White Paper. Return on Information: The New ROI. Getting value from data

Transcription

1 White Paper Return on Information: The New ROI Getting value from data

2 Contents Introduction... 1 Data Management... 1 Hadoop... 2 Data-Driven Decisions... 2 Data Visualization... 3 Big Data Analytics... 4 How SAS Can Help... 4 SAS Data Management...4 SAS and Hadoop...4 SAS Visual Analytics...4 SAS High-Performance Analytics...5 Conclusion... 5 Content Provider Stuart Rose is Global Insurance Marketing Director at SAS. Rose, a 25-year veteran of the insurance industry, began his career as an actuary. He worked for a global insurance carrier in both its life and property divisions, as well as for several software vendors, where he was responsible for marketing, product management and application development. Rose is a regular contributor to insurance publications and the Analytic Insurer blog. He frequently speaks at insurance conferences and is co-author of the book Executive Guide to Solvency II.

3 1 Introduction Data and analytics have always played a critical role in insurance. In fact, it could be argued that the first mortality tables were a form of analytics as the actuaries were using historical data to forecast the survival rates of their policyholders and insured. However, the explosion of new data is turning the insurance business model on its head. Extracting value from data remains an elusive goal for many insurance companies. The optimist s vision for this tidal wave of data is that organizations will be able to harvest and harness every relevant byte of it to make supremely informed decisions. The pessimist s view is one where organizations are drowning in a sea of data. For many insurers, the pessimist s view is a more common reality. Many decision makers whether data analysts or senior-level executives struggle to draw meaningful conclusions in a timely manner from the array of data available to them. This point of view was confirmed by The Economist report The promise of real-time data. In the report, 40 percent of financial services executives surveyed considered the inability to turn data into valuable insights an internal barrier that impedes their decisions to add value to the organization. In this paper, we will discuss the techniques and technology available for taking advantage of all this data to price better, expand markets and improve the business of underwriting risk and handling claims. Data Management The digital age has brought with it a quantum increase in the amount of data available to the modern organization. But it is not just the quantity of data that sets this time in history apart. The speed with which data reaches organizations, the variety of forms data takes and the insights contained therein are completely changing everything we have known about the collection, analysis and management of data. Many companies struggle with the most basic aspects of data management, such as cleaning, verifying or reconciling data across the organization. Basically, they struggle to deliver important data to the right people within an acceptable time frame. At the heart of most data management challenges is data governance, which helps companies define ownership, common language, process, policies and decision rights about how data is managed over its life cycle. IT departments are often overburdened with increasing workloads and meeting the ever-changing needs of the business. And it is more important than ever that IT and business be in alignment. A proper data management strategy can inform and prioritize IT functions so that IT activity is aligned with business priorities. Growing Big Data Data Diversity Data Quality Issues Master Data Issues Growing data volumes More frequent capture of data Data from new devices and sources New sources of data Legacy systems Mix of data and system types Duplication across systems Incomplete/missing data Data/system silos Incomplete knowledge of customer, product,etc. Conflicting information about a customer, product,etc. Too much time on data preparation/cleansing vs. information evaluation and decision making Figure 1: Data management challenges facing insurance companies.

4 2 Hadoop Data is often seen as a competitive differentiator; however, in today s big data environment, is having too much data really a problem? Many organizations find it too costly to store all the data and too time-consuming to analyze the data. For a growing number of organizations, the answer to overcoming this problem lies in taking advantage of distributed processing technologies, such as the Hadoop file system (HDFS). Hadoop is an open-source software framework for running applications on a large cluster of commodity hardware. Since Hadoop runs on commodity hardware that scales out easily and quickly, organizations are now able to store and archive a lot more data at a much lower cost. This is good news for IT departments, but it should also be music to the business professional s ears. No longer is it necessary to destroy data after its regulatory life is up in order to save on storage costs. No longer must business analysts or data scientists limit their data analysis to the last three, five or seven years. Now insurers can store and analyze all their historical internal and external data. Another advantage of capturing data in Hadoop is that you can store it in its raw, native state. It does not need to be formatted upfront like traditional, structured data stores; rather, you can format it at the time of the data request. To consume data and make it more meaningful, insightful and useful, insurance companies need better applications and tools. And because of how data is growing exponentially, a significant number of insurance companies are investing in big data technologies like Hadoop, even though it s still a relatively new and maturing technology. Data-Driven Decisions In a rapidly changing global business environment, the pressure on organizations to make accurate and timely decisions has never been greater. The ability to identify challenges, spot opportunities and adapt with agility is not just a competitive advantage; it s a requirement for survival. Business analytics is about dramatically improving the way an organization makes decisions, conducts business and successfully competes in the marketplace. Data lies at the heart of business analytics. Historically, the philosophy of many insurers has been to collect data, data and more data. However, even with all this data, many insurers are struggling to do better analytics and gain business insight, simply because many insurers systems typically aren t designed to master the data in such a way to make it useful for analytical purposes. The best way to maximize the efficiency and effectiveness of data-driven decision making is to focus on determining the sufficient amount and quality of data necessary to satisfy the execution of a business decision. Of course, this decisionmaking strategy is easier in theory than it is in practice. Can you really find decision needles in data haystacks? How can you determine when you have satisfied your decisionmaking needs? Data-driven decision management comprises three components: 1. Data requirements. Different decisions will have different data requirements, which include the data volume, variety and velocity necessary for data-driven decision making. Not every decision requires the same amount and quality of data. 2. Decision criteria. For example, a decision that must be made within 30 seconds has very different data requirements than a decision that should be made within 30 minutes or a decision that could be made within 30 days. 3. Decision evaluation. The quality of a decision is determined by the business results it produces, not the person who made the decision, the quality of the data used to support the decision or even the decision-making technique.

5 3 Decision Criteria Data Requirements Decision Criteria Time Constraints and Preferences Business Impact and Risks Human Intuitions and Biases Data Requirements Data Volume, Variety and Velocity Data Management and Governance Data Quality Thresholds Decision Evaluation Decision Evaluation Evaluate Quality Results Decision Quality Thresholds Re-Evaluate Decision Criteria Re-Evaluate Data Requirements Figure 2: Data-driven decision management process. Data Visualization The science of extracting insight from data is constantly evolving. Tools are more readily available, and industries are beginning to invest in the technology that supports big data. As the competency levels of firms continue to move along the big data continuum, they will realize increasing value. Companies that take advantage of data visualization are able to extract maximum value from their data and take their businesses to new heights. Data visualization is an art and a science unto itself, and there are many graphical techniques that insurance executives can use to better understand the stories their data is telling. However, one of the biggest challenges for nontechnical and business users is deciding which visual represents the data accurately. Auto-charting solves this problem, determining the most appropriate visualization by understanding the data and its composition, what information needs to be conveyed visually and how viewers process visual information. You ve no doubt heard the saying a picture is worth a thousand words. This is especially true when trying to understand and gain insights from data. It is particularly relevant when you re trying to find relationships among hundreds or even thousands of variables and to determine their relative importance. Figure 3: Data visualization techniques.

6 4 Big Data Analytics Big data is changing the way companies of all sizes, in all industries, go about their business. But what do insurers hope to derive from the increased volumes of data they collect? The goal depends very much on the insurance company, the market conditions and the strategic imperatives of a given carrier. Organizations want more business value from big data, and analytics is an important route to value. Hence the growth in the term big data analytics. Big data analytics enables you to examine and analyze more complex problems than ever before. The ability to quickly analyze big data can redefine so many important business functions. Below are several areas where big data analytics can positively affect your profitability: Catastrophe modeling. Catastrophe losses can have a significant impact on the financial stability of an insurance company. You must evaluate your loss exposure and financial position to meet liquidity requirements, often in a realtime environment. By using data visualization, you can create geographical risk exposure reports by augmenting existing policy data with geospatial data to assess and monitor loss exposure by geographic region. Subrogation. It is estimated that, on average, 5 percent of claims that should go to subrogation don t. By using data analytics and text mining techniques, you can minimize the number of missed recovery cases by recognizing known and unknown subrogation indicators in the claims information. In fact, one leading European insurer was able to improve its recovery rate by more than 4 percent, representing millions of dollars per year added to its bottom line. Fraud detection. Fraudulent activities are on the rise. Unfortunately, if the fraudulent behavior is not discovered quickly, it may never be detected. High-performance analytics enables you to analyze data within you organizations to detect unusual behavior. You can also use analytics to look at external data, such as social media, to increase the likelihood of detecting fraudulent activities prior to a claim being settled. Telematics. As usage-based insurance becomes more widely available, data visualization software will let you visually explore billions of records/journey points and seek correlation on data sets to develop predictive models for accurately determining risk factors for pricing and claims. For example, insurers are beginning to see a direct correlation between loss experience and driving behavior particularly braking habits. Product pricing. Actuaries have often used samples of historical data to run pricing models because the time it takes to prepare and run the models is too time-consuming. Today, actuarial departments are using advanced analytics like generalized linear modeling on the growing volumes of available data to enable more frequent variable exploration for finding subtle and non-intuitive relationships that can influence product pricing. For example, a regional workers compensation insurer found that account size and number of judgments or liens were very meaningful in terms of which companies would eventually file claims. How SAS Can Help Organizations want more business value from big data, and analytics is an important route to value. Comprehensive analytics management capabilities, spanning from data to decision, make it possible to take advantage of sophisticated analytical techniques, a large number of analytical models and a virtually unlimited number of variables and data volumes. SAS Data Management SAS Data Management enables you to treat your data as a corporate asset to bridge corporate silos, align business and IT, and drive a consistent, accurate view. High-quality, governed data is the foundation of accurate analytics and better decisions. Our unified framework provides one seamless technology for accessing, integrating, managing and governing your data, reducing integration costs and speeding time to value. SAS and Hadoop Out-of-the-box connectivity between SAS and Hadoop lets you take advantage of Hadoop s distributed processing architecture and exceptional scalability to solve a wide range of problems. SAS creates data flows that combine Hadoop processing with SAS processing, delivering optimal performance by using the best execution environment. SAS/ACCESS also supports Hadoop s high-performance stream loading, enabling you to rapidly load data into Hadoop from a variety of sources, including flat files and other relational database sources. SAS Visual Analytics SAS Visual Analytics provides a complete platform for data visualization, enabling you to identify patterns and relationships in data that weren t evident before. SAS Visual Analytics combines powerful in-memory technologies with an extremely easy-touse exploration interface and drag-and-drop analytics. Users of all skill levels can produce analytic visualizations and gain new insights from data.

7 5 SAS High-Performance Analytics Getting insights out of big data in a timely manner requires a different approach in-memory analytics. With SAS High- Performance Analytics, you can tackle previously unsolvable problems using big data and sophisticated analytics. Complex data exploration, model development and model deployment steps are processed in-memory and distributed in parallel across a dedicated set of nodes. Conclusion Insurers have long seen data as a source of competitive advantage. But data alone is worthless; it is insights derived from the data that matter. And with the emergence of big data, the possibilities for deriving insights are increasing dramatically. Data can be the difference between success and failure. Better data leads to better decisions, which ultimately leads to more profitable business. Today, the return on information is just as important as the return on investment.

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