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Big Data Analytics & Data Science Use Cases

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Big Data Analytics & Data Science Use Cases


Or How Businesses Outperform Industries Using Big Data In Practice


NEWS 25/03/2017


Big Data, Data Analytics, Data Science – all these terms make a noise in the world! Here are a few examples to clarify the difference and avoid messing them up:

  • Google has a number of successful products, Data Science Algorithms embedded within. For instance, all the digital marketing tools, like AdWords, AdSense, DoubleClick and Google Analytics, – use data science algorithms analyzing Internet searches to assist its users with their advertising activities, SEO strategies, analytical researches and understanding their visitors behavior patterns.

  • LinkedIn is the professional network injecting Big Data Analytics into various features to deliver their users really smooth experience. For example, such features as ‘People You May Know’, ‘Skills Endorsement’ and ‘Jobs You May Be Interested In’ are based on analyzing a huge amount of initial user data both structured and unstructured: skills, previous jobs, In-Mails, groups etc.

  • And about Big Data itself: literally this buzzword means huge volumes of information (structured and unstructured) demanding cost-effective and innovative processing in order to drive insights and improve decisions making.









Guess what, Big Data, it’s Analytics and Data Science have made their way into our daily activities some time ago and here’s how…

















Big Data Analytics for Healthcare





Hopefully, Big Data Analytics for healthcare is changing the sector to improve the quality of life. And here is how:


Apple and IBM Big Data Analytics use case


Apple and IBM partnership on big data analytics health platform was announced in 2015. Watson Health Cloud, their global information platform, intends to provide physicians, researchers, insurers and companies focused on wellness solutions with a complete picture of individualized health-related insights. They mention connected medical devices, personal fitness trackers, implants and other sensors that collect biometric data, to be generators of real-time health-related information. These pools of information are being connected with traditional sources: doctor-created medical records, clinical researches and individual genotypes. As the result of this collaboration, the platform’s users are equipped to make evidence-based decisions about health-related issues.




    Here’s a couple of health challenges IBM Watson Health solves:

  • Watson for Genomics: using big data to personalize the treatment options.

    This analytical tool helps physicians deliver patient-centric care by analysing the patient’s genomic data and providing potential ways of evidence-based therapy to consider.


  • IBM Watson for Drug Discovery: enabling big data to help life sciences researchers.

    This cloud-based solution empowers the scientists with visualized insights helping to discover novel connections between diseases and identify new drug targets.
















Pittsburgh Health Data Alliance Big Data Analytics use case



Another partnerships between big data engineers and medical professionals is carried out by Pittsburgh Health Data Alliance. Aiming to foretell potential future problems, the organization came up with the technical solution that incorporates big data analytics algorithms. To identify individual health-related problems before they happen, Pittsburgh Health Data Alliance takes the data from various sources: medical and insurance records, social media feedback, wearable sensors, and genome data. This allows to draw a comprehensive picture of any patient as an individual; which in turn suggests the most congruent healthcare package.




To cut it in short, the biggest Alliance challenge is “raising the quality and reducing the cost of healthcare” using big data and they are going to make it the following ways:




Focus on “big health care data analytics; personalized medicine and disease modelling; issues of privacy, security and compliance in the context of big data; data-driven patient and provider education and training; and a new general framework for big data health care”.




Research and invent new technologies and methods, “based on intelligently engineered big data solutions”, to create actionable.



















  • Big Data Analytics fights Ebola

    The spread of epidemics could be stopped by big data analytics as well! For instance, cell-phone location data has been already widely used to monitor population movements in Africa, where the risk of Ebola virus spread is alarmingly high.

    Another way to enable big data analytics to fight Ebola was creating the algorithm identifying the patterns of features making a bat Ebola virus carrier (scientists believe fruit bats are normal carriers of this virus, so predicting their new species capable of Ebola distribution is the key for preventing future disease hotspots).

    Relevant predictions generate insights on the regions that are in the most urgent need for relief, to open treatment centers there or implement population movements restrictions. Similar to epidemics prevention, earthquakes’ risks are decreased due to mobile phones localization that assist in this disaster relief planning actions.


  • Express Scripts’ Big Data & Analytics in practice

    When Express Scripts, a US drug prescription plans provider, analysed common pharmaceutical claims, the company understood that many patients who are given drug prescriptions have a tendency to forget about taking the medicines. Therefore, Express Scripts created their product – beeping medicine bottles, empowered by their automated phone calls reminder service, — beeping medicine bottles, empowered by their automated phone calls reminder service, — which processes drug prescriptions and reminds the patients to take the pills. The product’s success disrupted the industry.


  • 23andMe genomics data

    Named for the 23 pairs of chromosomes, the biotechnology company delivers genetic testing that could be also interpreted to individual consumers providing insights on their genetic history. In 2008, 23andMe offered the cutting-edge service of possible predisposition estimation for more than 90 traits and conditions (from baldness to blindness, as they say). This is big data analytics in practice, which is also ‘won’ Time magazine Invention of the Year award for the company.

    These days 23andMe develop research communities such as Parkinson’s disease and inflammatory bowel disease in order to understand why some people are more likely to get a disease and why people react differently on treatment and drugs.

















Big Data & Analytics for Finances



For financial institutions, big data solutions are tightly intertwined with reducing risks and meeting regulatory objectives. Also it’s quite common to use financial big data analytics to learn consumer behavior and enhance business performance.




Big Data & Analytics use cases for small-tier financial firms


Brokerage firms, asset management companies, regional banks, financial advisors, – they all are rapidly evolving towards big data challenges. Cloud and on-premise data platforms equip these financial organizations to provide better customer intelligence services. These include credit risks evaluation, individual safety measures (e.g. device usage authorization), client-oriented marketing of their financial services (remember your online banking feed messages), etc.






Experts say, big data analytics even allows small-tier financial players to leapfrog their competitors from relevant large-tier sector (international corporate banks) as the latest are rarely focused on rapid-growth strategies.





Commonwealth Bank of Australia Big Data & Analytics use case


Big data analysis has become one of the main strategic focuses for CBA a few years ago. CBA processes over 40% of card-based transactions in Australia. This way they offer the real-world information on consumer purchasing behaviors. CBA suggests its clienteles using their Bespoke Analytics tool in order to identify their business growth opportunities considering customer loyalty and footprint, spending frequency and socio-economic data, along with competitors and industry benchmark results.











Big Data & Analytics Use Cases for Retailers and Commerce


Doug Laney, Gartner analyst, presented 55 examples of big data case studies in 55 minutes. He joked, it’s like the Complete Works of Shakespeare, though ‘less entertaining and hopefully more informative’. According to the resource, big data & analytics play important role in commerce evolution.



    Here are 5 prominent big data case studies for retailers:
  • Macy’s Inc. adjusts real-time pricing for 73 million items. Their technology is based on available inventory and current demand.

  • Wal-Mart Inc. applies its search engine for the online platform. The technology includes semantic search, relies on text analysis, ML and incorporates synonym mining to produce congruent search results. They report billions of dollars revenue increase.

  • Tesco supermarket chain collects data from 70 million refrigerator points. The generated analytics reports allow better refrigerators’ maintenance and energy costs savings.

  • Another privately-owned fast food uses its product data to determine the marketing strategies for their digital menu board displays. The longer the lines are, the better the chances are for the quickly-served products to be displayed on cameras.

  • Woolworths grocery store chain uses business intelligence technologies to analyse customer shopping habits. Billions of dollars are spent for data analytics to leverage online sales.















Extra Big Data & Analytics Case Studies OR Miscellaneous


Many industries leverage their business strategies with big data insights:







Big Data Analytics for Security

The Los Angeles and Santa Cruz police departments in collaboration with a team of educators and a private company apply data analytics to predict future crime locations. The techno logy forecasts crimes, pointing down to 500 square feet potential location. As the result, a 33% of burglaries reduction and 21% of violent crimes reduction occurred in LA.






Also in September, 2014 the European scientists presented their algorithm of crime predictions based on demographics and mobile data. This algorithm obtains an accuracy of almost 70%.















Airline Industry Data Analytics

In their successful effort to overcome lost baggage issue, Delta Air Lines developed a solution to localize lost bags. Thanks to analyzing mishandled baggage data and implementing RFID technology Delta saved Air Transport Industry about $3 billion.













Big Data & Analytics for Logistics

UPS, North American shipment and logistics service, monitors 16.3 million packages for 8.8 million customers on a daily basis. Every day, the data analytics allows to satisfy 39.5 million requests from their customers.