workload of data and graph requests.
  • Neo4j’s most prominent features are:
    • Flexibility
    • High-availability and scalability
    • Support of ACID transactions
    • Cypher graph query language
    • Integrations with other DB

(Sources: Analytics Training, Towards Data Science, TechTarget, Whizlabs, IT Svit)

Big Data Use Cases

Let’s see how big data revolutionizes industries already.

Data Warehouse Optimization

  • Many corporations use data warehouses to handle their BI needs. The cheapest and easiest way to manage that information is to utilize open source big data solutions like Hadoop.
  • This ensures faster operation speed and lower costs.
  • The whole “big data vs business intelligence” competition has an obvious winner – traditional BI tools don’t scale when the users and data increase.
  • Customers now look for insights that only ML can provide. This calls for analytical tools that can work with all types of data.
  • Data warehouse optimization aims to facilitate a built-in scalable query mechanism that allows running individual workloads.

Price Optimization

  • BDA can provide companies with valuable insight about which prices have achieved the best results. It’s hard to maximize income without losing customers.
  • Utilizing big data software also allows for dynamic pricing. Companies can now build models predicting how much a customer will be willing to pay, as circumstances change.
  • BDA usage is very common, especially among B2B companies.

Recommendation Engines

  • This is one of the most popular uses of big data analytics.
  • BDA of historical data is why platforms like Amazon and Netflix always seem to know what you’ll like.
  • Most users now expect a recommendation engine when they’re shopping. Therefore, organizations that don’t utilize the data they’ve collected may lose their customers to competitors.

Preventive Maintenance and Support

  • The industrial sector can also benefit from predictive analytics. Companies in energy, agriculture, manufacturing and transportation have already come to this conclusion.
  • A variety of sensors constantly collect data from expensive equipment. They form the  Industrial Internet of Things – IIoT.
  • Analyzing the collected data can help detect malfunctions before they cause an accident. This saves companies a lot of expenses.

(Sources: Datamation, EDUCBA, HPE)

Benefits of Big Data and Big Data Analytics

In case you are doubting it still, big data has incalculable benefits. Just kidding. Proper big data analytics can calculate anything.

Reduced Cost

  • Big data software can help companies improve their processes and customer service. This increased effectiveness can have a big impact on reducing cost.
  • Surveys by Syncsort and NewVantage showed that BDA has helped 59.4% of respondents to decrease expenses.
  • 66.7% of companies stated that they began using big data for that purpose.
  • Almost 55% of respondents are aiming to instead increase their revenue and growth with BDA.

Increased Productivity

  • The high speed at which BDA tools operate allows businesses to make quick decisions.
  • Syncsort study indicates that 59.9% of companies use software like Hadoop to increase their productivity.
  • The big data statistics show that BDA increases both employees’ personal productivity and the effectiveness of operations in larger structures within companies.

New Product Development

  • BDA allows companies to keep up with trends and create successful products.
  • According to a NewVantage survey, 11.6% of executives are investing in big data with the goal of finding means of innovation.
  • The insights BDA offers can help a company pull ahead of their competitors.

Better Decision-Making

  • Big data allows organizations to better understand the constantly changing market conditions. Analyzing what people are purchasing helps companies plan ahead and produce what its customers want.
  • 36.2% of enterprizes interviewed for a NewVantage study stated that better decision-making is why they’re investing in BDA.
  • 59% of companies confirmed they experienced success in this area, thanks to BDA.

Fraud Detection

  • The financial industry is understandably very interested in big data and analytics when it comes to fraud detection.
  • Financial institutions use algorithms based on machine learning, so they excel at finding patterns and anomalies. This allows for a fast reaction in case of fraud.

(Sources: NewGenApps, Datamation, Syncsort, Syncsort,  NewVantage Partners)

Conclusion

Now that you’ve been amazed by all these big data stats, you can continue your cat toys research. Go ahead and teach that AI exactly what entertainment your feline companion prefers. That way you can get some awesome suggestions!