Big Data Use Cases
Big Data Use Cases
The most frequent industries are financial services, retail, and healthcare while the most frequent applications are security and 360-degree view of the customer.
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To understand the current and future state of big data, we spoke to 31 IT executives from 28 organizations. We asked them, "What are a couple of big data use cases you’d like to highlight? What is the business problem being solved?" Here's what they told us:
- 1) Fraud is big, anomaly detection in healthcare, financial services. 2) Customer 360, the more you know about a customer the more trends you can identify. Reduce waste of fraud in insurance payer industries. Patterns defined through the graph and look for new patterns. 3) Improving the cost of care of service similarities in how treatment is being done or person looking for new insurance – better recommendations. See use cases explode. Last five years collected data, now a knowledge graph applied to the use cases to find patterns and apply pattern matching.
- We provide managed security covering the threat kill chain end to end. We identify and prevent attacks against your business. Preventing and reacting to events is a responsibility with real business impacts – such as downtime, data loss, and sensitive and protected data theft. Mid-sized business cannot afford to provide effective multi-layer defense security environments. Staffing and operating an internal SOC and Incident Response Team is cost prohibitive, but we reduce cost, simplify security management, improve threat management, decrease vulnerabilities and help achieve regulatory compliance. Additionally, most IT departments spend over 50 percent of their time on repetitive maintenance tasks. When you spend too much time fighting fires, herding cats, and keeping the lights on, it’s time to start thinking differently. When automation tools are applied to big data sets, tasks that took weeks can now be delivered in about an hour.
- 1) GDPR in Europe looks at documents, builds categories, and identifies as GDPR-sensitive data. Look at the universe of GDPR documents and then search for the name of the person that wants to be deleted. 2) Entry-level digital hoarding. Clean up digital hoarding in organizations with catalogs, last access dates, categorize. 3) Security because categorized information tags based on security DLP engines can look at security classification tags to determine how to protect information. AIP rights management tags for rights usage. Use security tags to manage access rights.
- Here are a few use cases that are focused on the real-time analytics aspect of big data: 1) Real-Time Personalization – Personalizing web pages for media based on preference and changing titles based on real-time feedback. 2) Real-Time A/B Testing and Offers– In mobile gaming, testing new features, making the game easier for new users and sharing offers at the right time all need real-time analytics and decision making. 3) Fraud Prevention for Mobile Roaming and Credit Cards – Detecting credit card fraud through Machine Learning-based rules that get enacted in real time on an incoming stream of credit card events. 4) Compliance Management on Trade Data – Regulatory compliance for traditional and bitcoin exchanges to make sure the risk for/against a security trade is within control. 5) Dynamic Policy and Charging and Rules Function (PCRF) for Telcos – All customer phone calls need to be validated against a dynamic set of policies for proper operations and billing. 6) Telecom Application Servers (TAS) – Customer applications that allow users to effectively manage their phone and data plans by restricting usage or enabling features based on real-time analytics (and not month end billing analytics).
- 1) In the financial services verticals, new regulations have imposed real-time reporting requirements on many companies. For example, the Fundamental Review of the Trading Book (FRTB) regulations require financial services companies to calculate their portfolio value and risk exposure in real-time. To enable this, many companies are moving to in-memory computing which can provide hybrid transactional/analytical processing (HTAP) capabilities which means that companies can both record transactions in the in-memory layer as well as run the required regulatory calculations in real-time on their operational dataset. 2) In the online services space, many online travel websites respond to travel inquiries by site visitors by pulling price and availability data from multiple online sources such as hotel and airline sites. Margins are then calculated, results are sorted, and then the available options are displayed for the site visitor. Many online travel companies use in-memory computing solutions to meet their speed and scalability requirements and produce results quickly enough to keep visitors satisfied with their website’s responsiveness. 3) In the transportation and logistics space, major airlines have to respond to each change in flight status by recalculating the impact on variables such as airplane and crew availability, passenger connections, luggage handling, and gate availability in real-time. A single delayed flight may impact flights and passengers throughout the plane's network. Many major airlines and logistics companies use in-memory computing solutions to maintain the relevant data in RAM and recalculate the impact of each change on the entire chain in real-time and drive real-time responses to minimize the impact of the change.
- The most common Big Data analytical use cases that we see are 1) Communication and network analytics to optimize network performance and capacity planning so that businesses can lower infrastructure costs and prevent disruption of service. 2) Customer behavior analytics to better understand and engage with customers so that businesses can predict and prevent churn and gain a 360-degree of the business to uplift sales and increase Net Promoter Scores. 3) Fraud monitoring and risk management to identify, detect, and prevent fraudulent behavior and understand and manage risk for regulatory compliance. 4) IoT analytics to tap the business potential of a range of use cases from predictive maintenance to vehicle telematics to smart buildings to smart metering and more, so that companies can reduce service costs, differentiate based on connected products and generate entirely new revenue sources based on the value of the data.
- One of the hottest areas for fast distributed transactional databases right now is in the area of distributed ledgers and the related subcategory of the blockchain. How does that relate to big data? Again, big data isn’t confined to analytics — companies have a lot of customer data they are using for interactions with those customers and every time you call them or interact with them online, data is changing, i.e. it is being updated. Or at least it should be. How many times have you called your bank or internet service provider and realized they didn’t have any information on the last complaint you logged online or transaction you had at their local office? Those are recorded somewhere, but they aren’t made consistent across all their different data repositories for immediate benefit and action. There is a strong need for modern enterprises to move away from centralized ledgers (systems of record) toward using distributed ledgers across the world being driven by the economic advantages of distributed computing across low-cost commodity servers, whether provided by public cloud platforms or through a private cloud infrastructure.
Here’s who we spoke to:
- Cheryl Martin, V.P. Research Chief Data Scientist, Alegion
- Adam Smith, COO, Automated Insights
- Amy O’Connor, Chief Data and Information Officer, Cloudera
- Colin Britton, Chief Strategy Officer, Devo
- OJ Ngo, CTO and Co-founder, DH2i
- Alan Weintraub, Office of the CTO, DocAuthority
- Kelly Stirman, CMO and V.P. of Strategy, Dremio
- Dennis Duckworth, Director of Product Marketing, Fauna
- Nikita Ivanov, founder and CTO, GridGain Systems
- Tom Zawacki, Chief Digital Officer, Infogroup
- Ramesh Menon, Vice President, Product, Infoworks
- Ben Slater, Chief Product Officer, Instaclustr
- Jeff Fried, Director of Product Management, InterSystems
- Bob Hollander, Senior Vice President, Services & Business Development, InterVision
- Ilya Pupko, Chief Architect, Jitterbit
- Rosaria Silipo, Principal Data Scientist and Tobias Koetter, Big Data Manager and Head of Berlin Office, KNIME
- Bill Peterson, V.P. Industry Solutions, MapR
- Jeff Healey, Vertica Product Marketing, Micro Focus
- Derek Smith, CTO and Co-founder and Katie Horvath, CEO, Naveego
- Michael LaFleur, Global Head of Solution Architecture, Provenir
- Stephen Blum, CTO, PubNub
- Scott Parker, Director of Product Marketing, Sinequa
- Clarke Patterson, Head of Product Marketing, StreamSets
- Bob Eve, Senior Director, TIBCO
- Yu Xu, Founder and CEO, and Todd Blaschka, CTO, TigerGraph
- Bala Venkatrao, V.P. of Product, Unravel
- Madhup Mishra, VP of Product Marketing, VoltDB
- Alex Gorelik, Founder and CTO, Waterline Data
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