How big MNC’s like Google, Facebook, Instagram, Netflix etc stores, manages and manipulate Thousands of Terabytes of data with High Speed and High Efficiency.
What is big data ?
Big data is a term that describes the large volume of data — both structured and unstructured — that inundates a business on a day-to-day basis.
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.
So Big data is it self a problem but yes we have now some solution to manage, processes and to analyze these data. To solving the problem of Volume and Velocity we have now Distributed storage system concept.
Distributed storage concept
In recent years, building a large-scale distributed storage system has become a hot topic. Distributed consensus algorithms like Paxos and Raft are the focus of many technical articles. But those articles tend to be introductory, describing the basics of the algorithm and log replication. They seldom cover how to build a large-scale distributed storage system based on the distributed consensus algorithm.
The first one is HDFS for storage (Hadoop distributed File System), that allows you to store data of various formats across a cluster. The second one is YARN, for resource management in Hadoop. It allows parallel processing over the data, i.e. stored across HDFS.
How are the top MNCs using big data for there Advantage
1. Using big data analytics to Boost Customer Acquisition and Retention
It is good thing to know that your business has all the data you need to truly know and understand your customers. The key is to make sure you have the required big data analytics to get the most out of the data at your disposal, depending on the type of business you are in. Accurate analytics will give your business the capability to derive critical behavioral insights you can act upon.
Sometimes, acquiring a new customer can cost your business hundreds of dollars. It is a lot cheaper to retain the ones you already have. If the operations of your business call for the ability to process high-velocity, high-volume data with minimal latency or if you deal with live data that needs to be used while still in motion, stream processing can help you out. This type of data processing solution gives you the access to continuous/live data that you can integrate with historical data for more context.
Implementing better big data analysis helps you increase customer loyalty. You will be able to act upon the insights you get fast, giving you an opportunity to meet the needs to the consumer effortlessly. Here are some of the big data strategies your business can use to derive insights from customer satisfaction , drive customer loyalty, and be more competitive.
2. Use of big data Analysis to solve Advertiser problem and giving Market insights
Using the power of data analytics, advertisers can identify emerging trends and provide real-time live ad options. Since the key component of advertising is reaching the right audience at the right time, big data will help predict purchase , identify and analyze consumer behavior and the type of performance that certain segments of your audiences will perform against.
3. Big data Analytics as a Driver of Innovation and Product Development
To overcome challenges from relatively new competitors (e.g., Hyundai, Skoda, and Tata), Ford Motors is using consumer analytics to start its own revolution in product innovation and design. Ford captures primary consumer data from around four million of its vehicles on the road through sensors and remote app-management software . After analyzing the data collected from the car’s voice-recognition system, Ford realized that the immediate surrounding noise interfered with the software’s ability to understand driver commands, leading to the introduction of automatic noise reduction technology and the repositioning of microphones to better capture the driver’s voice . Ford facilitates product innovation in a rapid manner using Big Data without waiting for insights from traditional marketing research such as focus groups and surveys
4. Use of Big data in Supply chain Management
Big data is also helping companies manage more responsive supply chains as they can better comprehend customers and market trends, and thus, are able to predict and proactively strategist supply chain-related activities.
Examples of how some MNCs are using Big Data Analytics
Since launching in early 2004, Facebook has grown into an online community where 890 million individuals log on each day. People share thoughts, photos, links and videos and like, share and comment on each other’s status updates. Users have uploaded a staggering 250 billion photos, with 350 million new photos each day.There’s a lot of data stored on Facebook, and a lot of its users’ own content. That content is the most important asset on the service, and users need to believe it’s secure, otherwise they won’t share. Getting storage right is critical.
Facebook designs its own servers and networking. It designs and builds its own data centers. Its staff writes most of its own applications and creates virtually all of its own middle ware. Everything about its operational IT unites it in one extremely large system that is used by internal and external folks alike.
Instead of servers that include compute, memory, flash storage and HDD storage, Facebook’s disaggregated server model splits the various server components across separate racks, allowing it to tune the components for specific services and to use what Qin calls “smarter hardware refreshes” to extend useful life. By separating server resources mixes of compute, memory and storage on different racks can be combined, for example, to deliver a set of servers that can run Hadoop. As loads and usage change, the balance of components that power a service can be changed — keeping inefficiencies to a minimum.
So, how does Netflix use data analytics? By collecting data from their 151 million subscribers, and implementing data analytics models to discover customer behaviour and buying patterns. Then, using that information to recommend movies and TV shows based on their subscribers’ preferences.
With a company valuation of over $164 billion, Netflix has surpassed Disney as the most valued media company in the world. Their success can be attributed to their impressive customer retention rate, which is 93% compared to Hulu’s 64% and Amazon Prime’s 75%. However, it’s not just their ability to retain most of their 151 million subscribers that have made them successful.
According to Netflix, over 75% of viewer activity is based off personalised recommendations. Netflix collects several data points to create a detailed profile on its subscribers. The profile is far more detailed than the personas created through conventional marketing.
Back when Jeff Bezos filled orders in his garage and drove packages to the post office himself, crunching the numbers on costs, tracking inventory and forecasting future demand was relatively simple. Fast-forward 25 years, Amazon’s retail business has more than 175 fulfillment centers worldwide with more than 250,000 full-time associates shipping millions of items per day.
The online retail goliath has got access to a gigantic measure of information on its clients; names, locations, installments, and search accounts are altogether documented in its information bank. While this data is put to use in publicizing calculations, Amazon likewise utilizes the data to improve client relations, a region that numerous big data users disregard.
Whenever you contact the Amazon help work area with an inquiry, don’t be astounded when the worker on the opposite end has already received a large portion of the relevant data about you close by. The applicable data takes into consideration a quicker, progressively practical client administration experience that does exclude illuminating your name multiple times.
Google has made Big Data solutions even more reachable, available, and usable- to both companies who want to invest (Google Cloud Solutions), as well as those that don’t have the financial resources to do so (Data Solutions for Change program).
This product represents a suite of integrated Data Analytics tools, including BigQuery, Google Data Studio, Sheets, and others. It provides flexibility, scalability, collaboration, and achievement of advanced analytics to the business entities.
Besides, it enables the companies to analyze and process Big data to derive business insights, which suit the business objectives and use those for enhancing their level of decision-making, planning, productivity, and success.
This solution enables companies to integrate, store, and use all the Big Data they generate. Google’s data lake facilitates organizations to easily manage their data migration across networks, as well as to store it into a flexible, cost-wise, and powerful storage.
The solution supports the storage of omni-structure and omni-format data, which can be extracted for analytical purposes at any needed time, with full flexibility of data processing, and instant data querying for highly-efficient and quick analytics results.
Internet of Things
Google Cloud IoT offers a set of tools to connect, process, store, and analyze data both at the edge and in the cloud. What is more, it provides the opportunity for ML (machine learning) and IoT integration in order to ensure solutions to every occurred business need.
Besides, companies are enabled to get real-time business insights, analytics, and visualization of results to make the collected data comprehensible and accessible by every level of the organizational hierarchy.
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