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Big Data Statistics for the Big Future Ahead in Year 2020

Statistics about the Need for Big Data

  • Big Data Statistics: Generally, 40000 search queries are submitted per second on Google alone. That amounts to 1.5 trillion searches yearly
  • Mostly, 250 new hours of video show up on YouTube per minute. That's why there are more than 1 billion gigabytes of data on its servers
  • Basically, people share more than 100 terabytes of data on Facebook per day. Each minute, users send 30 million messages, and also they view 2.5 million videos
  • The usage of big data projects determines people those who take 75% of photos on their smartphones. Further, considering that only this year, over 1.5 billion devices will be shipped worldwide, so we can expect this percentage to increase
  • More than 25% of data can be uploaded to the cloud by next year
  • Smart devices such as sensors, Amazon Echo and fitness trackers produce 6 quintillion bytes of data daily. Also, we can expect the number of these gadgets to be more than 50 billion in the coming years

Major Industries Using Big Data

In general, big data is useful across the board. But the specific industries can benefit from it much more than others

Communication and Media

Since users expect rich media on-demand in several formats and a different type of devices, some significant data challenges in communication and media involve

  • Gathering, analyzing and using user insights
  • Leveraging mobile and social media content
  • Understanding real-time patterns and usage of media content

Healthcare Industry

The consulting firm, McKinsey, estimated that big data analytics adoption could save up to 18% of healthcare costs. In 2021, that can amount to $593 billion in reductions too.

Banking Industry

  • Modern consumers look for a highly personalized experience. As a matter of fact, 85% of executives surveyed by Oracle, agreed to this. Nearly 85% of them believe the solution which lies in IT cloud computing projects development
  • The adoption of big data in this industry can provide 18% increase in revenue. For a $1 billion company, this would come up to $180 million per year

Energy and Utility Industry

  • The value of big data got its fair share of attention by the energy field too. General Elastic massively improved their efficiency by using details from sensors on engines and turbines
  • In fact, the company estimates big data can improve US productivity by 1.6% Year over Year. Since those numbers stack up nicely in the long run

Retail Industry

  • Commonly, the surprising stats about big data did not go unnoticed by Amazon. In fact, the massive amounts of data were why they created AWS- their own cloud computing platform
  • Amazon creates an individual 360-degree view profile of each consumer. Also, the group you with others with similar interests to suggest products you will like
  • Prior to 2016, the company hardly had any profit. Afterwards, with the introduction of AWS, Amazon's income massively increased. In 2017, they earned $4 billion and in 2018 - $10.5 billion
  • The company Starbucks would not have been the coffeehouse chain we know, had they ignored the statistics about big data analytics. In fact, their business has constantly been growing and thanks to their smart details is 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
  • Further, the Starbucks mobile application has more than 18 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. In fact, the strategy is working very well. Hence, Starbucks will have 39000 stores worldwide by 2021.

Major Industries That are Moving Fast towards Big Data

As you know already, big data predictions 2020 reaches far. Furthermore, the significant industries those are moving very fast towards big data


  • As a matter of fact, construction industries are able to estimate their price quotes better. By analyzing big data and using the industry stats in each country, they will track material-based expenses
  • Moreover, knowing how long a project will take is also much easy when companies can compare it to similar work in the past


  • In London, public transport uses big data to deliver commuters along with personalized information about delays
  • Several sensors can monitor the condition of the train. One hundred trains can create up to 200 billion data points yearly. This means it increases safety in unthinkable ways

Big Data Tools

In order to, harvest the big data, you need a giant harvester. Some of the big data tools such as

  • Apache Hadoop
  • Apache Cassandra
  • MongoDB
  • Neo4j

Apache Hadoop

  • Generally, Hadoop is the software that helps to gets monitored when the topic of BDA arises. In fact, it does not need much hardware-wise and it can run both on-prem and in the cloud
  • As well as, Hadoop is the most popular for its huge-scale data processing. It is an open source framework and it delivers storage for any data type

Some of the better known features such as

  • HDFS
  • YARN
  • Hadoop Libraries
  • MapReduce


  • MongoDB is an open-source NoSQL database. It is very compatible with several programming languages
  • This MongoDB tool is well suitable for working with semi or unstructured data sets or one that frequently change

MongoDB can be able to

  • Cloud-Native deployment
  • Database partitioning
  • Configuration flexibility
  • Database storage


  • Neo4j is an open-source graph database
  • The tool performs well, even under a heavy workload of data and graph requests

The most prominent features such as

  • Flexibility
  • Scalability
  • Support for ACID transactions
  • Cypher graph query language
  • Integrations with other DB

Apache Cassandra

  • Apache Cassandra is used by major companies such as NetFlix, Twitter, Cisco and Facebook
  • It provide capabilities which no other NoSQL or relational database

These includes

  • High Fault Tolerance
  • Simplicity of operations
  • Built-in-high availability
  • Linear Scalability

Key Takeaways from Big Data Statistics 2020

In the final analysis, the amount of raw data that has been generated around us is an increasing exponentially. In fact, the big data becomes important when it is combined with effective big data management tools, so that insights can be derived from the mass of raw data.

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