2.5x Stock Price AppreciationSource: Outperforming in a data-rich, hyper-connected world, IBM Center for Applied Insights study conducted in cooperation with the Economist Intelligence Unit and the IBM Institute of Business Value. 2012
And leaders are outperforming their competitors in key financial measures
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What can I do now to unlock the value of business analytics within my organization? Script: Analytics has evolved from business initiative to business imperative. As you can see on the chart… a recent study conducted by the IBM Institute of Business Value and MIT Sloan Management Review revealed that the number of enterprises using analytics to create a competitive advantage jumped almost 60% in just one year… Nearly 6 out of 10 organizations now differentiating through analytics. We found that the overall increase in advantage went almost exclusively to organizations who were already experienced users of analytics… so the early adopters are extending their leadership and the laggards need to worry. These organizations who are achieving competitive advantage through analytics are seeing real measurable benefits in key financial measures… 1.6 times greater revenue growth 2 times greater EBITDA growth 2.5 times stock price appreciation
Today we are going to address the challenges and opportunities that the present-day Marketer is facing and how specific customer analytic techniques can acquire, grow and retain customers and ultimately, drive ROI for your organization. Today we’ll share a few short demos of our solution, so you can see customer analytics in action.
• How do you progress the experience in your organization?
• Deliver information in context to the user's role/responsibility
• Products that are easy to use, easy to understand
• Capabilities integrated into other business activities
Embedded
Consumer
/Individual
Line of
Business
IT
Time
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Delivering information in context to the user's role and responsibility--it's one thing to give someone a blank canvas and ask them to paint a picture. It's another to get them started in the right direction with pertinent information about what they care about (the context). Products that are easy to use, easy to understand -- it doesn't require intense training to pick up and use these products. They're visual, they're straightforward, they are "walk up and use" Capabilities integrated into other business activities -- sometimes people want to start with analytics, and then traverse from there. Others want to start in another activity and invoke analytics within that activity. Placing analytics at the point of impact--wherever that may be--is important to expand use throughout the business.
• Delivered in context of a person’s role• Rich, visual experience, with potential new use cases within/outside the enterprise• Fosters expanding business analytics within your environment
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IBM continues to invest in the iOS platform and Cognos Mobile V10.2 sees enhancements that deepen the integration with the iOS on the iPad. Namely, the ability to leverage iOS notifications. These notifications are very familiar to users who are iPad/iPhone users. A user can now choose when and how they are notified should reports be updated and sent to their iPads. Using the “settings” feature on the iPad, a user can turn on the server notifications option and then receive updates similar to how they would receive email, text or any other notification-based applications. So mobile users know when new content has been delivered or when reports have been updated with new data for more timely decisions.
Visualization Coach Guides Users to Best Visualization in Cognos Workspace
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It can be difficult to know how best to visualize a given dataset. With the latest release of Cognos Workspace, we have taken the ‘guess work’ out of how to best visualize information. By introducing the Visualization Coach, a user is offered recommendations on which chart-type will work best for your data. A user is free to override the recommendation if he/she prefers. To provide an example, users are often attracted to flashy gauge charts or pie charts. Unfortunately, they aren’t effective for visualizing time-series data. By applying the Visualization Coach, it would guide the user to use a line chart to effectively visualize time series data. And the best part of the Visualization Coach? It’s ‘heuristics based’. What this means is that it learns as you use it. It will become familiar to what visualizations a user leans towards and the best way to display the data.
Explore visualization on IBM Many Eyes IBM’s hub of interactive visualization surfacing thought leadership and technology
IBM Research Center for Advanced Visualization
Industry and visualization expertise
Interactive visualization with ‘looks and smarts’Community of 100K+ visualization enthusiasts & experts
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To deliver on the new prominence of visualization and to become the industry’s leading voice in visualization, we are bringing together all the valuable assets that we have across IBM---Research, Expertise, Technology and an Ecosystem---at Many Eyes. Our community for visual analytics thought leadership and technology. Engage us on Many Eyes today and participate as we evolve the community over the next several months as new capabilities and unique visualization insights from industry experts are added to the site. IBM Research These assets include our scientists and researchers in IBM Research, who have in the past contributed to some foundational work in visualizations, including the invention of the now-popular Wordle and phrase net visualizations for unstructured text. IBM Research continues to invent and build brand new visualizations for us, such as the temporal-causal visualization in the IBM CFO Performance Insight Dashboard signature solution. Visualization Luminaries Working alongside our researchers, we also have deep and broad industry expertise within IBM: people who understand your business domain, and the unique visualization requirements your industry, your data, your users demand. Every business is unique, and it is important to fine tune visualizations and analytics accordingly. From Data to Insight We have also built new technologies for visual analytics. Over the past two years, we have taken the literally hundreds of years of experience our development teams have in charting and visualization engines, and built from the ground up a new, interactive visualization engine. Powered by a declarative language, our new engine not only allows all known business charts to be described in this language, but it is flexible enough to accommodate brand new, unknown visualizations. Further, those visualizations can be deployed to multiple platforms: not just web, but also desktop, server, even mobile native applications. This engine is already being used by products in market today. We have also built new intelligent capabilities – powerful analytical components underneath the visualization engine – to help you interactively explore and understand your data. Capabilities that can add extra intelligence, relevance and meaning to your data, so you get more from your raw data than just the raw data itself. Expert systems that can anticipate what you want from your data, and provide you the answers you seek. Ecosystem At the center of the visualization ecosystem is the IBM Many Eyes community. Many Eyes is community of 100,000 visualization enthusiasts that connects IBM visualization luminaries, academia and professionals. Many Eyes is IBM’s portal for visualization thought leadership. It will provide the capability to create visualizations using IBM Rave, interact with visualizations and embed and share them across social networks. The Many Eyes web community democratizes data visualization by providing a simple three step process to create and interact with a visualization using the member’s data set.
Destination web community of 100,000+ visualization enthusiasts• Democratizes visualization creation
• Create a visualization in three easy steps
• Share and embed across the web• Visualization thought leadership
• Learn best practices and industry insight from visualization luminaries
• Comprehensively redesigned and updated site launching soon Preview the new site & explore visualizations at
www.many-eyes.com/
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This chart is the call-to-action for the audience. It explains the benefits of the Many Eyes visualization community (www.many-eyes.com) and positions the upcoming changes to the site launching in early 2013.
• Over 80% of information being stored is unstructured
• Text analytics unlocks the power of that information for a variety of functions and applications
ContentData
What is Text Analytics?Text Analytics (NLP*) describes a set of linguistic, statistical, and machine learning techniques that allow text to be analyzed and key information extracted for business integration
Many use cases in multiple industries where text analysis optimizes decision making and outcomes
• Social media and sentiment analysis• Fraud detection and prediction (insurance, FSS, healthcare and more)• Quality insight and warranty analysis (manufacturing)• Case management analytics (public sector, FSS, healthcare and more)• Healthcare provider analysis and optimization• Government, police and security intelligence• Regulatory compliance
Analyzing text to improve decision making and business outcomes is a closed loop process.
Ability to Rapidly Explore to Understand What is Happening
Text Analysis
Ability to Model and Predict Better
Business Outcomes
IBM Content Analytics IBM SPSS Modeler Premium
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Understand IBM Content Analytics (ICA) Based on search and exploration of unstructured and structured data by any knowledge worker or analyst Rapidly derive insight by visually exploring content and data to dynamically identify KPI, trends, patterns, correlations and anomalies to enable better decision making Enable deeper insight by integrating to other systems including ECM, data warehouse, Cognos BI or SMP predictive scenarios Optimize and Predict Based on text analysis, rules and predictive models by business analysts Leverages unstructured and structured information to identify KPP, and develop predictive models to optimize business outcomes and automate decisions Use SMP to build models and rules from ICA derived insights (optional)
When segmented into four groups based on current levels of big data activity, respondents showed significant consistency in organizational behaviors
Total respondents n = 1061Totals do not equal 100% due to rounding
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To better understand the big data landscape, we asked respondents to describe the level of big data activities in their organizations today. The results suggest four main stages of big data adoption and progression along a continuum that we have labeled Educate, Explore, Engage and Execute: Educate: Building a base of knowledge (24 percent of respondents) In the Educate stage, the primary focus is on awareness and knowledge development. Almost 25 percent of respondents indicated they are not yet using big data within their organizations. While some remain relatively unaware of the topic of big data, our interviews suggest that most organizations in this stage are studying the potential benefits of big data technologies and analytics, and trying to better understand how big data can help address important business opportunities in their own industries or markets. Within these organizations, it is mainly individuals doing the knowledge gathering as opposed to formal work groups, and their learnings are not yet being used by the organization. As a result, the potential for big data has not yet been fully understood and embraced by the business executives. Explore: Defining the business case and roadmap (47 percent) The focus of the Explore stage is to develop an organization’s roadmap for big data development. Almost half of respondents reported formal, ongoing discussions within their organizations about how to use big data to solve important business challenges. Key objectives of these organizations include developing a quantifiable business case and creating a big data blueprint. This strategy and roadmap takes into consideration existing data, technology and skills, and then outlines where to start and how to develop a plan aligned with the organization’s business strategy. Engage: Embracing big data (22 percent) In the Engage stage, organizations begin to prove the business value of big data, as well as perform an assessment of their technologies and skills. More than one in five respondent organizations is currently developing POCs to validate the requirements associated with implementing big data initiatives, as well as to articulate the expected returns. Organizations in this group are working – within a defined, limited scope – to understand and test the technologies and skills required to capitalize on new sources of data. Execute: Implementing big data at scale (6 percent) In the Execute stage, big data and analytics capabilities are more widely operationalized and implemented within the organization. However, only 6 percent of respondents reported that their organizations have implemented two or more big data solutions at scale – the threshold for advancing to this stage. The small number of organizations in the Execute stage is consistent with the implementations we see in the marketplace. Importantly, these leading organizations are leveraging big data to transform their businesses and thus are deriving the greatest value from their information assets. With the rate of enterprise big data adoption accelerating rapidly – as evidenced by 22 percent of respondents in the Engage stage, with either POCs or active pilots underway – we expect the percentage of organizations at this stage to more than double over the next year.
Study highlights how organizations are moving forward with big data
Big data is dependent upon a scalable and extensible information foundation
Big data is dependent upon a scalable and extensible information foundation2
The emerging pattern of big data adoption is focused upon delivering measureable business value
The emerging pattern of big data adoption is focused upon delivering measureable business value5
Customer analytics are driving big data initiativesCustomer analytics are driving big data initiatives1
Big data requires strong analytics capabilitiesBig data requires strong analytics capabilities4
Initial big data efforts are focused on gaining insights from existing and new sources of internal data
Initial big data efforts are focused on gaining insights from existing and new sources of internal data3
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By analyzing the responses of these early adopters, five key study findings show some common and interesting trends and insights: Across industries, the business case for big data is strongly focused on addressing customer-centric objectives A scalable and extensible information management foundation is a prerequisite for big data advancement Organizations are beginning their pilots and implementations by using existing and newly accessible internal sources of data Advanced analytic capabilities are required, yet often lacking, for organizations to get the most value from big data As organizations’ awareness and involvement in big data grows, we see four stages of big data adoption emerging.
e.g. optimize supply chain with sensor data and warranty claims
Create new perspectives
e.g. personalize co-marketing offers based on location information
Uncover new business opportunities
e.g. use Hadoop infrastructure for storing historical information
Reduce IT costs
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In fact, we have recognized the importance of Big Data for many years (it just wasn't’t called Big Data back then) and have established solutions in the marketplace tailored to Big Data: Volume – Unlike statistical analysis where you primarily deal with a sample in order to understand the big picture, data mining has always been best utilized when it consumes all data available. From the inception of data mining as a discipline it has been a bottom’s up approach, and large volumes of have always been in play. IBM SPSS Modeler is a world class data mining platform has an unbroken legacy of high performance, and accuracy, against large volumes of data. Velocity – SPSS Modeler streams can be published into an InfoSphere Streams to then allow streaming data - to the tune of billions of events per hour – to receive scores in real time. This means transactions in real time can be determined as fraudulent or not instantly, or millions of customers tagged with cross and up sell offers in seconds. Variety – IBM Social Media Analytics (Cognos Consumer Insight), SPSS Modeler Premium’s text analytics workbench, and even SPSS Text Analytics for Surveys allows for a wide variety of data to be included in the analysis (predictive or otherwise). Key today is the ability to understand and integrate the object, subject and sentiment of social media content. IBM SMA and Modeler Premium are both geared to do this for the ability to gain greater insight (SMA) and integrate such insights into predictive modeling (Modeler Premium). Veracity – SPSS Modeler Premium has Entity Analytics capability. This enables the resolution of multiple entities, such as customer records, where some are duplicates and some are deliberative obfuscations of truth for the purposes of committing fraud. This capability also extends to resolution of many “”differing” records of assets, which is critical when that asset is something like an engine on a passenger plane getting checkups at myriad airports around the world.
Take action and automate processes Use analytics to drive automated decisions that maximize business outcomes faster
• Quickly process and analyze perishable data from multiple input streams
• React immediately to opportunities before they expire using insight and rules
• Visualize streaming data to enable analysis and monitoring of events
• Trigger processes to manage customer cases and improve business outcomes
• Smarter fraud detection• Reduced claims processing time on low-risk claims by nearly 90%• Saved more than USD 2.5 million in payouts to fraudulent customers
Think….Plan….Act• Options available right now to advance your analytic journey• Determine which options will have biggest payback for your organization
Get it “right”• Right information to right people at right time in right consumption model• Consider accessing textual data to give richer insights
Tap information you’ve not been able to access• It’s there, just waiting to be understood; Start with readily accessible data,
open architecture and expand. • Find the value—the key to analytic success.
