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The growing network of entities and the relationships among them in the intelligenteconomy will challenge many of todays existing systems to optimize processes forsupporting an integrated set of strategic, operational, and tactical decision-makingprocesses. As a result, a new set of technologies is emerging to address theenterprise decision-making, analytics, and process optimization challenges. Based onin-memory processing, these technologies are creating the "freedom of excess" fororganizations across industries to compete at their peak performance.Technology providers and users have already started to migrate to in-memorytechnology to better capitalize on the opportunities presented by the intelligenteconomy. Where it has already been used, in-memory technology has been provento be effective and has experienced strong growth. In-memory technology platformsand specific applications built on such a platform have begun to significantly changethe computing landscape, and we believe they will continue to do so for theforeseeable future.Over the course of several months in 2011, IDC conducted a research study toidentify the opportunities and challenges to adoption of a new technology thatchanges the way in which traditional business solutions are implemented and used.The results of the study are presented in this white paper.METHODOLOGYThe goal of the research presented in this white paper was to discover how useof analytics, data warehouses, and business applications could be improved throughin-memory-based technologies.IDC began by conducting four focus groups in the United States and Germany thatgathered input from both IT managers and line-of-business managers and executivesin large public and private organizations across industries to gauge their level ofinterest in and the perception of the concept of in-memory technology if applied tocurrent analytic and operational processes.The discoveries made in the focus group, coupled with analyst experience, were usedto form a Web-based survey translated into four languages that resulted in anextensive data set from 1,002 respondents worldwide. Business unit–level executivesand managers with a stake in issues relevant to analytics, process innovation, andmanagement accounted for 70% of the respondents. IT managers, directors, andexecutives with responsibility for or knowledge of business process management,business analysis, systems architecture, or application development accounted for30% of the respondents. The survey was completed in August 2011.2 #230964 ©2011 IDC
3. Information Analysis and ManagementPractices: Industry Leaders Versus LaggardsThe information access, analysis, and management challenges of the intelligenteconomy can overwhelm organizations unprepared for the emerging changes. IDCdefines these organizations as "fumblers" compared with "fact finders." As describedin IDCs study Analytical Orientation and Competitiveness: The Difference BetweenFact Finders and Fumblers (IDC #223408, May 2010), "fact finders" are organizationsthat have the highest levels of the following characteristics: Reliance on analytics, which is defined as the degree to which the organizations management-level employees rely on analytics (as opposed to experience or intuition) for decision making Influence on actions, which is defined as the extent to which the output of the organizations business analytics solutions influences all employees actions Criticality to competitiveness, which is defined as the perceived level of criticality of the business analytics solutions to the organizations competitiveness Effectiveness of analysis on decision making, made at various levels, which is defined as the importance of the organizations business analytics technology and processes in helping to improve individual decision making or in facilitating intragroup or intergroup decision makingOur research demonstrates that, as a group, fact finders are more competitive thanfumblers (see Figure 1). Among leaders, 80% are fact finders — 20% more thanamong laggards. Their competitive differentiation, based on the application ofanalytics for ad hoc analysis, discovery, or planning processes and embedded intoother business applications, will continue to increase in the intelligent economy.©2011 IDC #230964 3
4. FIGURE 1Competitiveness of Fact Finders Versus Fumblers 100% 90% Fumblers 80% Fumblers 70% (% of respondents) 60% 50% 40% Fact Finders 30% Fact Finders 20% 10% 0% Laggards Leaders (Competitiveness groups)n = 1,000Source: Analytical Orientation and Competitiveness: The Difference Between Fact Finders and Fumblers (IDC#223408, May 2010)One of the common characteristics of these industry leaders is how they useinformation. IDC research has shown that leading organizations tend to: Base decisions on analytics rather than intuition Base decisions on the latest, granular multistructured data Rapidly evaluate alternative scenarios Frequently reassess forecasts and plans Utilize analytics to support a spectrum of strategic, operational, and tactical decision making4 #230964 ©2011 IDC
5. These leading companies are not limited by the lack of freshest data because theirsystems cannot retrieve or combine data into useful forms fast enough. Examples ofleaders include: Financial services firms whose trading systems support buy and sell decisions that are made automatically based on price shift patterns, and where even microseconds count Large retail chains that ensure the most competitive prices for their products while closely managing inventory by carefully tracking consumption patterns and shifting items from suppliers to stores in an optimal manner (Such systems require the very latest data, and the timelier the data is, the easier it is to offer products at the lowest cost while avoiding either overstocking or running out.) Banks that used to rely on overnight grace periods to reconcile their accounts data for online users, giving such users working estimates rather than actual account balances, which is no longer acceptable (Managing current account balances while tracking numerous online banking, bill pay, check processing, and ATM transactions demands the fastest data processing capability possible.)These examples, taken together, demonstrate that businesses do not need to Our research shows that it is possible to becompromise when it comes to timely information. Previous technology constraints at the top of your field,dictated how organizations utilized their data. Our research shows that it is possible to to be "faster, higher, stronger," if you havebe at the top of your field, to be "faster, higher, stronger," if you have the right data, at the right data, at thethe right time, delivered through the right technology. right time, delivered through the right technology.Identifying Data, Analytics, and Timeliness RequirementsImagine an Olympic sprinter who at the start of the race receives a tenth of a secondadvantage over her competitors. This could be a significant head start toward victory.However, the same tenth of a second would be meaningless to a marathon runner.Business decisions are no different. For example, an airline making rapid, ongoingpricing decisions has very different requirements from the airline manufacturerdeciding what new airplane model to produce. Figure 2 illustrates the key dimensionsfor evaluating IT requirements in support of a specific business decision.©2011 IDC #230964 5
6. FIGURE 2Range of Data, Analytics, and Decision TimelinessRequirements Data Volume Data Variety High Low Complexity of Analytics Time ConstraintSource: IDC, 2011 Data can be multistructured and multisourced. It can be structured, As the volume, variety, and velocity of semistructured, or unstructured; it can come from enterprise applications, point- data needing to be of-sale systems, clickstream from online commercial and social networking analyzed increase, so too does the activity, call detail records (CDRs), mobile devices, or sensors. Some of the data, requirement for a such as CDRs, mobile device activity, or vehicle traffic data, can be considered system that can part of the Big Data trend that emphasizes high-volume or high-velocity data of handle the corresponding various types. As the volume, variety, and velocity of data needing to be performance and analyzed increase, so too does the requirement for a system that can handle the analytic complexity requirements. corresponding performance and analytic complexity requirements. During one of the focus groups, a manager from a law firm explained how structured and unstructured data from multiple sources impacted performance when he said, "We need to have tools that enable us to track all client billing data from all the relevant client interaction channels, including mobile devices. Inability to do so is directly affecting our profitability." Analytics can involve relatively simplistic aggregations or complex predictive models, with visual and interactive interfaces when the intended consumer is a human decision maker and in the form of rules or scores when the target is another system that executes the decision automatically. As the complexity of analysis increases, so too does the need for a system that can enable users to perform the analysis rapidly, often over several iterations before a decision can be made. A promotional products wholesaler stated, "We cant just analyze past sales. We need to figure out what sort of sales to expect for the next couple of periods and how hot the particular design is going to be. We need to be able to better plan for what we need to sell or to ship to stores or concert dates.
