case study of big data solutions

8 case studies and real world examples of how Big Data has helped keep on top of competition

Fast, data-informed decision-making can drive business success. Managing high customer expectations, navigating marketing challenges, and global competition – many organizations look to data analytics and business intelligence for a competitive advantage.

Using data to serve up personalized ads based on browsing history, providing contextual KPI data access for all employees and centralizing data from across the business into one digital ecosystem so processes can be more thoroughly reviewed are all examples of business intelligence.

Organizations invest in data science because it promises to bring competitive advantages.

Data is transforming into an actionable asset, and new tools are using that reality to move the needle with ML. As a result, organizations are on the brink of mobilizing data to not only predict the future but also to increase the likelihood of certain outcomes through prescriptive analytics.

Here are some case studies that show some ways BI is making a difference for companies around the world:

1) Starbucks:

With 90 million transactions a week in 25,000 stores worldwide the coffee giant is in many ways on the cutting edge of using big data and artificial intelligence to help direct marketing, sales and business decisions

Through its popular loyalty card program and mobile application, Starbucks owns individual purchase data from millions of customers. Using this information and BI tools, the company predicts purchases and sends individual offers of what customers will likely prefer via their app and email. This system draws existing customers into its stores more frequently and increases sales volumes.

The same intel that helps Starbucks suggest new products to try also helps the company send personalized offers and discounts that go far beyond a special birthday discount. Additionally, a customized email goes out to any customer who hasn’t visited a Starbucks recently with enticing offers—built from that individual’s purchase history—to re-engage them.

2) Netflix:

The online entertainment company’s 148 million subscribers give it a massive BI advantage.

Netflix has digitized its interactions with its 151 million subscribers. It collects data from each of its users and with the help of data analytics understands the behavior of subscribers and their watching patterns. It then leverages that information to recommend movies and TV shows customized as per the subscriber’s choice and preferences.

As per Netflix, around 80% of the viewer’s activity is triggered by personalized algorithmic recommendations. Where Netflix gains an edge over its peers is that by collecting different data points, it creates detailed profiles of its subscribers which helps them engage with them better.

The recommendation system of Netflix contributes to more than 80% of the content streamed by its subscribers which has helped Netflix earn a whopping one billion via customer retention. Due to this reason, Netflix doesn’t have to invest too much on advertising and marketing their shows. They precisely know an estimate of the people who would be interested in watching a show.

3) Coca-Cola:

Coca Cola is the world’s largest beverage company, with over 500 soft drink brands sold in more than 200 countries. Given the size of its operations, Coca Cola generates a substantial amount of data across its value chain – including sourcing, production, distribution, sales and customer feedback which they can leverage to drive successful business decisions.

Coca Cola has been investing extensively in research and development, especially in AI, to better leverage the mountain of data it collects from customers all around the world. This initiative has helped them better understand consumer trends in terms of price, flavors, packaging, and consumer’ preference for healthier options in certain regions.

With 35 million Twitter followers and a whopping 105 million Facebook fans, Coca-Cola benefits from its social media data. Using AI-powered image-recognition technology, they can track when photographs of its drinks are posted online. This data, paired with the power of BI, gives the company important insights into who is drinking their beverages, where they are and why they mention the brand online. The information helps serve consumers more targeted advertising, which is four times more likely than a regular ad to result in a click.

Coca Cola is increasingly betting on BI, data analytics and AI to drive its strategic business decisions. From its innovative free style fountain machine to finding new ways to engage with customers, Coca Cola is well-equipped to remain at the top of the competition in the future. In a new digital world that is increasingly dynamic, with changing customer behavior, Coca Cola is relying on Big Data to gain and maintain their competitive advantage.

4) American Express GBT

The American Express Global Business Travel company, popularly known as Amex GBT, is an American multinational travel and meetings programs management corporation which operates in over 120 countries and has over 14,000 employees.

Challenges:

Scalability – Creating a single portal for around 945 separate data files from internal and customer systems using the current BI tool would require over 6 months to complete. The earlier tool was used for internal purposes and scaling the solution to such a large population while keeping the costs optimum was a major challenge

Performance – Their existing system had limitations shifting to Cloud. The amount of time and manual effort required was immense

Data Governance – Maintaining user data security and privacy was of utmost importance for Amex GBT

The company was looking to protect and increase its market share by differentiating its core services and was seeking a resource to manage and drive their online travel program capabilities forward. Amex GBT decided to make a strategic investment in creating smart analytics around their booking software.

The solution equipped users to view their travel ROI by categorizing it into three categories cost, time and value. Each category has individual KPIs that are measured to evaluate the performance of a travel plan.

Reducing travel expenses by 30%

Time to Value – Initially it took a week for new users to be on-boarded onto the platform. With Premier Insights that time had now been reduced to a single day and the process had become much simpler and more effective.

Savings on Spends – The product notifies users of any available booking offers that can help them save on their expenditure. It recommends users of possible saving potential such as flight timings, date of the booking, date of travel, etc.

Adoption – Ease of use of the product, quick scale-up, real-time implementation of reports, and interactive dashboards of Premier Insights increased the global online adoption for Amex GBT

5) Airline Solutions Company: BI Accelerates Business Insights

Airline Solutions provides booking tools, revenue management, web, and mobile itinerary tools, as well as other technology, for airlines, hotels and other companies in the travel industry.

Challenge: The travel industry is remarkably dynamic and fast paced. And the airline solution provider’s clients needed advanced tools that could provide real-time data on customer behavior and actions.

They developed an enterprise travel data warehouse (ETDW) to hold its enormous amounts of data. The executive dashboards provide near real-time insights in user-friendly environments with a 360-degree overview of business health, reservations, operational performance and ticketing.

Results: The scalable infrastructure, graphic user interface, data aggregation and ability to work collaboratively have led to more revenue and increased client satisfaction.

6) A specialty US Retail Provider: Leveraging prescriptive analytics

Challenge/Objective: A specialty US Retail provider wanted to modernize its data platform which could help the business make real-time decisions while also leveraging prescriptive analytics. They wanted to discover true value of data being generated from its multiple systems and understand the patterns (both known and unknown) of sales, operations, and omni-channel retail performance.

We helped build a modern data solution that consolidated their data in a data lake and data warehouse, making it easier to extract the value in real-time. We integrated our solution with their OMS, CRM, Google Analytics, Salesforce, and inventory management system. The data was modeled in such a way that it could be fed into Machine Learning algorithms; so that we can leverage this easily in the future.

The customer had visibility into their data from day 1, which is something they had been wanting for some time. In addition to this, they were able to build more reports, dashboards, and charts to understand and interpret the data. In some cases, they were able to get real-time visibility and analysis on instore purchases based on geography!

7) Logistics startup with an objective to become the “Uber of the Trucking Sector” with the help of data analytics

Challenge: A startup specializing in analyzing vehicle and/or driver performance by collecting data from sensors within the vehicle (a.k.a. vehicle telemetry) and Order patterns with an objective to become the “Uber of the Trucking Sector”

Solution: We developed a customized backend of the client’s trucking platform so that they could monetize empty return trips of transporters by creating a marketplace for them. The approach used a combination of AWS Data Lake, AWS microservices, machine learning and analytics.

• Reduced fuel costs
• Optimized Reloads
• More accurate driver / truck schedule planning
• Smarter Routing
• Fewer empty return trips
• Deeper analysis of driver patterns, breaks, routes, etc.

8) Challenge/Objective: A niche segment customer competing against market behemoths looking to become a “Niche Segment Leader”

Solution: We developed a customized analytics platform that can ingest CRM, OMS, Ecommerce, and Inventory data and produce real time and batch driven analytics and AI platform. The approach used a combination of AWS microservices, machine learning and analytics.

• Reduce Customer Churn
• Optimized Order Fulfillment
• More accurate demand schedule planning
• Improve Product Recommendation
• Improved Last Mile Delivery

How can we help you harness the power of data?

At Systems Plus our BI and analytics specialists help you leverage data to understand trends and derive insights by streamlining the searching, merging, and querying of data. From improving your CX and employee performance to predicting new revenue streams, our BI and analytics expertise helps you make data-driven decisions for saving costs and taking your growth to the next level.

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Big data case study: How UPS is using analytics to improve performance

A new initiative at UPS will use real-time data, advanced analytics and artificial intelligence to help employees make better decisions.

As chief information and engineering officer for logistics giant UPS, Juan Perez is placing analytics and insight at the heart of business operations.

Big data and digital transformation: How one enables the other

Drowning in data is not the same as big data. Here's the true definition of big data and a powerful example of how it's being used to power digital transformation.

"Big data at UPS takes many forms because of all the types of information we collect," he says. "We're excited about the opportunity of using big data to solve practical business problems. We've already had some good experience of using data and analytics and we're very keen to do more."

Perez says UPS is using technology to improve its flexibility, capability, and efficiency, and that the right insight at the right time helps line-of-business managers to improve performance.

The aim for UPS, says Perez, is to use the data it collects to optimise processes, to enable automation and autonomy, and to continue to learn how to improve its global delivery network.

Leading data-fed projects that change the business for the better

Perez says one of his firm's key initiatives, known as Network Planning Tools, will help UPS to optimise its logistics network through the effective use of data. The system will use real-time data, advanced analytics and artificial intelligence to help employees make better decisions. The company expects to begin rolling out the initiative from the first quarter of 2018.

"That will help all our business units to make smart use of our assets and it's just one key project that's being supported in the organisation as part of the smart logistics network," says Perez, who also points to related and continuing developments in Orion (On-road Integrated Optimization and Navigation), which is the firm's fleet management system.

Orion uses telematics and advanced algorithms to create optimal routes for delivery drivers. The IT team is currently working on the third version of the technology, and Perez says this latest update to Orion will provide two key benefits to UPS.

First, the technology will include higher levels of route optimisation which will be sent as navigation advice to delivery drivers. "That will help to boost efficiency," says Perez.

Second, Orion will use big data to optimise delivery routes dynamically.

"Today, Orion creates delivery routes before drivers leave the facility and they stay with that static route throughout the day," he says. "In the future, our system will continually look at the work that's been completed, and that still needs to be completed, and will then dynamically optimise the route as drivers complete their deliveries. That approach will ensure we meet our service commitments and reduce overall delivery miles."

Once Orion is fully operational for more than 55,000 drivers this year, it will lead to a reduction of about 100 million delivery miles -- and 100,000 metric tons of carbon emissions. Perez says these reductions represent a key measure of business efficiency and effectiveness, particularly in terms of sustainability.

Projects such as Orion and Network Planning Tools form part of a collective of initiatives that UPS is using to improve decision making across the package delivery network. The firm, for example, recently launched the third iteration of its chatbot that uses artificial intelligence to help customers find rates and tracking information across a series of platforms, including Facebook and Amazon Echo.

"That project will continue to evolve, as will all our innovations across the smart logistics network," says Perez. "Everything runs well today but we also recognise there are opportunities for continuous improvement."

Overcoming business challenges to make the most of big data

"Big data is all about the business case -- how effective are we as an IT team in defining a good business case, which includes how to improve our service to our customers, what is the return on investment and how will the use of data improve other aspects of the business," says Perez.

These alternative use cases are not always at the forefront of executive thinking. Consultant McKinsey says too many organisations drill down on a single data set in isolation and fail to consider what different data sets mean for other parts of the business.

However, Perez says the re-use of information can have a significant impact at UPS. Perez talks, for example, about using delivery data to help understand what types of distribution solutions work better in different geographical locations.

"Should we have more access points? Should we introduce lockers? Should we allow drivers to release shipments without signatures? Data, technology, and analytics will improve our ability to answer those questions in individual locations -- and those benefits can come from using the information we collect from our customers in a different way," says Perez.

Perez says this fresh, open approach creates new opportunities for other data-savvy CIOs. "The conversation in the past used to be about buying technology, creating a data repository and discovering information," he says. "Now the conversation is changing and it's exciting. Every time we talk about a new project, the start of the conversation includes data."

By way of an example, Perez says senior individuals across the organisation now talk as a matter of course about the potential use of data in their line-of-business and how that application of insight might be related to other models across the organisation.

These senior executive, he says, also ask about the availability of information and whether the existence of data in other parts of the business will allow the firm to avoid a duplication of effort.

"The conversation about data is now much more active," says Perez. "That higher level of collaboration provides benefits for everyone because the awareness across the organisation means we'll have better repositories, less duplication and much more effective data models for new business cases in the future."

Read more about big data

• Turning big data into business insights: The state of play
• Choosing the best big data partners: Eight questions to ask
• Report shows that AI is more important to IoT than big data insights

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A growing number of enterprises are pooling terabytes and petabytes of data, but many of them are grappling with ways to apply their big data as it grows. 

How can companies determine what big data solutions will work best for their industry, business model, and specific data science goals? 

Check out these big data enterprise case studies from some of the top big data companies and their clients to learn about the types of solutions that exist for big data management.

Enterprise case studies

Netflix on aws, accuweather on microsoft azure, china eastern airlines on oracle cloud, etsy on google cloud, mlogica on sap hana cloud.

Read next: Big Data Market Review 2021

Netflix is one of the largest media and technology enterprises in the world, with thousands of shows that its hosts for streaming as well as its growing media production division. Netflix stores billions of data sets in its systems related to audiovisual data, consumer metrics, and recommendation engines. The company required a solution that would allow it to store, manage, and optimize viewers’ data. As its studio has grown, Netflix also needed a platform that would enable quicker and more efficient collaboration on projects.

“Amazon Kinesis Streams processes multiple terabytes of log data each day. Yet, events show up in our analytics in seconds,” says John Bennett, senior software engineer at Netflix. 

“We can discover and respond to issues in real-time, ensuring high availability and a great customer experience.”

Industries: Entertainment, media streaming

Use cases: Computing power, storage scaling, database and analytics management, recommendation engines powered through AI/ML, video transcoding, cloud collaboration space for production, traffic flow processing, scaled email and communication capabilities

• Now using over 100,000 server instances on AWS for different operational functions
• Used AWS to build a studio in the cloud for content production that improves collaborative capabilities
• Produced entire seasons of shows via the cloud during COVID-19 lockdowns
• Scaled and optimized mass email capabilities with Amazon Simple Email Service (Amazon SES)
• Netflix’s Amazon Kinesis Streams-based solution now processes billions of traffic flows daily

Read the full Netflix on AWS case study here .

AccuWeather is one of the oldest and most trusted providers of weather forecast data. The weather company provides an API that other companies can use to embed their weather content into their own systems. AccuWeather wanted to move its data processes to the cloud. However, the traditional GRIB 2 data format for weather data is not supported by most data management platforms. With Microsoft Azure, Azure Data Lake Storage, and Azure Databricks (AI), AccuWeather was able to find a solution that would convert the GRIB 2 data, analyze it in more depth than before, and store this data in a scalable way.

“With some types of severe weather forecasts, it can be a life-or-death scenario,” says Christopher Patti, CTO at AccuWeather. 

“With Azure, we’re agile enough to process and deliver severe weather warnings rapidly and offer customers more time to respond, which is important when seconds count and lives are on the line.”

Industries: Media, weather forecasting, professional services

Use cases: Making legacy and traditional data formats usable for AI-powered analysis, API migration to Azure, data lakes for storage, more precise reporting and scaling

• GRIB 2 weather data made operational for AI-powered next-generation forecasting engine, via Azure Databricks
• Delta lake storage layer helps to create data pipelines and more accessibility
• Improved speed, accuracy, and localization of forecasts via machine learning
• Real-time measurement of API key usage and performance
• Ability to extract weather-related data from smart-city systems and self-driving vehicles

Read the full AccuWeather on Microsoft Azure case study here .

China Eastern Airlines is one of the largest airlines in the world that is working to improve safety, efficiency, and overall customer experience through big data analytics. With Oracle’s cloud setup and a large portfolio of analytics tools, it now has access to more in-flight, aircraft, and customer metrics.

“By processing and analyzing over 100 TB of complex daily flight data with Oracle Big Data Appliance, we gained the ability to easily identify and predict potential faults and enhanced flight safety,” says Wang Xuewu, head of China Eastern Airlines’ data lab.  

“The solution also helped to cut fuel consumption and increase customer experience.”

Industries: Airline, travel, transportation

Use cases: Increased flight safety and fuel efficiency, reduced operational costs, big data analytics

• Optimized big data analysis to analyze flight angle, take-off speed, and landing speed, maximizing predictive analytics for engine and flight safety
• Multi-dimensional analysis on over 60 attributes provides advanced metrics and recommendations to improve aircraft fuel use
• Advanced spatial analytics on the travelers’ experience, with metrics covering in-flight cabin service, baggage, ground service, marketing, flight operation, website, and call center
• Using Oracle Big Data Appliance to integrate Hadoop data from aircraft sensors, unifying and simplifying the process for evaluating device health across an aircraft
• Central interface for daily management of real-time flight data

Read the full China Eastern Airlines on Oracle Cloud case study here .  

Etsy is an e-commerce site for independent artisan sellers. With its goal to create a buying and selling space that puts the individual first, Etsy wanted to advance its platform to the cloud to keep up with needed innovations. But it didn’t want to lose the personal touches or values that drew customers in the first place. Etsy chose Google for cloud migration and big data management for several primary reasons: Google’s advanced features that back scalability, its commitment to sustainability, and the collaborative spirit of the Google team.

Mike Fisher, CTO at Etsy, explains how Google’s problem-solving approach won them over. 

“We found that Google would come into meetings, pull their chairs up, meet us halfway, and say, ‘We don’t do that, but let’s figure out a way that we can do that for you.'”

Industries: Retail, E-commerce

Use cases: Data center migration to the cloud, accessing collaboration tools, leveraging machine learning (ML) and artificial intelligence (AI), sustainability efforts

• 5.5 petabytes of data migrated from existing data center to Google Cloud
• >50% savings in compute energy, minimizing total carbon footprint and energy usage
• 42% reduced compute costs and improved cost predictability through virtual machine (VM), solid state drive (SSD), and storage optimizations
• Democratization of cost data for Etsy engineers
• 15% of Etsy engineers moved from system infrastructure management to customer experience, search, and recommendation optimization

Read the full Etsy on Google Cloud case study here .

mLogica is a technology and product consulting firm that wanted to move to the cloud, in order to better support its customers’ big data storage and analytics needs. Although it held on to its existing data analytics platform, CAP*M, mLogica relied on SAP HANA Cloud to move from on-premises infrastructure to a more scalable cloud structure.

“More and more of our clients are moving to the cloud, and our solutions need to keep pace with this trend,” says Michael Kane, VP of strategic alliances and marketing, mLogica 

“With CAP*M on SAP HANA Cloud, we can future-proof clients’ data setups.”

Industry: Professional services

Use cases: Manage growing pools of data from multiple client accounts, improve slow upload speeds for customers, move to the cloud to avoid maintenance of on-premises infrastructure, integrate the company’s existing big data analytics platform into the cloud

• SAP HANA Cloud launched as the cloud platform for CAP*M, mLogica’s big data analytics tool, to improve scalability
• Data analysis now enabled on a petabyte scale
• Simplified database administration and eliminated additional hardware and maintenance needs
• Increased control over total cost of ownership
• Migrated existing customer data setups through SAP IQ into SAP HANA, without having to adjust those setups for a successful migration

Read the full mLogica on SAP HANA Cloud case study here .

Read next: Big Data Trends in 2021 and The Future of Big Data

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GE’s Big Bet on Data and Analytics

Seeking opportunities in the internet of things, ge expands into industrial analytics., february 18, 2016, by: laura winig.

If software experts truly knew what Jeff Immelt and GE Digital were doing, there’s no other software company on the planet where they would rather be. –Bill Ruh, CEO of GE Digital and CDO for GE

In September 2015, multinational conglomerate General Electric (GE) launched an ad campaign featuring a recent college graduate, Owen, excitedly breaking the news to his parents and friends that he has just landed a computer programming job — with GE. Owen tries to tell them that he will be writing code to help machines communicate, but they’re puzzled; after all, GE isn’t exactly known for its software. In one ad, his friends feign excitement, while in another, his father implies Owen may not be macho enough to work at the storied industrial manufacturing company.

Owen's Hammer

Ge's ad campaign aimed at millennials emphasizes its new digital direction..

The campaign was designed to recruit Millennials to join GE as Industrial Internet developers and remind them — using GE’s new watchwords, “The digital company. That’s also an industrial company.” — of GE’s massive digital transformation effort. GE has bet big on the Industrial Internet — the convergence of industrial machines, data, and the Internet (also referred to as the Internet of Things) — committing $1 billion to put sensors on gas turbines, jet engines, and other machines; connect them to the cloud; and analyze the resulting flow of data to identify ways to improve machine productivity and reliability. “GE has made significant investment in the Industrial Internet,” says Matthias Heilmann, Chief Digital Officer of GE Oil & Gas Digital Solutions. “It signals this is real, this is our future.”

While many software companies like SAP, Oracle, and Microsoft have traditionally been focused on providing technology for the back office, GE is leading the development of a new breed of operational technology (OT) that literally sits on top of industrial machinery.

About the Author

Laura Winig is a contributing editor to MIT Sloan Management Review .

1. Predix is a trademark of General Electric Company.

2. M. LaWell, “Building the Industrial Internet With GE,” IndustryWeek, October 5, 2015.

3. D. Floyer, “Defining and Sizing the Industrial Internet,” June 27, 2013, http://wikibon.org.

i. S. Higginbotham, “BP Teams Up With GE to Make Its Oil Wells Smart,” Fortune, July 8, 2015.

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What is Big Data?

Big data refers to extremely large and diverse collections of structured, unstructured, and semi-structured data that continues to grow exponentially over time. These datasets are so huge and complex in volume, velocity, and variety, that traditional data management systems cannot store, process, and analyze them. 

The amount and availability of data is growing rapidly, spurred on by digital technology advancements, such as connectivity, mobility, the Internet of Things (IoT), and artificial intelligence (AI). As data continues to expand and proliferate, new big data tools are emerging to help companies collect, process, and analyze data at the speed needed to gain the most value from it. 

Big data describes large and diverse datasets that are huge in volume and also rapidly grow in size over time. Big data is used in machine learning, predictive modeling, and other advanced analytics to solve business problems and make informed decisions.

Read on to learn the definition of big data, some of the advantages of big data solutions, common big data challenges, and how Google Cloud is helping organizations build their data clouds to get more value from their data. 

Big data examples

Data can be a company’s most valuable asset. Using big data to reveal insights can help you understand the areas that affect your business—from market conditions and customer purchasing behaviors to your business processes. 

Here are some big data examples that are helping transform organizations across every industry: 

• Tracking consumer behavior and shopping habits to deliver hyper-personalized retail product recommendations tailored to individual customers
• Monitoring payment patterns and analyzing them against historical customer activity to detect fraud in real time
• Combining data and information from every stage of an order’s shipment journey with hyperlocal traffic insights to help fleet operators optimize last-mile delivery
• Using AI-powered technologies like natural language processing to analyze unstructured medical data (such as research reports, clinical notes, and lab results) to gain new insights for improved treatment development and enhanced patient care
• Using image data from cameras and sensors, as well as GPS data, to detect potholes and improve road maintenance in cities
• Analyzing public datasets of satellite imagery and geospatial datasets to visualize, monitor, measure, and predict the social and environmental impacts of supply chain operations

These are just a few ways organizations are using big data to become more data-driven so they can adapt better to the needs and expectations of their customers and the world around them. 

The Vs of big data

Big data definitions may vary slightly, but it will always be described in terms of volume, velocity, and variety. These big data characteristics are often referred to as the “3 Vs of big data” and were first defined by Gartner in 2001. 

In addition to these three original Vs, three others that are often mentioned in relation to harnessing the power of big data: veracity , variability , and value .  

• Veracity : Big data can be messy, noisy, and error-prone, which makes it difficult to control the quality and accuracy of the data. Large datasets can be unwieldy and confusing, while smaller datasets could present an incomplete picture. The higher the veracity of the data, the more trustworthy it is.
• Variability: The meaning of collected data is constantly changing, which can lead to inconsistency over time. These shifts include not only changes in context and interpretation but also data collection methods based on the information that companies want to capture and analyze.
• Value: It’s essential to determine the business value of the data you collect. Big data must contain the right data and then be effectively analyzed in order to yield insights that can help drive decision-making. 

How does big data work?

The central concept of big data is that the more visibility you have into anything, the more effectively you can gain insights to make better decisions, uncover growth opportunities, and improve your business model. 

Making big data work requires three main actions: 

• Integration: Big data collects terabytes, and sometimes even petabytes, of raw data from many sources that must be received, processed, and transformed into the format that business users and analysts need to start analyzing it. 
• Management: Big data needs big storage, whether in the cloud, on-premises, or both. Data must also be stored in whatever form required. It also needs to be processed and made available in real time. Increasingly, companies are turning to cloud solutions to take advantage of the unlimited compute and scalability.  
• Analysis: The final step is analyzing and acting on big data—otherwise, the investment won’t be worth it. Beyond exploring the data itself, it’s also critical to communicate and share insights across the business in a way that everyone can understand. This includes using tools to create data visualizations like charts, graphs, and dashboards. 

Big data benefits

Improved decision-making.

Big data is the key element to becoming a data-driven organization. When you can manage and analyze your big data, you can discover patterns and unlock insights that improve and drive better operational and strategic decisions.

Increased agility and innovation

Big data allows you to collect and process real-time data points and analyze them to adapt quickly and gain a competitive advantage. These insights can guide and accelerate the planning, production, and launch of new products, features, and updates. 

Better customer experiences

Combining and analyzing structured data sources together with unstructured ones provides you with more useful insights for consumer understanding, personalization, and ways to optimize experience to better meet consumer needs and expectations.

Continuous intelligence

Big data allows you to integrate automated, real-time data streaming with advanced data analytics to continuously collect data, find new insights, and discover new opportunities for growth and value. 

More efficient operations

Using big data analytics tools and capabilities allows you to process data faster and generate insights that can help you determine areas where you can reduce costs, save time, and increase your overall efficiency. 

Improved risk management

Analyzing vast amounts of data helps companies evaluate risk better—making it easier to identify and monitor all potential threats and report insights that lead to more robust control and mitigation strategies.

Challenges of implementing big data analytics

While big data has many advantages, it does present some challenges that organizations must be ready to tackle when collecting, managing, and taking action on such an enormous amount of data. 

The most commonly reported big data challenges include: 

• Lack of data talent and skills. Data scientists, data analysts, and data engineers are in short supply—and are some of the most highly sought after (and highly paid) professionals in the IT industry. Lack of big data skills and experience with advanced data tools is one of the primary barriers to realizing value from big data environments. 
• Speed of data growth. Big data, by nature, is always rapidly changing and increasing. Without a solid infrastructure in place that can handle your processing, storage, network, and security needs, it can become extremely difficult to manage. 
• Problems with data quality. Data quality directly impacts the quality of decision-making, data analytics, and planning strategies. Raw data is messy and can be difficult to curate. Having big data doesn’t guarantee results unless the data is accurate, relevant, and properly organized for analysis. This can slow down reporting, but if not addressed, you can end up with misleading results and worthless insights. 
• Compliance violations. Big data contains a lot of sensitive data and information, making it a tricky task to continuously ensure data processing and storage meet data privacy and regulatory requirements, such as data localization and data residency laws. 
• Integration complexity. Most companies work with data siloed across various systems and applications across the organization. Integrating disparate data sources and making data accessible for business users is complex, but vital, if you hope to realize any value from your big data. 
• Security concerns. Big data contains valuable business and customer information, making big data stores high-value targets for attackers. Since these datasets are varied and complex, it can be harder to implement comprehensive strategies and policies to protect them. 

How are data-driven businesses performing?

Some organizations remain wary of going all in on big data because of the time, effort, and commitment it requires to leverage it successfully. In particular, businesses struggle to rework established processes and facilitate the cultural change needed to put data at the heart of every decision.  

But becoming a data-driven business is worth the work. Recent research shows: 

• 58% of companies that make data-based decisions are more likely to beat revenue targets than those that don't
• Organizations with advanced insights-driven business capabilities are 2.8x more likely to report double-digit year-over-year growth
•  Data-driven organizations generate, on average, more than 30% growth per year

The enterprises that take steps now and make significant progress toward implementing big data stand to come as winners in the future. 

Big data strategies and solutions

Developing a solid data strategy starts with understanding what you want to achieve, identifying specific use cases, and the data you currently have available to use. You will also need to evaluate what additional data might be needed to meet your business goals and the new systems or tools you will need to support those. 

Unlike traditional data management solutions, big data technologies and tools are made to help you deal with large and complex datasets to extract value from them. Tools for big data can help with the volume of the data collected, the speed at which that data becomes available to an organization for analysis, and the complexity or varieties of that data. 

For example, data lakes ingest, process, and store structured, unstructured, and semi-structured data at any scale in its native format. Data lakes act as a foundation to run different types of smart analytics, including visualizations, real-time analytics, and machine learning . 

It’s important to keep in mind that when it comes to big data—there is no one-size-fits-all strategy. What works for one company may not be the right approach for your organization’s specific needs. 

Here are four key concepts that our Google Cloud customers have taught us about shaping a winning approach to big data: 

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Big Data Platforms and Applications

Case Studies, Methods, Techniques, and Performance Evaluation

• © 2021
• Florin Pop 0 ,
• Gabriel Neagu 1

University Politehnica of Bucharest, Bucharest, Romania

You can also search for this editor in PubMed   Google Scholar

National Institute for Research and Development in Informatics, Bucharest, Romania

• Presents a comprehensive review of the latest developments in big data platforms
• Proposes state-of-the-art technological solutions for important issues in big data processing, resource and data management, fault tolerance, and monitoring and controlling
• Covers basic theory, new methodologies, innovation trends, experimental results, and implementations of real-world applications

Part of the book series: Computer Communications and Networks (CCN)

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Table of contents (13 chapters)

Front matter, data center for smart cities: energy and sustainability issue.

• Anastasiia Grishina, Marta Chinnici, Ah-Lian Kor, Eric Rondeau, Jean-Philippe Georges, Davide De Chiara

Apache Spark for Digitalization, Analysis and Optimization of Discrete Manufacturing Processes

• Dorin Moldovan, Ionut Anghel, Tudor Cioara, Ioan Salomie

An Empirical Study on Teleworking Among Slovakia’s Office-Based Academics

• Michal Beno

Data and Systems Heterogeneity: Analysis on Data, Processing, Workload, and Infrastructure

• Roxana-Gabriela Stan, Catalin Negru, Lidia Bajenaru, Florin Pop

exhiSTORY: Smart Self-organizing Exhibits

• Costas Vassilakis, Vassilis Poulopoulos, Angeliki Antoniou, Manolis Wallace, George Lepouras, Martin Lopez Nores

IoT Cloud Security Design Patterns

• Bogdan-Cosmin Chifor, Ștefan-Ciprian Arseni, Ion Bica

Cloud-Based mHealth Streaming IoT Processing

• Marjan Gusev

A System for Monitoring Water Quality Parameters in Rivers. Challenges and Solutions

• Anca Hangan, Lucia Văcariu, Octavian Creţ, Horia Hedeşiu, Ciprian Bacoţiu

A Survey on Privacy Enhancements for Massively Scalable Storage Systems in Public Cloud Environments

• Gabriel-Cosmin Apostol, Luminita Borcea, Ciprian Dobre, Constandinos X. Mavromoustakis, George Mastorakis

Energy Efficiency of Arduino Sensors Platform Based on Mobile-Cloud: A Bicycle Lights Use-Case

• Alin Zamfiroiu

Cloud-Enabled Modeling of Sensor Networks in Educational Settings

• Florin Daniel Anton, Anca Daniela Ionita

Methods and Techniques for Automatic Identification System Data Reduction

• Claudia Ifrim, Manolis Wallace, Vassilis Poulopoulos, Andriana Mourti

Machine-to-Machine Model for Water Resource Sharing in Smart Cities

• Banica Bianca, Catalin Negru

Back Matter

• Big Data Platforms
• Big Data Applications
• High Performance Modelling and Simulation
• Data Processing
• Performance Analysis
• Formal Methods
• Cloud Computing
• Hadoop and Spark Ecosystems

About this book

This book provides a review of advanced topics relating to the theory, research, analysis and implementation in the context of big data platforms and their applications, with a focus on methods, techniques, and performance evaluation.

The explosive growth in the volume, speed, and variety of data being produced every day requires a continuous increase in the processing speeds of servers and of entire network infrastructures, as well as new resource management models. This poses significant challenges (and provides striking development opportunities) for data intensive and high-performance computing, i.e., how to efficiently turn extremely large datasets into valuable information and meaningful knowledge.

The task of context data management is further complicated by the variety of sources such data derives from, resulting in different data formats, with varying storage, transformation, delivery, and archiving requirements. At the same time rapid responses are neededfor real-time applications. With the emergence of cloud infrastructures, achieving highly scalable data management in such contexts is a critical problem, as the overall application performance is highly dependent on the properties of the data management service.

Editors and Affiliations

Gabriel Neagu

About the editors

Dr. Florin Pop is a Professor at the Department of Computer Science and Engineering at the University Politehnica of Bucharest, Romania and a Senior Researcher (1st Degree) at the Department of Intelligent and Distributed Data Intensive Systems at the National Institute for Research and Development in Informatics, Bucharest, Romania.

Dr. Gabriel Neagu is a Senior Researcher (1st Degree) at the Department of Intelligent and Distributed Data Intensive Systems at the National Institute for Research and Development in Informatics, Bucharest, Romania.

Bibliographic Information

Book Title : Big Data Platforms and Applications

Book Subtitle : Case Studies, Methods, Techniques, and Performance Evaluation

Editors : Florin Pop, Gabriel Neagu

Series Title : Computer Communications and Networks

DOI : https://doi.org/10.1007/978-3-030-38836-2

Publisher : Springer Cham

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : Springer Nature Switzerland AG 2021

Hardcover ISBN : 978-3-030-38835-5 Published: 29 September 2021

Softcover ISBN : 978-3-030-38838-6 Published: 30 September 2022

eBook ISBN : 978-3-030-38836-2 Published: 28 September 2021

Series ISSN : 1617-7975

Series E-ISSN : 2197-8433

Edition Number : 1

Number of Pages : XVII, 290

Number of Illustrations : 37 b/w illustrations, 60 illustrations in colour

Topics : Computer Communication Networks , Big Data , Data Storage Representation , Data Mining and Knowledge Discovery , IT in Business

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Big data analytics is the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.

What is big data exactly? It can be defined as data sets whose size or type is beyond the ability of traditional relational databases  to capture, manage and process the data with low latency. Characteristics of big data include high volume, high velocity and high variety. Sources of data are becoming more complex than those for traditional data because they are being driven by artificial intelligence (AI) , mobile devices, social media and the Internet of Things (IoT). For example, the different types of data originate from sensors, devices, video/audio, networks, log files, transactional applications, web and social media — much of it generated in real time and at a very large scale.

With big data analytics, you can ultimately fuel better and faster decision-making, modelling and predicting of future outcomes and enhanced business intelligence. As you build your big data solution, consider open source software such as Apache Hadoop , Apache Spark  and the entire Hadoop ecosystem as cost-effective, flexible data processing and storage tools designed to handle the volume of data being generated today.

Businesses can access a large volume of data and analyze a large variety sources of data to gain new insights and take action.  Get started small and scale to handle data from historical records and in real-time.

Flexible data processing and storage tools can help organizations save costs in storing and analyzing large anmounts of data.  Discover patterns and insights that help you identify do business more efficiently. 

Analyzing data from sensors, devices, video, logs, transactional applications, web and social media empowers an organization to be data-driven.  Gauge customer needs and potential risks and create new products and services.

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Learn how they are driving advanced analytics with an enterprise-grade, secure, governed, open source-based data lake.

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

Table of Contents

What is big data, the five ‘v’s of big data, what does facebook do with its big data, big data case study, challenges of big data, challenges of big data visualisation, security management challenges, cloud security governance challenges, challenges of big data: basic concepts, case study, and more.

Evolving constantly, the data management and architecture field is in an unprecedented state of sophistication. Globally, more than 2.5 quintillion bytes of data are created every day, and 90 percent of all the data in the world got generated in the last couple of years ( Forbes ). Data is the fuel for machine learning and meaningful insights across industries, so organizations are getting serious about how they collect, curate, and manage information.

This article will help you learn more about the vast world of Big Data, and the challenges of Big Data . And in case you thing challenges of Big Data and Big data as a concept is not a big deal, here are some facts that will help you reconsider: 

• About 300 billion emails get exchanged every day (Campaign Monitor)
• 400 hours of video are uploaded to YouTube every minute (Brandwatch)
• Worldwide retail eCommerce accounts for more than $4 billion in revenue (Shopify)
• Google receives more than 63,000 search inquiries every minute (SEO Tribunal)
• By 2025, real-time data will account for more than a quarter of all data (IDC)

To get a handle on challenges of big data, you need to know what the word "Big Data" means. When we hear "Big Data," we might wonder how it differs from the more common "data." The term "data" refers to any unprocessed character or symbol that can be recorded on media or transmitted via electronic signals by a computer. Raw data, however, is useless until it is processed somehow.

Before we jump into the challenges of Big Data, let’s start with the five ‘V’s of Big Data.

Big Data is simply a catchall term used to describe data too large and complex to store in traditional databases. The “five ‘V’s” of Big Data are:

• Volume – The amount of data generated
• Velocity - The speed at which data is generated, collected and analyzed
• Variety - The different types of structured, semi-structured and unstructured data
• Value - The ability to turn data into useful insights
• Veracity - Trustworthiness in terms of quality and accuracy 

Facebook collects vast volumes of user data (in the range of petabytes, or 1 million gigabytes) in the form of comments, likes, interests, friends, and demographics. Facebook uses this information in a variety of ways:

• To create personalized and relevant news feeds and sponsored ads
• For photo tag suggestions
• Flashbacks of photos and posts with the most engagement
• Safety check-ins during crises or disasters

Next up, let us look at a Big Data case study, understand it’s nuances and then look at some of the challenges of Big Data.

As the number of Internet users grew throughout the last decade, Google was challenged with how to store so much user data on its traditional servers. With thousands of search queries raised every second, the retrieval process was consuming hundreds of megabytes and billions of CPU cycles. Google needed an extensive, distributed, highly fault-tolerant file system to store and process the queries. In response, Google developed the Google File System (GFS).

GFS architecture consists of one master and multiple chunk servers or slave machines. The master machine contains metadata, and the chunk servers/slave machines store data in a distributed fashion. Whenever a client on an API wants to read the data, the client contacts the master, which then responds with the metadata information. The client uses this metadata information to send a read/write request to the slave machines to generate a response.

The files are divided into fixed-size chunks and distributed across the chunk servers or slave machines. Features of the chunk servers include:

• Each piece has 64 MB of data (128 MB from Hadoop version 2 onwards)
• By default, each piece is replicated on multiple chunk servers three times
• If any chunk server crashes, the data file is present in other chunk servers

Next up let us take a look at the challenges of Big Data, and the probable outcomes too! 

With vast amounts of data generated daily, the greatest challenge is storage (especially when the data is in different formats) within legacy systems. Unstructured data cannot be stored in traditional databases.

Processing big data refers to the reading, transforming, extraction, and formatting of useful information from raw information. The input and output of information in unified formats continue to present difficulties.

Security is a big concern for organizations. Non-encrypted information is at risk of theft or damage by cyber-criminals. Therefore, data security professionals must balance access to data against maintaining strict security protocols.

Finding and Fixing Data Quality Issues

Many of you are probably dealing with challenges related to poor data quality, but solutions are available. The following are four approaches to fixing data problems:

• Correct information in the original database.
• Repairing the original data source is necessary to resolve any data inaccuracies.
• You must use highly accurate methods of determining who someone is.

Scaling Big Data Systems

Database sharding, memory caching, moving to the cloud and separating read-only and write-active databases are all effective scaling methods. While each one of those approaches is fantastic on its own, combining them will lead you to the next level.

Evaluating and Selecting Big Data Technologies

Companies are spending millions on new big data technologies, and the market for such tools is expanding rapidly. In recent years, however, the IT industry has caught on to big data and analytics potential. The trending technologies include the following:

Hadoop Ecosystem

• Apache Spark
• NoSQL Databases
• Predictive Analytics
• Prescriptive Analytics

Big Data Environments

In an extensive data set, data is constantly being ingested from various sources, making it more dynamic than a data warehouse. The people in charge of the big data environment will fast forget where and what each data collection came from.

Real-Time Insights

The term "real-time analytics" describes the practice of performing analyses on data as a system is collecting it. Decisions may be made more efficiently and with more accurate information thanks to real-time analytics tools, which use logic and mathematics to deliver insights on this data quickly.

Data Validation

Before using data in a business process, its integrity, accuracy, and structure must be validated. The output of a data validation procedure can be used for further analysis, BI, or even to train a machine learning model.

Healthcare Challenges

Electronic health records (EHRs), genomic sequencing, medical research, wearables, and medical imaging are just a few examples of the many sources of health-related big data.

Barriers to Effective Use Of Big Data in Healthcare

• The price of implementation
• Compiling and polishing data
• Disconnect in communication

Other issues with massive data visualisation include:

• Distracting visuals; the majority of the elements are too close together. They are inseparable on the screen and cannot be separated by the user.
•  Reducing the publicly available data can be helpful; however, it also results in data loss.
• Rapidly shifting visuals make it impossible for viewers to keep up with the action on screen.

The term "big data security" is used to describe the use of all available safeguards about data and analytics procedures. Both online and physical threats, including data theft, denial-of-service assaults, ransomware, and other malicious activities, can bring down an extensive data system.

It consists of a collection of regulations that must be followed. Specific guidelines or rules are applied to the utilisation of IT resources. The model focuses on making remote applications and data as secure as possible.

Some of the challenges are below mentioned:

• Methods for Evaluating and Improving Performance
• Governance/Control
• Managing Expenses

And now that we know the challenges of Big Data, let’s take a look at the solutions too!

Hadoop as a Solution

Hadoop , an open-source framework for storing data and running applications on clusters of commodity hardware, is comprised of two main components:

Hadoop HDFS

Hadoop Distributed File System (HDFS) is the storage unit of Hadoop. It is a fault-tolerant, reliable, scalable layer of the Hadoop cluster. Designed for use on commodity machines with low-cost hardware, Hadoop allows access to data across multiple Hadoop clusters on various servers. HDFS has a default block size of 128 MB from Hadoop version 2 onwards, which can be increased based on requirements.

Hadoop MapReduce

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Hadoop features Big Data security, providing end-to-end encryption to protect data while at rest within the Hadoop cluster and when moving across networks. Each processing layer has multiple processes running on different machines within a cluster. The components of the Hadoop ecosystem , while evolving every day, include:

• Sqoop For ingestion of structured data from a Relational Database Management System (RDBMS) into the HDFS (and export back).
• Flume For ingestion of streaming or unstructured data directly into the HDFS or a data warehouse system (such as Hive
• Hive A data warehouse system on top of HDFS in which users can write SQL queries to process data
• HCatalog Enables the user to store data in any format and structure
• Oozie A workflow manager used to schedule jobs on the Hadoop cluster
• Apache Zookeeper A centralized service of the Hadoop ecosystem, responsible for coordinating large clusters of machines
• Pig A language allowing concise scripting to analyze and query datasets stored in HDFS
• Apache Drill Supports data-intensive distributed applications for interactive analysis of large-scale datasets
• Mahout For machine learning

MapReduce Algorithm

Hadoop MapReduce is among the oldest and most mature processing frameworks. Google introduced the MapReduce programming model in 2004 to store and process data on multiple servers, and analyze in real-time. Developers use MapReduce to manage data in two phases:

• Map Phase In which data gets sorted by applying a function or computation on every element. It sorts and shuffles data and decides how much data to process at a time.
• Reduce Phase Segregating data into logical clusters, removing bad data, and retaining necessary information.

Now that you have understood the five ‘V’s of Big Data, Big Data case study, challenges of Big Data, and some of the solutions too, it’s time you scale up your knowledge and become industry ready. Most organizations are making use of big data to draw insights and support strategic business decisions. Simplilearn's Caltech Post Graduate Program in Data Science will help you get ahead in your career!

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