Big Data Solves Real-World Problems

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Big Data Solves Real-World Problems

Working smartly with Big Data results in improved customer and patient experiences across a variety of different industries.

· Big Data Zone ·
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To gather insights on the state of Big Data today, we spoke with 22 executives from 20 companies who are working in Big Data themselves or providing Big Data solutions to clients.

Here's what they told us when we asked, "What are real world problems you, or your clients, are solving with data?" 

  • Healthcare – payment, care, fraud, and abuse. Pattern recognition and anomaly detection can identify fraud, waste, and abuse for healthcare insurance companies. Credit card fraud detection in milliseconds helps financial services firms protect their customers’ security while reducing loss due to fraud.
  • Telco’s with embedded software sold to carriers via HPE. OSS BSS stack for authorization of service driving to virtualization (NSV stacks). Not just calls but sets of services. Higher degree of granularity on personalization to improve click-throughs for the AdTech space. Rich context relevance with social media. Real-time dashboards for investment firms to provide advice from brokers. Regulation compliance ensuring you can meet the regulations in the time required. We help companies operate more quickly to meet SEC regulations. IoT data integrates with real-time data.
  • In pharmaceutical, drug names are unique. We’re able to interrogate the Twitter feed to determine public sentiment data to determine is we need more physician training, different advertising. In omnichannel retail we make it easier to shop and for the customer to receive targeted ads.
  • We took the Scopus database for Elsevier with 60 million research papers that weren’t tagged. Tagged, added citations and put in a searchable database. The H-Index of some authors changed by as many as five points because of proper citation of their work. Tagged 750,000 pages of data for the Optical Society of America which they are now monetizing. Help the U.S. Patent Office manage five million patent applications every year by entering two million pages of data every month with OCR scanning and tagging. Make documentation flexible so users can find what they’re looking for with different queries.
  • Our Big Data SaaS solution allows our clients to gather network traffic and performance telemetry in live streaming fashion, and then display the results in dashboards and consoles while also watching for trigger conditions to raise alerts and alarms. This is used to recognize abnormal patterns of network activity or behavior, such as service degradations, bandwidth events, and security incidents such as DDoS attacks. We also provide a fully functional data exploration console that enables endlessly flexible data forensics, which allow for fast and definitive troubleshooting and root cause identification. Finally, our advanced analytics for network routing and peering lets our customers understand how their traffic is behaving as it transits neighboring networks, and plan network changes to optimize cost and service quality.
  • We have a client using endpoint data connected to smart light bulbs to change with KPIs directly changing human psychology. One has green lights when their NPS is great than 50, yellow when 45 to 49, and red when less than 45. NPS consistently above 50 because everyone wants to avoid red lights. We have an enterprise environment customer who embedded a new system that users were not adopting. They had KPIs for engagement connected to lights and adoption went from 16 to 85% because users wanted to avoid the red lights. Take Big Data and convert it in a way that’s meaningful for humans.
  • Focused on personalization to improve the customer experience (CX). In retail, physical stores integrate with online stores and the CX is personalized based on where they are and what device they are using. The ability to provide more up to date information on usage of the product so the manufacturer knows how effective their solution is for the customer and how improvements can be made to provide a greater benefit to the customer.
  • Self-service analytics for data-driven decisions. Governance – controlled access to the appropriate data. Method to catalog and invest time into protecting the data. Having a tag-based policy to manage access. Meta-data becomes critical so you can automatically find what you’re looking for. Data rationalization to identify and eliminate duplicate data.
  • Fleet management is ingesting breadcrumbs from 200,000 trucks and people and integrating with traffic and weather data to project vehicle and package delivery time. Retailers are tracking when the truck is due so they can have the necessary people to unload the truck. One client managing 2.8 million deliveries per year. CPG companies are getting a view of the customer by building views of the household.
  • A real-world problem is eliminating shadow IT infrastructure/clusters — data risk and governance. Bring IT infrastructure into the mainstream. Take advantage of Lambda to create differentiated services.
  • Stanley tool’s healthcare division is marketing equipment location and tracking in hospitals with RFID chips and sensors. Since patient experience in the ER is such an important CX issue, we’re using RFID on patient badges and caregivers to track time of interaction. This provides the ability to see what’s going on with the patient, tie back to the medical record and see where the problems are without a lot of data points. Able to get a lot of detail in a short period of time. Create workflow that you need to visit. Identify what rooms are available and when a patient left. We’ve taken sensor data, detailed for the patient in the hospital using real-time data to solve business problems.
  • We help with loyalty card shopping baskets and optimizing insurance premium pricing by adding external data (telematics and smart car data). We help clinical research organizations by adding non-clinical data to add insights to a project.
  • A large bank was struggling with how to identify whether small businesses exist. They needed to validate with legitimate business information. They employed hundreds of people to chase down whether a business is legitimate or not. We rewrote how we matched businesses together and created a score workflow platform to attach to the name, search on Google, maps, and website. For every 100 companies, we could automatically identify 35 of them put them in a file to show to auditors. Twelve weeks to prototype, six months to production.
  • Help people understand customers better by analyzing buyer behavior and web metrics. Large adoption by ad agencies and social media platforms. Increase product and service efficiency with predictive maintenance and IoT. Reduce risk for companies to understand cybersecurity threat vectors. Help security vendors understand where their clients’ greatest liabilities lie. Healthcare – able to track tremors in Parkinson’s patients to see if medication is affecting. Instruments on people synch to smart phones to track the tremors. Work with hospitals to predict incidents of sepsis using predictive analytics.
  • Omnichannel customer analytics with clickstream, mobile, application, and brick and mortar data enhanced to blend into a single data set. Operational analytics around IoT. Fraud reduction and compliance. Data driven products and services like CRM and social media.
  • Voice-based solutions — customer service help desks, text, and chat. How to create a smarter interactive solution. IT operations innovation run a Cloud Automation Center use data to analyze and predict the health of the system. Monitor levels and reduce noise so Ops can focus on actual problems. Logistics and fleet management — shorten routes, integrate with third party systems.
  • We had a client for whom it was taking nearly two minutes to search for customer email addresses and reduced that to 14 thousandths of a second enabling CSRs to be more efficient and help many more customers per day. Swimwear client with multiple websites competing with other sites assumed their target audience were the same as their competitors. We showed them the audiences they were targeting we vastly different that the people buying the product. This enable the client to spend their money more efficiently and target buyers.
  • Instantly predict market trends and customer needs. Predict how market price volatility will impact your production plans. See changes in demand or supply across your entire supply chain immediately. Monitor and analyze all deviations and quality issues in your production process. Provide exactly the right offers and service levels to every customer. Have a continuously updated window into future sales, showing changes in real time. Understand what your customers and potential customers are saying about you – right now. Predict cash flows to manage collections, risk, and short-term borrowing in real time
  • Monitoring of performance through events captured from IoT, for example. Text processing of data, so that unstructured data can be analyzed structurally. Monitoring of business anomalies, such customer churn, seasonality, change in buying behavior.
  • Many; including subscription churn prediction, product and content optimization, greater insight into customer behavior and preferences, product pricing optimization, and so much more.

What real-world problems are you using Big Data to solve?

By the way, here’s who we talked to!

  • Nitin Tyagi, Vice President Enterprise Solutions, Cambridge Technology Enterprises.
  • Ryan Lippert, Senior Marketing Manager and Sean Anderson, Senior Product Marketing Manager, Cloudera.
  • Sanjay Jagad, Senior Manager, Product Marketing, Coho Data.
  • Amy Williams, COO, Data Conversion Laboratory (DCL).
  • Andrew Brust, Senior Director Market Strategy and Intelligence, Datameer.
  • Eric Haller, Executive Vice President, Experian DataLabs.
  • Julie Lockner, Global Product Marketing, Data Platforms, Intersystems.
  • Jim Frey, V.P. Strategic Alliances, Kentik.
  • Eric Mizell, Vice President Global Engineering, Kinetica.
  • Rob Consoli, Chief Revenue Officer, Liaison.
  • Dale Kim, Senior Director of Industrial Solutions, MapR.
  • Chris Cheney, CTO, MPP Global.
  • Amit Satoor, Senior Director, Product and Solution Marketing, SAP.
  • Guy Levy-Yurista, Head of Product, Sisense.
  • Jon Bock, Vice President of Product and Marketing, Snowflake Computing.
  • Bob Brodie, CTO, SUMOHeavy.
  • Kim Hanmark, Director of Professional Services EMEA, TARGIT.
  • Dennis Duckworth, Director of Product Marketing, VoltDB.
  • Alex Gorelik, Founder and CEO and Todd Goldman, CMO, Waterline Data.
  • Oliver Robinson, Director and Co-Founder, World Programming.
big data, customer experience, healthcare

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