IBM Big Data Hub Blog

Improving Claims Fraud Detection in Insurance

kim-minor — Tue, 21 May 2013 13:46:32 +0000

Solutions for analyzing big data can play a critical role in addressing the increasing prevalence of claims fraud. Traditionally, fraud is estimated to account for approximately 10 percent of insurance company losses[1], and that percentage is rising. Insurance companies need ways to quickly identify potential fraudulent claims, enhance the efficiency of investigations and prosecutions, and facilitate rapid reporting and visualization to improve ongoing antifraud efforts.

Friday Data Flick: A Closer Look at Enhanced 360-degree View

david-pittman — Fri, 17 May 2013 13:47:51 +0000

In our Friday Data Flick series this week, we look at how companies are achieving an “enhanced 360-degree view of the customer,” which is another of the top five uses for big data (also called “use cases”). In less than 15 minutes, you can watch these two videos and walk away with a thorough understanding of what this use case is, how you can apply it, and you will hear one client describe how he expects this technology will help his company delivery customer services “they can’t even dream of today.”

Sentiment Analysis and the Customer Experience

twitterchat — Thu, 16 May 2013 21:51:41 +0000

In our last #CXO twitterchat guest, Seth Grimes, founder, and a principal, of Alta Planta Corporation, led the conversation as we discussed “Sentiment Analysis and the Customer Experience.” Here are some of the nuggets from that conversation.

Data Scientist: Bias, Backlash and Brutal Self-Criticism

james-kobielus — Thu, 16 May 2013 13:31:25 +0000

Data scientists such as Nate Silver have recently begun to receive rockstar status in the big-data universe. That’s a tricky status to sustain for long, because it inevitably inspires popular backlash. You can already see that backlash gaining force, as evidenced through the growing volume of popular discussions of “data-scientist bias,” such as this article and this one.

Following the Money

mike-kearney — Wed, 15 May 2013 15:46:20 +0000

Although I live in Australia, work brings me to both the USA and Asia where discussing big data projects leaves me with the impression of significant differences in adoption rates and approaches between organizations in both regions. My intuition is supported by a timely and informative report titled The Emerging Big Returns on Big Data[1], recently published by Tata Consulting Services.

Smarter Digital Banking: Leveraging Information and Mobile

bob-palmer — Wed, 15 May 2013 14:20:04 +0000

Part 2 in a 3-part series: Big Data and Next Generation Banking – Leveraging information and mobile to drive revenue and expand the customer value proposition

Regulatory Drivers and Quality Healthcare

deanna-nole — Wed, 15 May 2013 12:41:36 +0000

The transition to a healthcare pay-for-performance model is driven by both regulatory and consumer factors. Let's dig into the key drivers and information needs that will be critical to harness and leverage to manage the challenges posed by this new model.

The key regulatory drivers include changes in Medicare reimbursement based on key Meaningful Use core measure. Accountable quality of care metrics also introduce not only clinical factors, but also patient experience and satisfaction surveys.

The Rise of the Data Scientist: Recap of IBM Twitter chat

james-kobielus — Tue, 14 May 2013 19:21:09 +0000

Big data is not just about scaling your data analytics processing platforms to keep up with the onslaught of new information. Just as important, big data is about bringing together your best and brightest minds—your data scientists—and giving them the tools they need to interactively and collaboratively explore rich information sets.

Tapping the Power of Big Data for the Oil and Gas Industry

michael-brule — Tue, 14 May 2013 17:31:29 +0000

The petroleum industry is no stranger to large volumes of data. Operating in arguably the original sensor-based industry, oil and gas companies have for decades used tens of thousands of data-collecting sensors installed in subsurface wells and surface facilities to provide continuous, real-time monitoring of assets and environmental conditions. These companies closely monitor the performance of their operational assets. They also conduct advanced physics-based modeling and simulation to support operational and business analytics and optimization.

Mastering Data in Healthcare: Beyond Patients

lorraine-fernandes — Mon, 13 May 2013 21:37:00 +0000

Increasingly, recent conversations with customers around the world have included the need to address multiple types of data, commonly called “multi-domain” in master data management (MDM) circles. This stems from several factors: an increasing emphasis on data exchange; the rise of accountable care organizations (ACOs) in the United States; greater understanding that care coordination is one of the pillars of improving quality and reducing costs; the rise of consumerism in healthcare; and the big data and analytic movements.

Friday Data Flick: A Closer Look at Operations Analysis

david-pittman — Fri, 10 May 2013 14:11:02 +0000

This week’s Friday Data Flick gives you insight into “operations analysis,” which is one of the top five uses (also called “use cases”) for big data. Operations analysis is about analyzing a variety of machine data to get improved business results. The key is combining machine and business data, which allows you to put insight right into the hands of the operational decision maker. We have two videos to help you understand what operations analysis is and how it can be applied.

Cultivating Deeper Customer Understanding

twitterchat — Thu, 09 May 2013 20:29:16 +0000

In our market today, shifts in demographics, attitudes and patterns of behavior fragment customer value drivers. Lifestyle patterns, household composition, and age all cause shifts in value drivers. Customers utilize different ways to shop and purchase -- especially the younger generations. As such, it’sharder for businesses to define “norms.” Customer’s today mandate that businesses dig deeper and leverage all customer data to truly understand their spoken and unspoken needs. In our last #CXO twitterchat guest, Tammy McLeod, VP and Chief Customer Officer, Arizona Public Service, (@T_McLeod) joined us as we dug into the topic “Cultivating Deeper Customer Understanding.” Here are some of the thoughts from that conversation.

Addressing Government Challenges with Big Data Analytics

michael-stevens — Thu, 09 May 2013 12:30:24 +0000

It is my privilege to support government sector marketing for IBM. We live in a complex and sometimes dangerous world with growing threats to public safety, national security and the environment. We expect more and more from our governments and at the same time, there are significant economic and budgetary pressures on national, regional and local jurisdictions.

Measuring the Business Value of Big Data

james-kobielus — Thu, 09 May 2013 11:08:35 +0000

Putting a dollar value on data is a very tricky endeavor. Data is only as valuable as the business outcomes it makes possible, though the data itself is usually not the only factor responsible for those outcomes. Doug Laney of Gartner provides a good discussion here of the challenges in attaching a monetary value to data. He refers to his approach as “infonomics.”

How big data and cognitive computing are transforming insurance – Part 3

kim-minor — Wed, 08 May 2013 17:41:04 +0000

In my previous two blogs [here and here], I’ve talked about how cognitive computing and big data present the insurance industry with great opportunities and some challenges. To develop strategies that capitalize on the potential gold mine of information that big data represents, many carriers will have to challenge their data-centric, business-as-usual approach and the traditional principles that form the foundation of the industry.

Making Communications Smarter: Join IBM at TM Forum’s Management World

gaurav-deshpande — Wed, 08 May 2013 16:43:46 +0000

Next week, thousands of telecommunications professionals will gather in Nice, France for the annual TM Forum. The top talk is about navigating the “digital storm” that is sweeping the industry.

Storms are full of power and they can be disruptive. At IBM, we see the data storm as a fury of untapped information, and we are working with our clients to harness the power of big data.

Big Data Integration

Praveenkumar-Hosangadi — Wed, 08 May 2013 12:09:12 +0000

Given the explosion in the volume, variety and velocity of data growth, it is clear that big data has a low value per byte compared to the traditional enterprise data. An oft repeated analogy to this is a gold mine where you dig tons of dirt to discover an ounce of gold. But, enterprises can still derive superior insights from big data. The question here is: how do we process big data and derive insights at a lower cost?

3 Telcos Call On Big Data

rima-mukherjee — Tue, 07 May 2013 20:12:08 +0000

Across industries, companies are finding that a critical factor in their success is the ability to analyze massive amounts of data in near real time. Telecommunications is one of the leading industries that not only creates a lot of data but is also tasked with understanding customer behavior and gaining insight to improve business decision-making at a very fast pace.

Share to Unlock Data’s Potential

mike-kearney — Tue, 07 May 2013 12:11:13 +0000

Common themes of many big data success stories are the imperative to analyze data in motion (generated by customers, instrumented devices and sensors), and to combine these newly acquired data with one or more historic data sets. Correlation and predictive analytics help us understand now in context of then. Previously, we looked primarily within our organizations for these historic data. This is changing as our society recognizes the value of sharing data via the web.

Will Hadoop replace or augment your Enterprise Data Warehouse?

ven-kumar — Tue, 07 May 2013 11:10:11 +0000

There is all the buzz about Hadoop these days and its potential for replacing the enterprise data warehouse (EDW). The promise of Hadoop has been the ability to store and process massive amounts of data using commodity hardware that scales extremely well and at very low cost. Hadoop is good for batch-oriented work and not really good at OLTP workloads.

5 Game-Changing Big Data Strategies

matt-carter — Mon, 06 May 2013 14:47:55 +0000

Though the true focus of IBM's 2013 Impact Conference is "mobile" technology, Eric Sall's Wednesday session on "5 Game-Changing Big Data Strategies" was standing-room only. Sall is Vice President of Product Marketing for IBM Information Management and has been speaking quite a bit about these key big data strategies. While studying hundreds of examples of customers leveraging big data, five key use-cases have emerged.

How MDM Fits with Big Data, Mobile & Cloud

joncase — Mon, 06 May 2013 05:00:00 +0000

More and more, organizations are asking how MDM fits in with other emerging trends – namely big data, mobile and cloud. We’ll discuss how these trends intersect.

Big Data - For today's post, big data means the overall umbrella of social media data, unstructured documents, streaming data from instrumented devices, and more.

Friday Data Flick: Uses of Big Data and Hadoop as Data Warehouse

david-pittman — Fri, 03 May 2013 16:44:49 +0000

You like videos and we like making them for you. This week, for our Friday Data Flick feature, we bring you three new videos: two that help you understand more about the top uses for big data and one that takes an in-depth look at Hadoop as a data warehouse.

Top Big Data Use Cases Explained

Eric Sall, vice president of product marketing for IBM's Information Management Group, joins Wikibon analysts Dave Vellante and Jeff Kelly to discuss real-world big data use cases:

Crowdsourced Surveillance: Next Big App of Big Media

james-kobielus — Fri, 03 May 2013 11:11:52 +0000

Boston’s recent ordeal demonstrated to everybody that civilization now has a powerful new tool for constant surveillance. Whether we use it in the cause of catching the bad guys or letting the bad guys control our lives is another question. But there’s no denying that social media, crowdsourcing, digital photography and the “Internet of Things” have eliminated any lingering illusions that we’re not being watched, or are capable of being monitored 24x7, in our public lives.

The Impact of Digital Innovation on Customer Experience

twitterchat — Thu, 02 May 2013 21:15:24 +0000

Digital innovation transforms the way we communicate, shop and buy. With unlimited data access and choices, customers today expect instant gratification. They have company and product data at their disposal and demand that businesses know them and deliver exceptional service. To compete successfully, businesses must ready their sales team for this digitization with customer insight and knowledge. As such, companies must utilize digital networks and all customer interactions online and offline to garner deeper customer understanding. In our recent #CXO chat, Brian Vellmure, Analyst, Consultant, and Keynote Speaker, joined us as we discussed “the impact of digital innovation on customer experience.” Here is a glimpse of that discussion.