Business Intelligence and Big Data Analytics: Speeding the Cycle from Insights to Action Four Steps to More Profitable Customer Engagement

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1 white paper Business Intelligence and Big Data Analytics: Speeding the Cycle from Insights to Action Four Steps to More Profitable Customer Engagement»» Summary For business intelligence analysts the era of Big Data is good news and bad news. Good news, because they now have more data than ever on virtually every aspect of their business and their customers. Bad news, because the sheer volume of data and the speed at which it accumulates make it extremely challenging to analyze and derive value from it in a timely way. More to the point, the biggest challenge is how to use that data effectively how to transform data into intelligence, intelligence into insights, and insights into actions that will improve customer engagement and interaction. Data also ages rapidly, updating many times a day or even in real time. Organizations have to learn how to act on it before it gets too old and opportunities pass. Conventional business intelligence solutions by themselves take too long to deliver the kind of actionable results that a rapidly changing marketplace demands. Business Intelligence (BI) systems have relied on database snapshots in time data warehouses for their analytics. They cannot keep pace with the accumulation and aging of Big Data. This paper examines how augmenting BI with predictive analytics and decision management to leverage Big Data can accelerate the cycle from data analysis to delivering operational decisions that drive profitable customer engagement. And it introduces a solution for speeding the adoption, integration and deployment of analytic-powered decision management to turn business intelligence into real-time business decisions. Make every decision count TM

2 »» Business Intelligence and Customer Engagement In recent years, enterprises have increasingly turned to business intelligence not simply to understand how the business is performing and why, but also to figure out where and how to improve performance. Business managers are raising questions and demanding answers that require BI experts to look at business performance from new and different perspectives. It used to be enough to know what was selling and the impact of various factors pricing, seasonality, geography, competition and others to guide management decisions. Now, managers want to know not just what is selling but who is buying and what is motivating them, as well as what is not selling and why. And based on that knowledge, what can the company do to generate more profitable business from existing and new customers? The data certainly exists to help answer those questions. In the age of electronic transactions through multiple channels, businesses have become very adept at gathering data on customer behavior and interaction. The problem is sifting through that data, analyzing and deriving meaning and value from it. Analytic models make it easier to identify insights hidden in large amounts of data, compared to human analysts wading through tables and graphs to arrive at insights using traditional BI reports. However, creating analytic models and putting them to work in decisions that drive customer engagement currently takes too long, consumes resources and requires skills most organizations lack. Creating an automated machine learning loop that constantly watches and learns from customer behavior to improve engagement and business profitability is the ultimate goal and is very hard to do. In the past, business intelligence systems were effective because data and market conditions did not change as often or as quickly. And they certainly play a valuable role in providing senior executives with the big-picture perspective for making long-term strategic decisions. The fact is, though, that the majority of decisions in an enterprise are not being made in the C-suite. Traditional business intelligence methodologies do not address the needs of sales, marketing, customer service and other front line units responsible for data-driven decisions that make or break the customer relationship. Today, competitive pressure requires enterprises to be able to turn business intelligence into actionable strategies and deliver results faster. That means being able to push decisions out to the people in the trenches managing everyday customer engagement. And that is where conventional BI tools fall short. BI analysis has tended to be historical in perspective what brought the business to this point? Rather than always looking to the past for answers, enterprises need solutions that give them insights into the possibilities of the future.»»from Descriptive To Prescriptive Analytics: How Decision Management Complements Business Intelligence To better understand how to improve business performance, business intelligence needs to evolve from reliance on descriptive to prescriptive analytics. Descriptive analytics give you metrics that help you understand what has already happened. Prescriptive analytics help you figure out what you need to do to move business metrics in a positive direction. Developing analytical capabilities to help achieve business objectives can be done in incremental steps. Leading organizations are applying the principles of decision management and advanced analytics to create learning loops that automate data capture and analysis to continuously optimize customer engagement and adapt to changing market conditions. Many of these organizations started by taking the initial step of adding predictive analytics to their decision processes Fair Isaac Corporation. All rights reserved. page 2

3 Decision management technology helps advance the enterprise along that path. It enables analysts and managers to formulate automated decision strategies based on a combination of business intelligence data and predictive analytics. It allows them to test and refine alternative strategies and simulate their outcomes to determine the best course of action before committing to it. Business Intelligence Within a Decision Management Process Analytics Human Input Descriptive What happened? Diagnostic Why did it happen? Data Predictive What will happen? Decision Action Prescriptive What should I do? Decision Support Decision Automation Source: Gartner, #G (September 2013) Business Intelligence is just one part of an enterprise end-to-end decision management solution. Decision Management Data Feedback Loop Resolve Assess Act Decide Ensuring that new data or customer action continuously informs and updates a decision management solution is critical to automating and optimizing the process. Once strategies are in place, business users can track their performance via a dashboard and adjust them in real time as market conditions warrant. The cloud-based FICO Decision Management Platform encapsulates a proven set of tools and processes that enable enterprises to build and deploy analytic-powered decision management solutions in a fraction of the time that it would take starting from scratch. It helps companies bridge the gap between business intelligence, which tends to be internal in focus, and channels of customer interaction that require an external focus. Extending decision management with Big Data analytics makes it easier to analyze a greater variety and volume of customer data in a shorter amount of time, using machine learning and predictive modeling techniques. It streamlines the process of turning analytic insights into executable models that can be used to improve business decisions and customer interactions. With FICO s rapid application development tools, organizations can create solutions faster, see the payback from decision management earlier and have greater flexibility to adapt to change Fair Isaac Corporation. All rights reserved. page 3

4 »» Changing the Rules Complementing predictive analytics are the business rules, or decision rules that inform automated decisions. The FICO platform provides business rules management capabilities that enable managers to monitor and update their strategies so they can react quickly to changing market conditions. Another key component of decision management is optimization, which calculates the tradeoffs among potentially thousands of variables to arrive at the optimal course of action Four Steps to More Profitable Engagement to achieve the desired business objective. When the goal is to make each customer 1. Identify the business goals you want to achieve. engagement more effective and profitable, 2. Identify the decision strategies needed to achieve those goals, including such factors as optimization enables the business to extend customers targeted, pricing, timing of offer and others. the right offer to the right customer at the right time, when the customer is most likely 3. Create the analytic models that will use Big Data to generate those decisions based on known customer behavior and value. to accept it, based on known customer value. 4. Deploy, test and optimize continually adapting decisions to get the best performance based on market conditions and business results. As a decision management solution executes business decisions, it is also learning from them. It captures data about actions and results, and feeds that information back into the BI system for measuring the effectiveness of strategies. This continuous learning loop enhances both the quality of customer data in the BI system and the precision of decisions generated.»»breaking Down the Barriers Until recently, the promise of predictive analytics has too often collided with the realities of implementation, putting analytic-powered solutions out of reach of all but the largest enterprises. Among the more obvious obstacles, analytics were deemed too expensive, complex and difficult to deploy. As a cloud-based solution, the FICO Decision Management Platform makes it far easier and less costly to adopt, learn and deploy decision management solutions quickly, eliminating barriers to implementation and accelerating time-to-value significantly. Cloud deployment also eliminates the need for costly hardware and software installation and configuration, while giving organizations the flexibility to scale from tens of thousands to millions of customer interactions. Big Data can confer a big advantage to enterprises but only if they know how to apply it. Otherwise, it is simply an overwhelming mass of information whose value is locked away. Decision management with predictive analytics is the key to harnessing big data and making it work for you. Integrated with business intelligence, decision management speeds the cycle from insight into action and creates competitive advantage through more effective customer engagement and a superior customer experience Fair Isaac Corporation. All rights reserved. page 4

5 Overcoming Obstacles How the cloud-based FICO Decision Management Platform addresses the top 10 historical barriers to analytic adoption. Too Expensive Too Complex Lack of Analytic Talent Hard to Deploy Poor Presentation Long Time to Insight Siloed Data Wrong Questions Cultural Barriers Disconnect from Actions Cloud deployment reduces cost by eliminating need for hardware infrastructure, software implementation and labor. Subscription pricing makes entry costs and investment more aligned to usage. Integration of analytic components with rapid application development capabilities and data visualization tools dramatically reduces the time and resources needed to deliver analytically powered solutions. Customizable applications are designed to help business users do more on their own. Access to a community of experts, templates and easy-to-get-started guides significantly lowers the barriers to entry. Pre-packaged analytic components provide instant deployment of a wide range of models. Support for industry standards allows models to be authored in nearly any authoring tool. Rapid application development tools reduce solution delivery time by up to 10x. Powerful visualization capabilities allow business experts to explore data to track model and decision performance and to uncover new insights to improve future decisions. Integrated solution development environment significantly shortens time to analyze data and turn insights into actions that deliver business value. Machine learning algorithms streamline analytic development and make it possible to optimize decisions in real time. Decision management breaks down data silos and connected decisions enable data in one area of business to inform decisions in every other. Analytics contribute directly to decisions that drive targeted business goals and objectives rather than simply focusing on visual reports and ad hoc queries. Automated and consistent data-driven decisions are a proven approach to improving business performance. Cloud accessibility and collaboration tools allow diverse organizations to work more effectively. Decision management solutions can be easily integrated into channels of customer and business interaction to deliver consistent actions across mobile, web, phone and on-premises interactions. Actions can be automatically tracked to provide a feedback loop that continuously improves predictive models and business strategies.»» Enhancing the Value of Business Intelligence Augmenting BI with analytic-powered decision management has another important benefit: It enhances the value of business intelligence and its role in the organization by turning BI insights into decisions and actions that advance business goals. While investments in BI solutions are smart and necessary, and produce tangible value, adding the power of decision management is what creates a differentiated solution. Business intelligence becomes the platform for not only analyzing and understanding business performance, but also for improving it. Organizations should invest in enhancing their business intelligence platforms with decision management platform capabilities. The combination will make it faster and easier to turn insights into intelligence and actions that drive business value and competitive differentiation Fair Isaac Corporation. All rights reserved. page 5

6 about FICO FICO (NYSE: FICO) is a leading analytics software company, helping businesses in 80+ countries make better decisions that drive higher levels of growth, profitability and customer satisfaction. The company s groundbreaking use of Big Data and mathematical algorithms to predict consumer behavior has transformed entire industries. FICO provides analytics software and tools used across multiple industries to manage risk, fight fraud, build more profitable customer relationships, optimize operations and meet strict government regulations. Many of our products reach industry-wide adoption such as the FICO Score, the standard measure of consumer credit risk in the United States. FICO solutions leverage open-source standards and cloud computing to maximize flexibility, speed deployment and reduce costs. The company also helps millions of people manage their personal credit health. Learn more at For more information North America toll-free International web (0) FICO and Make every decision count are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. Other product and company names herein may be trademarks of their respective owners Fair Isaac Corporation. All rights reserved. 3020WP 10/13 PDF

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