PDF PREVIEW EMERGING TECHNOLOGIES. Applying Technologies for Social Media Data Analysis

Transcription

1 VOLUME 34 BEST PRACTICES IN BUSINESS INTELLIGENCE AND DATA WAREHOUSING FROM LEADING SOLUTION PROVIDERS AND EXPERTS PDF PREVIEW IN EMERGING TECHNOLOGIES POWERFUL CASE STUDIES AND LESSONS LEARNED FOCUSING ON: Agile Business Intelligence Real-Time Data Integration Self-Service Business Intelligence Tools Five Emerging Trends at the Leading Edge of Business Intelligence and Analytics David Stodder, TDWI Research Find out about the five trends that are shaping technology s evolution and implementation. PAGE 2 TDWI RESEARCH EXCERPTS Matching Functionality to Requirements PAGE 12 Applying Technologies for Social Media Data Analysis PAGE 16 TDWI BEST PRACTICES AWARDS 2012 PAGE 20

2 Table of Contents 2 Five Emerging Trends at the Leading Edge of Business Intelligence and Analytics Find out about the five trends that are shaping technology s evolution and implementation. David Stodder, Director, TDWI Research, Business Intelligence CASE STUDIES AND LESSONS FROM THE EXPERTS 6 Emerging Technologies Defined EMERGING TECHNOLOGIES IN BUSINESS INTELLIGENCE 7 KYDEX, LLC Gains Faster, Easier Access to Key Information and Analysis 8 Building Customer Equity at International Financial Data Services 9 Customer-Facing BI: Simple Concept, Big Benefits TDWI RESEARCH: BEST PRACTICES REPORTS EXCERPTS 12 Matching Functionality to Requirements 16 Applying Technologies for Social Media Data Analysis MORE INFORMATION 20 Best Practices Awards Solution Providers 28 About TDWI 29 TDWI Partners EMERGING TECHNOLOGIES IN DATA INTEGRATION 10 MegaFon Centralizes 200 Billion Real-Time, Mobile Communications Billing Transactions to Analyze Security and Revenue, and Protect Against Fraud 11 Five Ways to Revolutionize Business Insight with Real-Time Data Integration WHAT WORKS in EMERGING TECHNOLOGIES VOLUME 34 1

3 Five Emerging Trends at the Leading Edge of Business Intelligence and Analytics DAVID STODDER, DIRECTOR, TDWI RESEARCH, BUSINESS INTELLIGENCE If only the recipe for good business intelligence (BI) and analytics were as simple as just add data. As users adjust to dynamic changes in their business environments and apply information insights to realize new competitive advantages, requirements for BI and analytics change. As requirements change, so does data. We are moving into an era in which canned reports and predetermined analyses are not good enough. Users want control so they can interact with the data as they see fit and bring in new data sources if necessary. Fortunately, technologies and practices are evolving to meet users needs. As always, organizations have to evaluate the strengths and weaknesses of new technologies based on the purposes for which they plan to deploy them. The good news, however, is that new players arrive and shake things up just when it seems like there will be no more innovation. We ve seen this in every sector that matters for BI and analytics, including databases, storage, servers, and data integration and transformation not to mention BI tools themselves. This article covers five emerging trends that are shaping technology s evolution and implementation. The trends focus on changes in user requirements as they seek to do more with data, and as organizations seek to apply information to a wider array of decisions. 1. Machine data is the new frontier for analytics and operational improvement. When the topic of big data comes up, professionals in the BI and data warehousing (DW) community can be forgiven for focusing rather exclusively on the data that they know: massive volumes of it, coming in like a tsunami. However, much of this big data falls outside the traditional structures of transaction data, business application data, or standard documents. Most organizations have 2 WHAT WORKS in EMERGING TECHNOLOGIES VOLUME 34

4 considerable amounts of data created by their applications, yet they often overlook nontraditional data generated by their own networks, websites, appliances, and other systems. The volume of this machine data coming from both internal and external systems and devices is starting to explode. Every day, we step further into an interconnected world known as the Internet of things, a phrase credited to technology visionary Kevin Ashton. Networks of sensors, including radio frequency identification (RFID) tags, smart meters, cameras, electronic toll collection devices, and other tracking equipment are used in the military as well as by manufacturing, supply chain, energy, utilities, and retail organizations. Joining these things are mobile devices now used by billions of people that generate and consume user authentication data, location data, IP addresses, and more. Medical devices, household appliances, set-top boxes, and vehicles are rapidly joining the Internet of things as well. Although we are just at the beginning of this trend from a data management perspective, organizations today can discover insights from machine data sources as well as through analysis of trends, patterns, and correlations found in relationships between machine and traditional data sources. Thus, it is important for organizations to become familiar with machine data, including where and why it is being generated, which is important to understanding its structure. Contained within these sources could be a mother lode of insight about customer behavior, fraudulent and threatening activity, complex decision processes, and much more. 2. Predictive analytics becomes part of the mainstream BI/analytics portfolio. Predictive analytics is fast becoming decisive for achieving a range of desired business outcomes, including higher customer profitability, stickier websites, more relevant products and services, and more efficient operations and finances. Predictive analytics involves methods and technologies for organizations to spot patterns and trends in big data, test large numbers of variables, develop and score models, and mine data for unexpected insights. Sources for predictive analytics are expanding to include semistructured and unstructured data, such as machine data, which makes it important to incorporate search and text analytics (or mining) into technology portfolios. When applied to marketing processes, predictive analytics can play a major role in helping organizations investigate how much customers in particular segments are spending over time, and model their propensity to buy additional or more expensive products. Organizations can analyze variables in customer life cycles to determine when to attempt cross-sell and up-sell offers and what kinds of products to offer at significant phases in the life cycle. However, predictive analytics has largely been out of reach for many organizations due to inadequate budgets and expertise. The specialized work of developing models and running analytic processes has been exclusively the realm of an elite cadre of data scientists working primarily at large organizations. Figure 1 shows how predictive analytics and other statistical analysis tools and techniques rank in terms of current implementation for customer analytics. Although the percentages using these tools and techniques are significant, the penetration is not as great as it could be. (The results are drawn from TDWI Research s recent Best Practices Report, Customer Analytics in the Age of Social Media. An excerpt from this report can be found on page 16.) What analytic tools or techniques are currently being implemented in your organization for customer analytics? (Please select all that apply.) Basic reporting 76% OLAP/ business intelligence 65% Statistical techniques (e.g., regression analysis) 35% Data mining/machine learning 34% Segmentation techniques (e.g., clustering, k-means) 33% Predictive modeling and scoring 31% Location/geospatial analysis 21% Basic text analysis 16% Pattern matching or path analysis 16% Big data analytics (e.g., Hadoop, MapReduce) 10% Sentiment analysis 10% Social influencer or graph analysis 9% Attribution analysis 7% Figure 1. Based on 1,546 responses from 426 respondents; nearly 4 responses per respondent, on average. Two emerging trends will help organizations that do not have resident Ph.D.s and large budgets. First, many organizations can take advantage of cloud computing options to avoid the expense and difficulty of establishing in-house infrastructure; they can create analytic sandboxes in the cloud for predictive analytics. Second, with many BI and analytics technology vendors tightening the integration between predictive analytics tools and BI, it is becoming easier to develop a complementary strategy that uses current BI technology investments more effectively. Although BI and analytics have different use patterns for data, a complementary strategy can significantly strengthen data-driven decision making across an organization. 3. Data visualization enables nontechnical users to work effectively with analytics. As big data volumes grow and organizations seek to integrate diverse and complex information, a user s ability to quickly comprehend the data s significance and put insights to use hinges on data visualization. Data visualization and visual data analysis enable nontechnical users to see data patterns and trends they would have struggled to grasp with tabular reports, spreadsheets, and primitive graphics. Improved technologies on the front and back ends of BI, analytics, and DW systems are enabling more exciting and sophisticated graphics, including animation, for users to employ in their reporting, analysis, simulation, forecasting, and more. Read the rest of Five Emerging Trends at the Leading Edge of Business Intelligence and Analytics and more by downloading the full publication here. WHAT WORKS in EMERGING TECHNOLOGIES VOLUME 34 3

5 Enjoyed this sample? Download the full publication to read more case studies and lessons, plus other valuable TDWI content and research.