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Big Data Use Cases - 2016

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Big Data Use Cases - 2016

The most frequently mentioned use cases involve: 1) real-time analytics; 2) IoT; and, 3) predictive analytics.

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To gather insights for DZone's Big Data Research Guide, scheduled for release in August, 2016, we spoke to 15 executives who have created big data solutions for their clients.

Here's who we talked to:

Uri Maoz, Head of U.S. Sales and Marketing, Anodot | Dave McCrory, CTO, Basho | Carl Tsukahara, CMO, Birst | Bob Vaillancourt, Vice President, CFB Strategies | Mikko Jarva, CTO Intelligent Data, Comptel | Sham Mustafa, Co-Founder and CEO, Correlation One | Andrew Brust, Senior Director Marketing Strategy, Datameer | Tarun Thakur, CEO/Co-Founder, Datos IO | Guy Yehiav, CEO, Profitect | Hjalmar Gislason, Vice President of Data, Qlik | Guy Levy-Yurista, Head of Product, Sisense | Girish Pancha, CEO, StreamSets | Ciaran Dynes, Vice Presidents of Products, Talend | Kim Hanmark, Director, Professional Services, TARGIT | Dennis Duckworth, Director of Product Marketing, VoltDB.

We asked these executives, "What are the real world problems being solved with big data by you or your clients?"

Here's what they told us:

  • 1) An auto finance company in Plano, Texas used to have all of its infrastructure on premises and made the decision to move to the cloud. It was important for them not to let go of data protection and management. You can move to the cloud but you still have to meet the standards and requirements of your industry. Clients use Cassandra and Kafka to manage a 4 to 10 terabyte databases. An SQL database used to be a half a terabyte. Clients are still able to do backup and recovery from a previous state. 2) A financial services industry client with a large ATM network has an internal app called the Consumer Event Hub which checks withdrawals, deposits, statement requests, and performs internal analytics that enables banks and credit unions to upsell and cross-sell customers, in real time, based on machine learning. They need to protect social security numbers, account,  and transaction data for all clients and end users.
  • Energy markets look at energy use at large industrial companies and predict use – the level of use the next day, next week, and next month so companies can use the information to purchase energy more efficiently. This saves companies millions of dollars in energy costs and saves energy as well. They also have the ability to predict other factors that will affect energy consumption like weather and demand.
  • Telcos need to authorize a “send” command on a cell phone in milliseconds while determining if the person calling has sufficient minutes left this month to make the call. Candy Crush needs to dynamically change the game based on who’s playing, what their tendencies are, and how a player is interacting with it. Big data is being used and acted on in real time. Cell phone companies can display ads to you before you put your cell phone away. These have very high redemption rates due to their timeliness and relevance.
  • A financial services company uses our product to make marketing campaigns more targeted and precise based on demographics and preferences. They have the ability to chug through a lot of data very quickly without slowing down.
  • Two-thirds of our projects are built on solving the internal problems of the client company. One-third are external facing analytics to end users. Link internal analytics to end users with networked business intelligence providing value-added services to their customers. A networked architectural model is able to expose data to customers without access back into the company. Real-time access to spend management data provides a value-added benefit by integrating with other internal data.
  • 1) Real-time personalization for web apps using big data – how to personalize and recommend. 2) Services need to install quality of service in data center application logs. 3) Cybersecurity. 4) Generic interest in building IoT apps – ingesting data from apps. Companies are trying to build their own but it’s not their core competency.
  • 1) Predictive maintenance for airlines show meantime to failure. This reduces downtime by knowing what product needs to be repaired or replaced in minutes. GE is using big data to maintain and optimize their turbines. 2) Water companies are using IoT to track water systems for outages, pump, and meter performance. 3) Ecommerce companies are using for dynamic pricing, recommendations, shopping, and reducing shopping cart abandonment. One user is tracking 22 attributes of users and shopper behavior to predict shopping cart abandonment. Wal-Mart is tracking different family members for Thanksgiving – who’s responsible for the turkey versus the wine. 4) Sensors are being used on farms in Africa to determine the amount of fertilizer to use. This has a direct impact on people’s lives. Small devices are mapping entire regions.  A large U.S. agricultural co-op is using weather sensors and GPS on combine harvesters to predict water, pesticide, and fertilizer needs for crops. They are reselling this data to Wall Street firms who are doing predictive analytics to determine the future price of commodities.
  • 1) Retail has a lot of data and it’s growing. Highly influenced by the weather. Traditional systems are too slow for real-time analytics. Collect data in Google Bit Query and use our product to analyze. Our client is able to share dashboards to show what’s happening in real time. 2) Production is using IoT to track energy use and track information regarding production. 3) Trucking is collecting real-time information from buses regarding driving patterns for security and fuel economy. The client sends reports to 5,000 drivers letting them know how they can improve. With in-memory data you can do real-time ingestion of the data.
  • 1) A supply chain management company is sharing data throughout its organization around the world. They started with a small B.I. project with five users, then 50, then 250. Their goal is to reach more than 1000 users globally providing business managers the ability to interact with the data to provide relevant insights. 2) Seminal scale mobile virtual network operator with more than 1,000 stores. Providing store managers with a view of the data that’s relevant to their store – bandwidth, aggregated, employee performance, profitability. 
  • 15 years ago Accenture did a study showing on-shelf availability was 8%. Even with analytics and machine learning, a recent Adobe study shows that on-shelf availability was 11%. Inventory is more important today because of ecommerce. Fashion-oriented companies typically have a 60 to 65% accurate inventory. They’re using our product to help them get to 90%+. Companies don’t know how to report or show the ROI of big data projects so we put that in our reports.
  • Provide an overview and allow users to drill down. We have a large telco client with call detail data on cell tower performance to monitor dropped calls. A gaming company, King.com produces two billion rows of data per day that we provide an overview of the health of the games and enable them to drill down to subsets of games, players, geographies, and more.
  • 1) Business incident detection. Online payment provider is looking at millions of data points and wants to know if there’s a problem with a payment or a vendor (i.e. Visa on an Android device). Ability to to see all transactions in real time. 2) Real-time ad space decisions. Ability to measure, with granularity, the performance of a specific campaign, ad, click throughs and respond in real-time. 3) Ecommerce – able to see a drop in conversions in Thailand due to an issue with a specific version of the Chrome browser in Thailand. 4) IoT – manufacturers are collecting more data for machines so they can do predictive maintenance. We’ve seen a lot of maturity in companies becoming ready for a solution – they have the data, they want to be able to analyze it and take action.
  • High availability while focusing on the correctness of the data. If two attempts are made to write data at the same time, we accept both and then present for conflict resolution. This is attractive to customers like Bet365, NHS, and chat services for the League of Legends. We collect session data, weather data, archive, analyze, and visualize analytics once the desired analysis is complete.
  • We help telcos make things real-time and personalized for their customers using relatively small subsets of data. We use big data sets for predictive analytics and customer recommendations.
  • We worked with the Ted Cruz for President campaign, aggregating data across all fundraising and grassroots efforts, as well as powering the campaign’s bundling and #CruzCrowd initiative. CruzCrowd engaged supporters on a grassroots level, prompting them to sign up online and begin earning themed badges as they recruited new supporters. Campaign managers could then assess the data, learning what issues mattered to voters and how and where to best communicate with them. The campaign saw more than 900,000 supporters make more than 1.5 million contributions, with a total of more than $90 million. Approximately 99 percent of all donors were small-dollar donors. Since its launch in October 2015, CruzCrowd has grown to 4,000 users with more than 500,000 weekly impressions on social media.

What examples do you have where big data is being used to solve real world problems?

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Topics:
big data ,big data analytics solutions ,predictive analytics ,real-time analytics ,iot app development ,real-time ingestion

Opinions expressed by DZone contributors are their own.

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