HOW BIG IS BIG DATA? BIG DATA USAGE AND ATTITUDES AMONG NORTH AMERICAN FINANCIAL SERVICES FIRMS

Size: px
Start display at page:

Download "HOW BIG IS BIG DATA? BIG DATA USAGE AND ATTITUDES AMONG NORTH AMERICAN FINANCIAL SERVICES FIRMS"

Transcription

1 HOW BIG IS BIG DATA? BIG DATA USAGE AND ATTITUDES AMONG NORTH AMERICAN FINANCIAL SERVICES FIRMS Craig Beattie, Bob Meara March 19, 2013

2 CONTENTS Executive Summary... 1 Big Data Understanding and Value... 3 Where the Perceived Value Lies... 7 Project Experience: Where s the Beef?... 9 Has Big Data Delivered? Cultural Impact Barriers to Success Going Forward Survey Methodology Leveraging Celent s Expertise Support for Financial Institutions Support for Vendors Related Celent Research... 26

3 EXECUTIVE SUMMARY Big Data remains nascent, with hype surrounding its use far exceeding actual experience of its use. But, among those North American financial services firms with hands-on Big Data experience, Big Data is a big deal that holds big promise and will usher in big cultural changes in how those firms do business. Firms not yet experimenting with Big Data need to be. There is significant activity surrounding Big Data among surveyed firms, but the activity has not yet resulted in live implementations, except for a small minority of larger institutions. Most respondents characterized their firm as immature in its understanding and use of Big Data (see Figure 1). Most North American financial institutions are still getting their arms around the topic and are quick to concede significant gaps in their leveraging of little data to its fullest - much less Big Data. Figure 1: Big Data Remains Nascent Among North American Financial Institutions Big Data Maturity Level Banks 38% 25% 25% 13% Exploring Experimenting Deploying Insurers 25% 25% 25% 25% Expanding 0% 40% 60% 80% 100% Resp. (%) Based on survey results and post-survey interviews among North American banks and insurers, Celent observes these Big Data and analytics trends: Big Data remains trendy and ill-defined. In the absence of a broadly embraced definition, attitudes vary about Big Data. Firms with hands-on experience are more bullish about Big Data and the value it can provide. A majority of firms (60%) maintain that information (including Big Data and analytics) holds the key to competitive advantage to a significant extent. A surprising number (nearly 40%), however, assert a small to moderate influence on competitive advantage.

4 Chapter: Executive Summary Despite the explicit importance of data and analytics competency, a minority of surveyed banks (24%) and insurers () have hands-on experience with Big Data in a production environment. An equivalent percentage have pilot initiatives in place, often more than one. The bulk of experience lies with projects aimed at improving sales results or reducing risk/fraud. Big Data experience is closely tied to the value placed on data and its ability to derive competitive advantage. Early-adopters are those embracing a competitive advantage by doing so. Among financial institutions with longevity of experience (more than one year), 70% of projects have met or exceeded business case expectations. For these institutions, the promise of Big Data is very real. Never mind Big Data per se, the status quo among most North American financial services firms is decidedly not as data-driven as it should be. Survey respondents were quick to note that data abounds, but it is not systematically used to its fullest advantage. Barriers to success are many, with lack of technical expertise and analytic talent key among them. Deriving compelling value from Big Data thus requires deliberate and sustained effort. Some financial institutions are comparatively well along the Big Data maturity continuum, but they are in a small minority. The road from product silos to customer centricity is indeed a long road that most have not begun in earnest to traverse. In this context, Big Data might even be icing on the cake. To those willing to do the hard work, however, the Big Data hype is becoming competitive advantage. This report is based on an online survey conducted between December 2012 and January 2013 and telephone interviews conducted in February A total of 36 business and IT executives and managers among 33 financial institutions were contacted for the report. Large institutions (banks with >$50 billion in assets, insurers with >$5 billion direct written premium) comprised about half the respondent base. Celent found it difficult to recruit respondents for this research. Commonly, financial institution contacts would reflect significant interest in the topic, but concede they didn t know who within their organization we should speak with. This suggests that Big Data expertise currently resides with comparatively few individuals. And, where it resides, it is as likely to be knowledgeable at the business unit level as the enterprise. Big Data is a term originally associated with dealing with Web or Internet scale data sets such as those faced by Google, Facebook and others. Today that definition has been broadened to not only cover volume but also velocity or speed and variety, distinguishing well-structured data from unstructured data. Celent defines Big Data as the scale of data in one or more of these dimensions with which companies current technology solutions are found lacking. In the survey, respondents answered based on their own understanding of Big Data and their own perception of maturity. The report begins with a review of how North American financial institutions understand and value Big Data and data analytics. This includes where banks and insurers expect to see activity and how the investment in Big Data will show up. The next section explores the level of experience financial institutions have with Big Data, the technologies employed, and results achieved. Then, the report examines how Big Data is impacting organization, culture, and decision-making in financial industry firms. The report concludes with a discussion of the barriers to success: both perceived and real. 2

5 Chapter: Big Data Understanding and Value BIG DATA UNDERSTANDING AND VALUE Like beauty, Big Data is in the eye of the beholder. In Celent s view, three rapidly evolving dimensions in modern financial services data environments most usefully warrant the distinction of being called Big Data: volume, velocity, and variety. Companies face unique challenges among any of the Three V s, as shown in Table 1. Table 1: The Three V s of Big Data DIMENSION DEFINITION CHALLENGE Volume Velocity Variety Source: Celent The amount of information gathered is often in terabytes, sometimes in petabytes, and will soon be in exabytes. The speed at which data is collected, analyzed, and presented to users. Data can take many forms and be gathered from many devices and from internal and external systems and sources, including social media. Digital information to process is exceeding the current data mastery investment in most financial firms. High-speed data flows and response expectations of end users. Growth in the number of data types available, especially with unstructured data. Each dimension in Table 1 is relative, as is the challenge associated with managing them. The difficulty of the problem increases as the scale along any of the three dimensions increases. Technology solutions need to deal efficiently with data that is massive, fast, or of varying structure. Therefore, Celent defines Big Data as the scale of data in one or more of these dimensions with which companies current technology solutions are found lacking. Financial services firms face practical and compelling reasons to embrace the rapid growth in each of these three dimensions, but few have done so. This definition of Big Data is shared by a growing number of parties, but is not embraced industry wide. This lack of clarity came across loud and clear in the survey. Said simply, Big Data remains trendy and ill-defined. This is reflected by the broad alignment of Big Data to multiple, unrelated attributes, rather than close alignment to the Three V s (see Figure 2 on Page 4). For example: Association with semistructured and unstructured data (74%) is not surprising and aligns with most common definitions of Big Data. Association with large data volumes (beyond the organization s current ability to accommodate with traditional relational databases) was less common (59%), which may be a surprise since the big in Big Data implies high volume. Gratifyingly, few (27%) associated the term Big Data with social media data. Social media analysis is a fine example of high-volume, semi-structured data analysis, but should not in itself define Big Data. Obviously, not everyone shares this opinion. Figure 2 shows predictive analytics closely tied to the definition of Big Data (68%). This is somewhat curious, when predictive analytics has been somewhat widely practiced across multiple industries long before the term Big Data came into prominence. This close association of predictive analytics suggests that, to many, Big Data is about actionable 3

6 Chapter: Big Data Understanding and Value insight and altering decision-making rather than rearward-looking business intelligence that has been the domain of analytics to a large extent. Figure 2: Financial Institutions Don t Embrace a Singular Definition of Big Data Data that is semi-structured or unstructured Predictive analytics/modeling Definitions of Big Data 68% 74% Large volumes of data that can't be accommodated with traditional RDMS Data volume that is beyond our organization s current ability to analyze Data that requires real-time analysis 35% 41% 59% Social media data 27% 0% 40% 60% 80% Resp. (%) Q: How does your organization define Big Data? Select up to three (3) from among the following. As a group, financial institutions that assert experience with one or more Big Data projects share a somewhat different view of Big Data compared to financial institutions with no such experience. This shows up in a number of ways, beginning with what is meant by Big Data in the first place. Firms with experience are more likely to associate Big Data with predictive analytics and real-time analysis, but are less likely to associate Big Data with social media data (see Figure 3). Here variety and volume are still king, but velocity is gains in importance for experienced institutions. Figure 3 Experience with Big Data Appears to Color One s Perception Definitions of Big Data Data that is semi-structured or unstructured 75% 67% Predictive analytics/modeling 55% 80% Large volumes of data that can't be accommodated with Data volume that is beyond our organization s current ability to Data that requires real-time analysis 33% 60% 60% 50% 53% No Big Data Exp. Big Data Exp. Social media data 13% 40% 0% 40% 60% 80% 100% Resp. (%) Q: How does your organization define Big Data? Select up to three (3) from among the following. Value Despite the varying understanding of Big Data, North American financial institutions think highly of it. In Figure 4 on Page 5: 4

7 Chapter: Big Data Understanding and Value Nearly all respondents (90%) think skillful use of Big Data will define the future winners in financial services. However, about half of respondents somewhat or completely agreed with the idea that the promise of Big Data is overblown. Banks, more so than insurers, felt that Big Data is merely a new term for what they had already been doing (58%) and that Big Data is too poorly defined to be useful (42%). In Celent s opinion, this apparent contradiction is explained by how closely Big Data is associated with analytics. In other words, a number of banks and insurers do feel Big Data is overhyped, even as they place high value in data and data analytics. A number of large banks, for example, have deployed customer profitability and next best product analytics applications long before Big Data appeared in the headlines. It is no wonder, then, that some of these banks would treat the current Big Data hysteria with a degree of skepticism. Similarly, several banks interviewed for this report took issue with the term Big Data while justifying significant activity within the bank to develop competencies in its many facets. Insurers interviewed generally didn t identify with the Big in Big Data, citing that volume was not their challenge but Big Data technologies were enabling them to address variety and velocity issues. Insurers felt that the promise was overblown but that the technologies allowed them to do things with the data they couldn t do otherwise, the latter driving their view on skillful use of Big Data. Figure 4: Financial Institutions Think Highly of Big Data Attitudes Toward Big Data Big Data is too poorly-defined to be useful 10% 42% Big Data is a trendy term for what we ve been doing 30% 58% Insurers Banks The promise of Big Data is overblown 53% 60% Skilful use of Big Data will define future winners in financial services 90% 90% 0% 40% 60% 80% 100% % somewhat or completely agree How do you regard Big Data? Indicate your agreement or disagreement to the following statements. 100% of institutions with Big Data experience show themselves to be believers in the value of skillful use of data for competitive advantage, compared to 85% among firms with no practical experience. Clearly the early-movers have so distinguished themselves based on this confidence. But, the narrow margin between implementers and nonimplementers suggests that mere attitudinal differences don t separate early-movers from the rest of the market. Other factors are at work and will be explored later. In a separate question, respondents were asked to opine on the extent to which the use of information creates a competitive advantage for their institution. How good at data are you, in other words. Although far from uniform in their assessment, financial institutions embrace information as a competitive advantage by a 4:1 margin versus the detractors; 15% of respondents associated information as less than a moderate competitive 5

8 Resp. (%) Chapter: Big Data Understanding and Value Resp. (%) advantage (1 or 2 on a five-point scale). Banks were a bit more bullish than insurers with responses averaging 3.7 vs. 3.1 on the same five-point scale (see Figure 5). In interviews, Celent observed that respondents assessments were colored primarily by their personal experience, rather than their institution overall. Figure 5: Most Financial Institutions Embrace the Value of Information 50% Extent to which information creates a competitive advantage 46% 40% 30% 23% 17% 10% 3% 11% 0% Competitive Advantage through data Small Moderate Large Q: To what extent does the use of information create a competitive advantage for your institution? Firms with Big Data experience are significantly more bullish about their ability to derive competitive advantage from the use of information (3.9 versus 3.4), but not overwhelmingly so (see Figure 6). This is another indication that there is more to being a data-driven organization than implementing the latest Big Data technology. Figure 6 Big Data Firms are More Bullish about their Use of Data for Competitive Advantage 60% Extent to which information creates a competitive advantage 50% 40% 33% 40% 50% 30% 10% 0% 27% 15% 10% 5% 0% 0% Small Moderate Large Big Data Experience No Big Data Experience Q: To what extent does the use of information create a competitive advantage for your institution? But information is a broad topic. The next section explores where banks and insurers think the value of information lies. 6

9 Chapter: Big Data Understanding and Value WHERE THE PERCEIVED VALUE LIES All respondents were asked to comment where they had or expected to have Big Data projects. All respondents had an opinion on the matter, even though comparatively few of them had live implementations. Insurers and banks responded rather differently to this question. Significantly more banks associated Big Data with risk or fraud initiatives (95% vs. 60%). In contrast, significantly more insurers (70% vs. 35%) associated Big Data with research initiatives and offered other areas of interest including actuarial, reinsurance risk, and product development. Insurers seem intent on using Big Data solutions to research and develop new products. Banks and insurers answered similarly in closely associating Big Data with marketing and customer experience initiatives (see Figure 7). Figure 7: Banks and Insurers Have Different Views on Applications of Big Data Areas of Big Data Interest or Activity Risk/fraud 60% 95% Marketing 80% 75% Customer experience 60% 75% Insurers Research 35% 70% Banks Operations 30% 45% 0% 40% 60% 80% 100% Resp. (%) Q: In what areas do you have or expect to have Big Data projects? Select all that apply. These expectations seemed to be influenced more by demand from lines of business rather than perceptions cultivated by vendors or industry publications. In several cases, responses directly reflected areas of existing or planned activity. Among financial institutions with hands-on Big Data experience, expectations for sustainable advantage resulting from Big Data skew significantly towards risk management and improved sales results leading to revenue or margin growth. Banks and insurers answered similarly, except for compliance management, where more banks expect to derive benefits from Big Data (see Figure 8 on Page 8). Large credit card issuers, for example, have employed data analytics to support risk and marketing objectives. On the risk side, transaction analytics has been used to predict default and reduce charge-offs. On the marketing side, analytics has been used to target customers, establish relationship pricing, and improve campaign effectiveness with great success. Each of these examples can be viewed through the line of business lens, which is where Celent observes most Big Data projects to initially gain traction. 7

10 Chapter: Big Data Understanding and Value Figure 8 Sales and Risk Applications Dominate Big Data Benefits Better risk management Big Data Expectations Improved sales results (revenue/margin growth) Improved customer experience/retention Improved operational efficiency Insurers Banks Improved compliance management 0% 40% 60% 80% 100% Resp. (% of FIs with experience) Q. Based on your experience thus far, in which areas do you expect to derive the most benefits from the successful application of Big Data to your business over time? Vendor Perceptions Respondents identified vendors that were viewed as leaders in the Big Data environment. Among survey respondents, a small cadre of solution providers competes as perceived leaders in the Big Data space. Among them, IBM was most often cited as being a leader, both as a hardware and software vendor. Interestingly, Hadoop was mentioned repeatedly in the survey and post-survey interviews, reflecting both awareness and usage by financial institutions, so it s no surprise that software vendors supporting Hadoop such as IBM, Cloudera, Hortonworks, and MapR are mentioned (see Table 2). Interestingly, vendor leadership perceptions do not correlate to usage among surveyed financial institutions. Among those with Big Data initiatives in place, the most common investment has been in scalable data storage infrastructures (58% of respondents see Figure 12 on Page 11) and Microsoft SQL (76% of respondents see Figure 13 on page 12). Hadoop is in use among comparatively fewer institutions (48%). Table 2: Leadership Perceptions Were Enjoyed by Relatively Few Vendors PERCEIVED LEADERS SOFTWARE HARDWARE Vendors Listed in Order of Mention IBM Cloudera Hortonworks MapR SAS SAP Oracle Tableau *Axiom *Lucene *Information Builders IBM EMC HP Oracle Teradata Dell SuperMicro NetApp Q: Which software vendors below do you perceive to be leaders? Please indicate your perception whether you have experience with the vendors or not. * These vendors received a single mention This small list of vendors is further evidence of the novice state of Big Data in financial services. But, having an understanding of and opinion about Big Data is no substitute for experience. The next section examines the state of Big Data implementation experience among large North American financial institutions. 8

11 Chapter: Project Experience: Where s the Beef? PROJECT EXPERIENCE: WHERE S THE BEEF? A small minority of North American financial institutions have hands-on experience with Big Data projects. Those that do, are disproportionally larger institutions, but even then are a small minority. In Celent s survey sample, roughly a fourth of banks and just one in five insurers have one or more Big Data implementations in production anywhere in the enterprise. A similar percentage has formative initiatives that are not yet in production (see Figure 9). Not surprisingly, there is a strong correlation between firms understanding of Big Data and their activity. Institutions that associate information and Big Data analytics with competitive advantage tend to have a head start on their peers in terms of hands-on Big Data experience (Figure 6 on Page 6). Figure 9: A Broad Spectrum of Big Data Experience Big Data Experience Banks 24% 24% 5% 29% 19% In production Piloting Planning an initiative Insurers Considering or researching Not yet begun 0% 40% 60% 80% 100% Resp. (%) Q: Which option below best matches your companies' Big Data experience? Based on Figure 9, it appears that North American banks and insurers enjoy a vaguely similar level of experience with Big Data, but not exactly. While a similar percentage of respondents assert experience with Big Data, A handful of North American banks enjoy a considerable head start in terms of their longevity of experience (Figure 10 on page 10). Among insurers with Big Data implementations, three-quarters of them have less than a year of experience. By comparison, nearly 40% of responding banks with Big Data experience have been experimenting for two or more years. This is key. One defining characteristic of Big Data is that there is no such thing (yet) as a shrink-wrapped vendor solution. By definition then, any Big Data effort no matter how narrow the scope requires a significant amount of trial and effort. Based on telephone interviews among firms with live implementations, one year is hardly enough time to fine-tune an implementation to the point of considering it optimized. 9

12 Chapter: Project Experience: Where s the Beef? Figure 10: More Banks Have Longevity of Experience Longevity of Big Data Experience More than 3 years 2 to 3 years 1 to 2 years Insurers Banks 6 months to 1 year Less than 6 months 0% 40% 60% 80% Resp. (% of FIs with experience) Q: How long has your organization been actively investing in and implementing Big Data projects? Such objective measures, however, are not necessarily a good gauge of where institutions think they are or wish they were in terms of capability or maturity. North American insurers appear to be slightly further along the Big Data maturity continuum than banks, according to their own assessment and despite a minority of banks having a bit of a head start. Insurers prior investment in actuarial tools and specific implementations around telematics products may be an influence in this self-perception. Figure 11: Insurers Offer a More Bullish Self-Assessment on Big Data Maturity Big Data Maturity Level Banks 38% 25% 25% 13% Exploring Experimenting Deploying Insurers 25% 25% 25% 25% Expanding 0% 40% 60% 80% 100% Resp. (%) Q: Which selection below best describes your institution s overall maturity with Big Data? In terms of platform components and appliances, banks have invested more heavily than have insurers. Key targets for banks have been scalable data storage solutions, data 10

13 Chapter: Project Experience: Where s the Beef? security, and governance as well as analytics appliances. Insurers too have invested in data storage infrastructure and analytics appliances but aren t investing as heavily in components and appliances aimed at data security and governance. This may be reflected in greater comparative investment in software, and insurers avoidance of public cloud infrastructures. The apparent need to scale data solutions (58% of respondents) is at odds with the earlier definition of Big Data regarding volume of data. Perhaps scaling data storage is seen as a non-big Data activity as well. Figure 12: Data and Data Storage Appear to Be the Most Popular Big Data Investment Big Data Platforms and Appliance Utilization Scalable data storage infrastructure 58% Analytics appliance/accelerators 54% Data security and governance 46% Scripting and development tools 42% Workload optimization 25% Complex event processing (CEP) 8% 0% 40% 60% Resp. (% of FIs with experience) Q: Which platform components or appliances are either currently in pilot or installed to support your organization s Big Data efforts? Select all that apply. In keeping with insurers recency of experience, they appear to be using a bit more modern technology compared to North American banks (see Figure 13 on page 12). Despite the relative novelty of insurers to Big Data compared with the North American banks, insurers have been quick to adopt Hadoop style infrastructures. They prefer to build out that infrastructure internally and not follow the few banks exploring public cloud solutions. The relative adoption of Hadoop, visualization software, multiple parallel processing, and private cloud suggests that these technologies are often built out together at a financial institution. Clearly financial institutions are working to make their investment in SQL-based solutions scale rather than follow the trend route of NoSQL-based databases. While these alternative data stores have proved popular with social network organizations, and in biomedical applications it seems that financial institutions don t have the same requirements, perhaps preferring to work with incumbent suppliers on their data storage needs. The use of data storage infrastructure and analytics appliances by North American banks may point to a preferred, hardware-based alternative approach. 11

14 Chapter: Project Experience: Where s the Beef? Figure 13: Adoption of Hadoop Draws in Adoption of Supporting Technologies Big Data Technology Utilization SQL Hadoop Visualization Multiple parallel processing Private Cloud Insurers Banks NoSQL Public Cloud 0% 40% 60% Resp. (% of FIs with experience) Which of the following technologies is your institution utilizing? Select any that apply. Particularly interesting is the comparatively higher use of private cloud among insurers roughly twice that of banks among the survey s relatively small sample. Those that employ cloud-based solutions do so for well-defined reasons. Namely, capacity and cost savings (see Figure 14). Figure 14: Cloud Users Primarily Do So for Scalable Capacity Rationale for Cloud Based Solutions Scalable capacity Potential cost savings Systems redundancy (disaster recovery, business continuance) Shortage of skilled staff in house 0% 40% 60% Resp. (% of FIs with experience) Q: If your institution is using cloud-based solutions to support your Big Data objective, what are the key drivers for using cloud computing? Select any that apply. With few financial institutions having significant experience with Big Data, it should be no surprise to see third parties involved in both influencing and executing strategy. Systems integrators and pure-play consulting firms have been involved in a minority of institutions. 12

15 Chapter: Project Experience: Where s the Beef? This suggests that solution providers, particularly software firms, have earned their reputation as the go-to experts on Big Data matters (see Figure 15). It is still a surprise to see such heavy reliance on internal staff in these projects, perhaps supporting the view that effective deployment of Big Data is seen as a differentiator. More likely, financial services firms insist on owning the analysis of their data and being directly involved, with the assistance of third parties as a way to build centers of excellence. Figure 15: Software Vendor Influence Is Significant Resources Employed for Big Data Projects Internal External - Software vendors External - System Integrators Execute Influence External - Other Consulting Services 0% 40% 60% Resp. (% of FIs with experience) Q: What resources have helped influence your Big Data strategy? Q: What resources have helped execute your Big Data strategy? Thus far, we have seen a diversity of definitions and attitude toward Big Data, with earlyadopting firms more bullish about their ability to use data and analytics for competitive advantage. We have seen that most hands-on experience with Big Data has been recent, with banks generally having more longevity of experience and insurers enjoying experience with comparatively more modern technology. But to what end has this Big Data experience led? Has Big Data delivered? HAS BIG DATA DELIVERED? In the minds of nearly half of North American financial institutions with hands-on Big Data experience, it is too early to tell whether investments will meet expectations. Only one in five implementations among surveyed institutions delivered business case expectations, and an equal number over-delivered (see Figure 16 on Page 14). Conversely, excluding those too early to tell suggests the majority of Big Data implementations far along enough to assess have delivered or over-delivered expectations exceptional in Celent s experience when compared to the broader experience base of technology deployments. There are several ways to interpret this data. One interpretation suggests that Big Data business case assumptions are imprecise. That may indeed be the case, and would be logical given the nascent state of Big Data. Several banks and insurers interviewed for this report commented on the lack of available case studies and use cases. Moreover, learning how to become more data-driven takes more than a well-implemented technology project. There are cultural, process, and organizational implications that are both hard to embrace and highly uncertain as part of a business case discussion. These impacts are discussed in the next section. 13

16 Chapter: Project Experience: Where s the Beef? Figure 16: Half of Firms with Hands on Experience Don t Yet Have a Read on Big Data Efficacy Big Data Results vs. Expectations Too early to tell 48% Overdelivered Met Undelivered 12% 0% 40% 60% Resp. (% of FIs with experience) Q: To what extent has your investment in Big Data produced results consistent with your business case expectations? On the other hand, the large percentage of implementations that are too early to tell may simply be a reflection of their fledgling status. Arguably, such would likely be the case for any significant implementation under way for just a few months. And, with Big Data benefits requiring some degree of experimentation or test and learn iterations, the too early to tell status of these initiatives is not surprising. This appears to be the case (see Figure 17). Through this lens, 70% of Big Data projects underway for a year or longer have met or exceeded business case expectations. Impressive indeed! Figure 17: Big Data Projects with Longevity Have Met or Exceeded Business Case Expectations Big Data Results vs. Expectations Too early to tell 69% Overdelivered 13% 30% Met 6% 40% <1 year Exp. >1 year Exp. Undelivered 13% 10% 0% 40% 60% 80% Resp. (% of FIs with experience) Q: To what extent has your investment in Big Data produced results consistent with your business case expectations? 14

17 Chapter: Project Experience: Where s the Beef? Like the early days of CRM, success or failure of emerging technology initiatives is not merely a function of technical prowess. In Celent s experience, many large financial services firms regard their own early CRM experience as an expensive and timeconsuming IT project whose returns have never justified the build/maintenance costs. Is such a revelation an indictment of CRM? Clearly not! In Celent s view, the root cause of many poorly performing CRM initiatives relates to a design flaw in the original implementation. Many of these projects were driven by IT with limited business input. As a result, the operating model changes enabled by technology investments do not materialize. This is not a technology problem, but a cultural one. Big Data could see a similar fate if financial institutions are not careful. Big Data is more challenging than some of the technology adoptions of the past since an institution cannot simply pick up the tool and start using it. Instead the data on which to apply these tools and disciplines must exist. More so than most other technology projects, without change to the operating model to the attitude to data these initiatives will likely fail. Among early-adopters, however, a commendable number of initiatives appear to be off to a roaring start. The next section looks at the cultural impact Big Data is having (or not). 15

18 Chapter: Cultural Impact CULTURAL IMPACT As mentioned earlier, Big Data is part of a larger movement toward evolving organizations to be more data-driven. This is a big topic and is beyond the scope of this report. Excellent resources on the topic include Analytics at Work and Competing on Analytics by Thomas H. Davenport. Most financial institutions have long surpassed the culture of heroics for driving change in the organization and are instead adopting processes in a disciplined manner. Some are monitoring and optimizing these at a local level internally and making use of strong business cases. Anecdotally, most financial services organizations are struggling in gathering data about their customers and reaction to their products; those that do gather this data struggle to make sense of it. For most, being consistently data-driven and making decisions based on evidence is deemed a lofty and difficult goal. What does it mean to be a data-driven company? It means to cultivate the culture and practice of making decisions and improving performance in key business domains using data and analysis, instead of experience and intuition. Doing so to any meaningful extent is a big task that a minority of firms have undertaken. Being data-driven doesn t mean implementing Big Data projects, but fully leveraging Big Data requires a datadriven culture. What are some examples of highly data-driven companies? Respondents were asked to cite up to three companies (beyond Amazon, Google, and Netflix - common examples of firms known to compete on analytics) which in their opinion are highly data-driven. Then, respondents were asked to cite firms from within the industry they felt were data-driven. Citations were few and far between (see Table 3). Some of the firms cited have relatively well-known Big Data capabilities, such as Capital One s rewards card programs, Progressive s pricing strategy and recent telematics products and Toyota s continual improvement initiatives. In telephone interviews, respondents typically justified their citations based on one or two technical achievements, not a highly-differentiated culture with a test and learn approach to everything. Table 3 Respondents Cited Few Data-Driven Companies FIRMS PERCEIVED TO BE DATA-DRIVEN REGARDLESS OF INDUSTRY BANKS INSURERS LISTED IN ORDER OF TIMES MENTIONED Wal-Mart Microsoft Apple American Express Toyota ebay Facebook Progressive Giant Eagle Citi Chase Wells Fargo Bank of America Capital One Progressive Travelers USAA GEICO Liberty Q: List three companies beyond Amazon, Google and Netflix that in your opinion are highly data-driven. Q: Within your industry, are there any institutions you feel are highly data-driven? When asked for a self-assessment, most respondents both banks and insurers rated their own firms just moderately data-driven. Less than a third of respondents considered their firms highly data-driven (rated 4 or 5 on a five-point scale). In interviews, respondents were quick to cite a number of reasons for their sober assessments, such as: lack of an enterprisewide data model or data governance, siloed data sources and 16

19 Chapter: Cultural Impact Resp. (%) Resp. (%) channel platforms, poorly utilized internal sources of customer data, and so on. The nature of their assessments and the examples offered were pragmatic. The status quo among most North American financial services firms is decidedly not as data-driven as it could be. Data abounds, but it is not systematically used to its fullest advantage. This is both a cultural and technical challenge. Improving on the status quo will require senior leadership at the enterprise level something not evident thus far in most financial institutions. Figure 18: Financial Institutions Have a Sober Estimate of Themselves 70% Self-assessment: How data-driven is your firm? 60% 58% 50% 40% 30% 10% 0% 19% 12% 12% 0% Not at all Moderately Highly Q: To what extent to you consider your own institution data-driven? To a modest extent, firms with Big Data experience consider themselves more datadriven (3.6 versus 3.0 on a five point scale), with of experienced firms considering themselves highly data-driven (see Figure 19). Figure 19: More Data-Driven Firms Have Big Data Experience Self-assessment: How data-driven is your firm? 70% 60% 50% 53% 67% 40% 30% 17% 17% Big Data Exp. No Big Data Exp. 10% 0% 7% 0% 0% 0% Not at all Moderately Highly Q: To what extent to you consider your own institution data-driven? 17

20 Chapter: Cultural Impact A related question asked respondents to indicate how much Big Data has or will change how their firms operate; how much technology will influence culture. Responses ranged from 1 (no change) to 5 (change greatly). To a large extent, these responses reflect sentiment, not experience, since less than 25% of responding financial institutions have Big Data initiatives in production, and most of those are not enterprisewide initiatives. Figure 20 shows the percentage of respondents who responded with a 4 or 5 to each of the change attributes. Most respondents (62%) felt Big Data will affect the speed of decision-making, and a nearly equal number felt Big Data has/will make their firm open to new ideas. Organizational changes were deemed likely by a small majority of respondents (54%), while half felt Big Data has/would affect the degree of innovation at their institution. Of note is the area of least impact, the centralization of data accountability and responsibility. From discussions with survey respondents, it is common to align ownership and governance of data with the owning business unit, rather than move to a central model. But a central model of data governance is where most firms know they need to get. Figure 20: Most Feel Big Data Has/Will Affect Their Institution in Significant Ways Big Data Influence on Company Culture Speed of decision making 62% Openness to new ideas 58% Organization/role changes 54% Willingness to experiment with new ideas 52% Degree of innovation 50% Centralization of data accountability/responsibility 42% 0% 10% 30% 40% 50% 60% 70% Resp. (% top 2-box) Q: For each of the following, please indicate how much Big Data has/will change how your company operates. Comparing Big Data early-movers to financial service firms with no hands-on experience shows a marked difference in cultural expectations (see Figure 21 on Page 19). Earlymover firms by a large margin have the expectation that Big Data has or will: Increase the speed of decision making (67% to 50%) Create openness to new ideas (80% to 25%) Create a willingness to experiment with new ideas (60% to 36%) Enable a greater degree of innovation (60% to 33%) It is not clear whether these stark differences are a result of going-in expectations or are the result of experience. 18

21 Chapter: Cultural Impact Resp. (%) Figure 21: Big Data Early-Movers Embrace Different Cultural Expectations Big Data Influence on Culture Speed of decision making 50% 67% Openness to new ideas 25% 80% Organization/role changes Willingness to experiment with new ideas 36% 47% 58% 60% No Big Data Exp. Big Data Exp. Degree of innovation 33% 60% Centralization of data accountability/responsibility 33% 50% 0% 40% 60% 80% Resp. (% top 2-box) Q: For each of the following, please indicate how much Big Data has/will change how your company operates. Regardless of Big Data experience, however, North American financial institutions sense a tension between where they are and where they should be. This tension is clear when two questions are graphed together: To what extent does the use of information create a competitive advantage for your institution? and How data-driven is your company? (See Figure 22). The mismatch between the status quo and achieving competitive advantage through the use of data and data analytics suggests there are barriers to success. The next section looks into these barriers. Figure 22: Tension Between Where Firms Are and Where They Need to Be 70% Extent to which information creates a competitive advantage 60% 58% 50% 47% 40% 30% 10% 0% 24% 19% 15% 12% 12% 12% 3% 0% Small Moderate Large Extent to which BD/analytics create competitive advantage How data-driven is your company? Source: Celent survey of financial institutions, January 2013, n=35 Q: To what extent does the use of information create a competitive advantage for your institution? Q: How data-driven is your company? 19

22 Chapter: Barriers to Success BARRIERS TO SUCCESS Celent identified a variety of likely barriers to Big Data success and invited survey respondents with some experience with Big Data to rate each obstacle based on their own experience on a 1 (small) to 5 (large) scale. Not all respondents have the same degree of experience. Some are planning projects; others have one or more pilot projects; and others are in production and may be expanding the scope of their Big Data investments. In any institution a barrier might keep a project from progressing altogether. Others may show up in lackluster results. Fully half of respondents cited lack of analytical talent as a key barrier to success with Big Data (see Figure 23). This is not surprising given the rapid development in data analytics, see Figure 18. Note, however, the comparatively high ratings among barriers that are not unique to Big Data. Specifically, data privacy (43%), lack of business sponsorship (39%), and lack of a clearly defined business case (39%). In interviews, lack of access to appropriate data referred in every case to internal data and difficulties associated with getting it across channel or line of business silos, or formatting it as needed for use. These aren t unique to Big Data, but they are legitimate barriers and help to explain why so many Big Data initiatives are narrow in scope. Figure 23: Barriers to Big Data Success Are Many Big Data Barriers to Success Lack of analytical talent data scientists, quants, etc. Lack of understanding of Big Data solutions Concerns about data privacy Lack of technical expertise within the organization 43% 43% 43% 50% Lack of business sponsorship Lack of clearly defined business use case Lack of access to appropriate data Lack of access to appropriate technology 13% 39% 39% 38% 0% 10% 30% 40% 50% 60% Resp. (% top 2-box) Q: What obstacles has your organization encountered that limited the success or business gain realized by your Big Data projects? It is interesting to compare the barriers to success cited among firms with some tangible Big Data experience with firms not having experience as yet. The latter firms may have not yet begun looking into Big Data in a meaningful way, whereas others may be actively considering or researching Big Data. As a group, these non-implementers have a lesser view of the importance of Big Data as well as its ability to help their firm reach its business objectives (2.8 on a 5-point scale, see Figure 24 on Page 21). 20

23 Chapter: Barriers to Success It is further interesting to note that many of those financial institutions with no experience cited lack of technical expertise as a key issue perhaps lack of access to these skills stops the adoption of Big Data and data scientists are the next key resource thereafter. Figure 24: Sharp Differences Based on Having Experience with Big Data Big Data Barriers to Success Lack of analytical talent data scientists, quants, etc. Lack of understanding of Big Data solutions Concerns about data privacy Lack of technical expertise within the organization Lack of business sponsorship Lack of clearly defined business use case Lack of access to appropriate data Lack of access to appropriate technology 13% 39% 39% 38% 40% 40% 43% 43% 43% 50% 60% 60% 60% 60% 0% 40% 60% 80% Resp. (% top 2-box) 80% Experience No Experience Q: What obstacles has your organization encountered that limited the success or business gain realized by your Big Data projects? Almost all respondents had some form of data governance in place. There was a preference for a centralized approach, although this approach accounts for only half of the respondents with experience. Again, half of respondents describe their Big Data and analytics strategy as jointly owned. Most of the remaining FIs had Big Data and analytics owned entirely by IT, and only 4% had pure business ownership of data. Clearly the days of the all data being owned by the CIO and IT are numbered, and Celent welcomes the involvement of the business owners in data strategy. No FI surveyed had a Chief Science or Data Officer. Figure 25: Approaches to Data Governance and Leadership Q: Which of the following best describes your data governance? 21

24 Chapter: Going Forward GOING FORWARD The financial services industry has evolved from a money business to a money and information business (see Figure 26). This has occurred alongside an explosion of both the number and variety of ways in which customers interact with financial services firms as well as their expectations for doing so. In this context, the clarion call to leverage Big Data is not merely a selling proposition for hardware and software firms. It is a new reality brought about by a new normal in financial services. Figure 26: Financial Services Economics Are Changing in Favor of Data Source: Oliver Wyman, A Money and Information Business, State of the Financial Services Industry 2013 Yet, the status quo among most North American financial services firms is decidedly not as data-driven as it should be. Data abounds, but it is not systematically used to its fullest advantage. This invites a challenge that is both cultural and technical. Changing the status quo will require senior leadership at the enterprise level something not evident thus far in most financial institutions. More common are isolated and often uncoordinated line of business led initiatives that seek specific business outcomes within a traditional non-data driven culture using siloed data warehouses. This is precisely the context in which Celent observes most Big Data initiatives. While this approach is an excellent way to develop the technical capability to manage increasing data volumes, velocity, and variety, it will not produce the step changes in business outcomes that are so sorely needed in financial services. Big Data will not be particularly helpful without three key underpinnings. Specifically: 1. Top-down, enterprisewide cultural change that embraces the growing importance of information and its use in delivering customer and shareholder value. 2. Managing information flow through the organization, from its capture, through its transformation, storage, use, and ultimately its thoughtful and deliberate deletion. 3. Understanding of information, analytics, and their potential. Without education and understanding information will be lost and unanalyzed. With them in place, Big Data s impact will likely be big. 22

GOVERNANCE MOVES BIG DATA FROM HYPE TO CONFIDENCE

GOVERNANCE MOVES BIG DATA FROM HYPE TO CONFIDENCE GOVERNANCE MOVES BIG DATA FROM HYPE TO CONFIDENCE By Elliot King, Research Analyst Produced by Unisphere Research, a Division of Information Today, Inc. June 2014 Sponsored by 2 TABLE OF CONTENTS Introduction

More information

Big Data Analytics. Research Report Executive Summary. Assessing the Revolution in Big Data and Business Analytics. Sponsored by

Big Data Analytics. Research Report Executive Summary. Assessing the Revolution in Big Data and Business Analytics. Sponsored by Big Data Analytics Assessing the Revolution in Big Data and Business Analytics Research Report Executive Summary Sponsored by Copyright Ventana Research 2013 Do Not Redistribute Without Permission Sponsor

More information

Big Data Survey. Exclusive Research from

Big Data Survey. Exclusive Research from 2014 Big Data Survey Exclusive Research from EXECUTIVE SUMMARY Big Data Strides Forward Companies place a high priority on their ability to harness data in the service of better business decisions. For

More information

Wikibon Big Data Analytics Adoption Survey, 2014-2015 Frequency Analysis

Wikibon Big Data Analytics Adoption Survey, 2014-2015 Frequency Analysis Wikibon.com - http://wikibon.com Wikibon Big Data Analytics Adoption Survey, 2014-2015 Frequency Analysis by Jeff Kelly - 1 July 2014 http://wikibon.com/wikibon-big-data-analytics-adoption-survey-2014-2015-frequency-analysis/

More information

I D C T E C H N O L O G Y S P O T L I G H T

I D C T E C H N O L O G Y S P O T L I G H T I D C T E C H N O L O G Y S P O T L I G H T Capitalizing on the Future with Data Solutions December 2015 Adapted from IDC PeerScape: Practices for Ensuring a Successful Big Data and Analytics Project,

More information

Cherwell Software Software Audit Industry Report

Cherwell Software Software Audit Industry Report Cherwell Software Software Audit Industry Report Cherwell Software has released the findings of its 2013 industry report that benchmarks software audit activity, trends, experiences, and perceptions among

More information

Big Data Analytics. 10 Best Practice Recommendations. Assessing the Revolution in Big Data and Business Analytics. Sponsored by

Big Data Analytics. 10 Best Practice Recommendations. Assessing the Revolution in Big Data and Business Analytics. Sponsored by Big Data Analytics Assessing the Revolution in Big Data and Business Analytics 10 Best Practice Recommendations Sponsored by Copyright Ventana Research 2013 Do Not Redistribute Without Permission February

More information

Sales & Marketing Alignment Benchmarks, Insights & Advice

Sales & Marketing Alignment Benchmarks, Insights & Advice Benchmarking Report Sales & Marketing Alignment Benchmarks, Insights & Advice Statistically Significant Findings from 550 Survey Responses in June 2013 Sponsored By: 2013 Demand Metric Research Corporation.

More information

Exploring the Impact of Geographic Context On Business Processes. Research Report Executive Summary

Exploring the Impact of Geographic Context On Business Processes. Research Report Executive Summary Business Trends in Location Analytics Exploring the Impact of Geographic Context On Business Processes Research Report Executive Summary Copyright Ventana Research 2013 Do Not Redistribute Without Permission

More information

BUSINESS INTELLIGENCE MATURITY AND THE QUEST FOR BETTER PERFORMANCE

BUSINESS INTELLIGENCE MATURITY AND THE QUEST FOR BETTER PERFORMANCE WHITE PAPER BUSINESS INTELLIGENCE MATURITY AND THE QUEST FOR BETTER PERFORMANCE Why most organizations aren t realizing the full potential of BI and what successful organizations do differently Research

More information

Sales Compensation Management. Research Report Executive Summary. Improving the Impact of Pay and Incentives to Maximize Revenue.

Sales Compensation Management. Research Report Executive Summary. Improving the Impact of Pay and Incentives to Maximize Revenue. Sales Compensation Management Improving the Impact of Pay and Incentives to Maximize Revenue Research Report Executive Summary Sponsored by Copyright Ventana Research 2013 Do Not Redistribute Without Permission

More information

2012 Benchmark Study of Product Development and Management Practices

2012 Benchmark Study of Product Development and Management Practices 2012 Benchmark Study of Product Development and Management Practices 2012 Benchmark Study of Product Development and Management Practices Contents 1. Introduction 2 2. Participant Profile 3 3. Methodology

More information

2015 Social Media Marketing Trends

2015 Social Media Marketing Trends 2015 Social Media Marketing Trends A 2015 survey and report on social media marketing practices and software usage By Megan Headley Research Director, TrustRadius First Published May 2015 2015 TrustRadius.

More information

The Shadow IT Phenomenon

The Shadow IT Phenomenon The Shadow IT Phenomenon CIOs respond with internal service provider transformation IT DEPT A research paper from Logicalis based on a global study of CIO pressures and priorities In summary This report

More information

OPTIMUS SBR. Optimizing Results with Business Intelligence Governance CHOICE TOOLS. PRECISION AIM. BOLD ATTITUDE.

OPTIMUS SBR. Optimizing Results with Business Intelligence Governance CHOICE TOOLS. PRECISION AIM. BOLD ATTITUDE. OPTIMUS SBR CHOICE TOOLS. PRECISION AIM. BOLD ATTITUDE. Optimizing Results with Business Intelligence Governance This paper investigates the importance of establishing a robust Business Intelligence (BI)

More information

The Challenge of Big Data Benchmarking Large-Scale Data Management Insights from Benchmark Research

The Challenge of Big Data Benchmarking Large-Scale Data Management Insights from Benchmark Research Benchmarking Large-Scale Data Management Insights from Presentation Confidentiality Statement The materials in this presentation are protected under the confidential agreement and/or are copyrighted materials

More information

SOCIAL MEDIA AND BUSINESS INTELLIGENCE SURVEY RESULTS & ANALYSIS CONDUCTED JANUARY FEBRUARY 2012

SOCIAL MEDIA AND BUSINESS INTELLIGENCE SURVEY RESULTS & ANALYSIS CONDUCTED JANUARY FEBRUARY 2012 SOCIAL MEDIA AND BUSINESS INTELLIGENCE SURVEY RESULTS & ANALYSIS CONDUCTED JANUARY FEBRUARY 2012 By Peter J. Auditore Produced by Unisphere Research, a Division of Information Today, Inc. March 2012 Sponsored

More information

Build an effective data integration strategy to drive innovation

Build an effective data integration strategy to drive innovation IBM Software Thought Leadership White Paper September 2010 Build an effective data integration strategy to drive innovation Five questions business leaders must ask 2 Build an effective data integration

More information

Adoption, Approaches & Attitudes

Adoption, Approaches & Attitudes Adoption, Approaches & Attitudes The Future of Cloud Computing in the Public and Private Sectors A Global Cloud Computing Study Sponsored by JUNE 2011 TABLE OF CONTENTS Executive Summary... 1 Methodology

More information

The Evolution of Enterprise Social Intelligence

The Evolution of Enterprise Social Intelligence The Evolution of Enterprise Social Intelligence Why organizations must move beyond today s social media monitoring and social analytics to Social Intelligence- where social media data becomes actionable

More information

Delivering Customer Value Faster With Big Data Analytics

Delivering Customer Value Faster With Big Data Analytics Delivering Customer Value Faster With Big Data Analytics Tackle the challenges of Big Data and real-time analytics with a cloud-based Decision Management Ecosystem James Taylor CEO Customer data is more

More information

Predicting the future of predictive analytics. December 2013

Predicting the future of predictive analytics. December 2013 Predicting the future of predictive analytics December 2013 Executive Summary Organizations are now exploring the possibilities of using historical data to exploit growth opportunities The proliferation

More information

SEYMOUR SLOAN IDEAS THAT MATTER

SEYMOUR SLOAN IDEAS THAT MATTER SEYMOUR SLOAN IDEAS THAT MATTER The value of Big Data: How analytics differentiate winners A DATA DRIVEN FUTURE Big data is fast becoming the term keeping senior executives up at night. The promise of

More information

Five Core Principles of Successful Business Architecture. STA Group, LLC Revised: May 2013

Five Core Principles of Successful Business Architecture. STA Group, LLC Revised: May 2013 Five Core Principles of Successful Business Architecture STA Group, LLC Revised: May 2013 Executive Summary This whitepaper will provide readers with important principles and insights on business architecture

More information

BIG DATA IN THE FINANCIAL WORLD

BIG DATA IN THE FINANCIAL WORLD BIG DATA IN THE FINANCIAL WORLD Predictive and real-time analytics are essential to big data operation in the financial world Big Data Republic and Dell teamed up to survey financial organizations regarding

More information

BUILDING THE CASE FOR CLOUD: HOW BUSINESS FUNCTIONS IN UK MANUFACTURERS ARE DRIVING PUBLIC CLOUD ADOPTION

BUILDING THE CASE FOR CLOUD: HOW BUSINESS FUNCTIONS IN UK MANUFACTURERS ARE DRIVING PUBLIC CLOUD ADOPTION BUILDING THE CASE FOR CLOUD: HOW BUSINESS FUNCTIONS IN UK MANUFACTURERS ARE DRIVING PUBLIC CLOUD ADOPTION Industry Report Contents 2 4 6 Executive Summary Context for the Sector Key Findings 3 5 9 About

More information

How Big Is Big Data Adoption? Survey Results. Survey Results... 4. Big Data Company Strategy... 6

How Big Is Big Data Adoption? Survey Results. Survey Results... 4. Big Data Company Strategy... 6 Survey Results Table of Contents Survey Results... 4 Big Data Company Strategy... 6 Big Data Business Drivers and Benefits Received... 8 Big Data Integration... 10 Big Data Implementation Challenges...

More information

Cloud Computing. Exclusive Research from

Cloud Computing. Exclusive Research from 2014 Cloud Computing Exclusive Research from Cloud Computing Continues to Make Inroads Companies are expanding their use of cloud as they work through implementation and organizational challenges Cloud

More information

TOP 8 TRENDS FOR 2016 BIG DATA

TOP 8 TRENDS FOR 2016 BIG DATA The year 2015 was an important one in the world of big data. What used to be hype became the norm as more businesses realized that data, in all forms and sizes, is critical to making the best possible

More information

Hosting and cloud services both provide incremental and complementary benefits to the organization

Hosting and cloud services both provide incremental and complementary benefits to the organization 33 Yonge St., Suite 420, Toronto, Ontario Canada, M5E 1G4 W H I T E P A P E R I D C a n d T E L U S E n t e r p r i s e C l o u d S t u d y, 2 0 1 3 : C a p i t a l i z i n g on C l o u d ' s W i n d o

More information

Big Data and Data Analytics

Big Data and Data Analytics 2.0 Big Data and Data Analytics (Volume 18, Number 3) By Heather A. Smith James D. McKeen Sponsored by: Introduction At a time when organizations are just beginning to do the hard work of standardizing

More information

Understanding the Development and Use of Analytical Business Intelligence Applications

Understanding the Development and Use of Analytical Business Intelligence Applications Understanding the Development and Use of Analytical Business Intelligence Applications By Elliot King, Ph.D Professor of Communication Lattanze Center Loyola University Maryland Table of Contents Introduction...1

More information

Big Data Integration. Research Report Executive Summary. Challenges and Opportunities in Accessing and Using Today s Information.

Big Data Integration. Research Report Executive Summary. Challenges and Opportunities in Accessing and Using Today s Information. Big Data Integration Challenges and Opportunities in Accessing and Using Today s Information Research Report Executive Summary Sponsored by Copyright Ventana Research 2013 Do Not Redistribute Without Permission

More information

White. Paper. EMC Isilon: A Scalable Storage Platform for Big Data. April 2014

White. Paper. EMC Isilon: A Scalable Storage Platform for Big Data. April 2014 White Paper EMC Isilon: A Scalable Storage Platform for Big Data By Nik Rouda, Senior Analyst and Terri McClure, Senior Analyst April 2014 This ESG White Paper was commissioned by EMC Isilon and is distributed

More information

Customer Service Analytics: A New Strategy for Customer-centric Enterprises. A Verint Systems White Paper

Customer Service Analytics: A New Strategy for Customer-centric Enterprises. A Verint Systems White Paper Customer Service Analytics: A New Strategy for Customer-centric Enterprises A Verint Systems White Paper Table of Contents The Quest for Affordable, Superior Customer Service.....................................

More information

DATA VISUALIZATION: When Data Speaks Business PRODUCT ANALYSIS REPORT IBM COGNOS BUSINESS INTELLIGENCE. Technology Evaluation Centers

DATA VISUALIZATION: When Data Speaks Business PRODUCT ANALYSIS REPORT IBM COGNOS BUSINESS INTELLIGENCE. Technology Evaluation Centers PRODUCT ANALYSIS REPORT IBM COGNOS BUSINESS INTELLIGENCE DATA VISUALIZATION: When Data Speaks Business Jorge García, TEC Senior BI and Data Management Analyst Technology Evaluation Centers Contents About

More information

2011 Cloud Computing SURVEY. Exclusive Research from CIO magazine

2011 Cloud Computing SURVEY. Exclusive Research from CIO magazine 2011 Cloud Computing SURVEY Exclusive Research from CIO magazine EXECUTIVE SUMMARY Cloud computing has come a long way in the past few years, from experts disagreeing on what the term cloud computing meant

More information

Cloud computing insights from 110 implementation projects

Cloud computing insights from 110 implementation projects IBM Academy of Technology Thought Leadership White Paper October 2010 Cloud computing insights from 110 implementation projects IBM Academy of Technology Survey 2 Cloud computing insights from 110 implementation

More information

Big Data and Analytics Survey. Exclusive Research from

Big Data and Analytics Survey. Exclusive Research from 2015 Big Data and Analytics Survey Exclusive Research from 2015 Big Data & Analytics Survey Insights into Initiatives and Strategies Driving Data Investments Companies are still scrambling to manage ongoing

More information

The Promise and Performance of Enterprise Systems in Higher Education

The Promise and Performance of Enterprise Systems in Higher Education ECAR Respondent Summary October 2002 Respondent Summary The Promise and Performance of Enterprise Systems in Higher Education Paula King Enterprise system implementations are among the single largest investments

More information

A financial software company

A financial software company A financial software company Projecting USD10 million revenue lift with the IBM Netezza data warehouse appliance Overview The need A financial software company sought to analyze customer engagements to

More information

The 3 questions to ask yourself about BIG DATA

The 3 questions to ask yourself about BIG DATA The 3 questions to ask yourself about BIG DATA Do you have a big data problem? Companies looking to tackle big data problems are embarking on a journey that is full of hype, buzz, confusion, and misinformation.

More information

The Next Wave of Data Management. Is Big Data The New Normal?

The Next Wave of Data Management. Is Big Data The New Normal? The Next Wave of Data Management Is Big Data The New Normal? Table of Contents Introduction 3 Separating Reality and Hype 3 Why Are Firms Making IT Investments In Big Data? 4 Trends In Data Management

More information

Next-Generation Predictive Analytics. Research Report Executive Summary. Using Forward-Looking Insights to Gain Competitive Advantage.

Next-Generation Predictive Analytics. Research Report Executive Summary. Using Forward-Looking Insights to Gain Competitive Advantage. Next-Generation Predictive Analytics Using Forward-Looking Insights to Gain Competitive Advantage Research Report Executive Summary Sponsored by Copyright Ventana Research 2013 Do Not Redistribute Without

More information

Workforce Analytics Enable Smarter Decisions

Workforce Analytics Enable Smarter Decisions Ventana Research: Workforce Analytics Enable Smarter Decisions Workforce Analytics Enable Smarter Decisions Finding the Right Tool for Human Capital Management White Paper Sponsored by 1 Ventana Research

More information

IBM i WORKPLACE AND DATABASE PRACTICES SURVEY

IBM i WORKPLACE AND DATABASE PRACTICES SURVEY IBM i WORKPLACE AND DATABASE PRACTICES SURVEY December 2014 New Generation Software, Inc. 3835 North Freeway Blvd., Suite 200 Sacramento, CA 95834 New Generation Software, Inc. (NGS) has taken reasonable

More information

Incorporating Social Media into a Technical Content Strategy White Paper

Incorporating Social Media into a Technical Content Strategy White Paper Incorporating Social Media into a Technical Content Strategy White Paper Authored by Bill Gearhart, Comtech Services, Inc. USER-GENERATED CONTENT Table of Contents Table of Contents Introduction...2 Selected

More information

What s Trending in Analytics for the Consumer Packaged Goods Industry?

What s Trending in Analytics for the Consumer Packaged Goods Industry? What s Trending in Analytics for the Consumer Packaged Goods Industry? The 2014 Accenture CPG Analytics European Survey Shows How Executives Are Using Analytics, and Where They Expect to Get the Most Value

More information

CFO Insights How CFOs Can Own Analytics

CFO Insights How CFOs Can Own Analytics CFO Insights How CFOs Can Own Analytics Much has been made about the unprecedented quantities of data companies collect these days, from their own operations, supply chains, production processes, and customer

More information

Key Drivers of Analytical Maturity Among Healthcare Providers

Key Drivers of Analytical Maturity Among Healthcare Providers Key Drivers of Analytical Maturity Among Healthcare Providers January, 2015 Written by: International Institute for Analytics Sponsored by: Executive Summary Firms of all shapes and sizes today are confronted

More information

Market Pulse Research: Big Data Storage & Analytics

Market Pulse Research: Big Data Storage & Analytics Market Pulse Research: Big Data Storage & Analytics MARKETING RESEARCH EMPLOYEE ENGAGEMENT A WORLD OF INSIGHTS January 2015 Presented on behalf of HP & Microsoft METHODOLOGY & RESEARCH OBJECTIVES Sample

More information

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

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

More information

Data Virtualization A Potential Antidote for Big Data Growing Pains

Data Virtualization A Potential Antidote for Big Data Growing Pains perspective Data Virtualization A Potential Antidote for Big Data Growing Pains Atul Shrivastava Abstract Enterprises are already facing challenges around data consolidation, heterogeneity, quality, and

More information

Predictive Analytics

Predictive Analytics Predictive Analytics Improving Performance by Making the Future More Visible Benchmark Research Research Report Executive Summary Sponsored by Aligning Business and IT To Improve Performance Ventana Research

More information

Self-Service Big Data Analytics for Line of Business

Self-Service Big Data Analytics for Line of Business I D C A N A L Y S T C O N N E C T I O N Dan Vesset Program Vice President, Business Analytics and Big Data Self-Service Big Data Analytics for Line of Business March 2015 Big data, in all its forms, is

More information

Three Asset Lifecycle Management Fundamentals for Optimizing Cloud and Hybrid Environments

Three Asset Lifecycle Management Fundamentals for Optimizing Cloud and Hybrid Environments Three Asset Lifecycle Management Fundamentals for Optimizing Cloud and Hybrid Environments An ENTERPRISE MANAGEMENT ASSOCIATES (EMA ) White Paper Prepared for BMC April 2011 IT & DATA MANAGEMENT RESEARCH,

More information

Business Intelligence and Analytics: Leveraging Information for Value Creation and Competitive Advantage

Business Intelligence and Analytics: Leveraging Information for Value Creation and Competitive Advantage PRACTICES REPORT BEST PRACTICES SURVEY: AGGREGATE FINDINGS REPORT Business Intelligence and Analytics: Leveraging Information for Value Creation and Competitive Advantage April 2007 Table of Contents Program

More information

Session 4a A Money and Information Business: Insurance E-Business Development and Trend in China. Steven Chen, FSA, MAAA, CFA

Session 4a A Money and Information Business: Insurance E-Business Development and Trend in China. Steven Chen, FSA, MAAA, CFA Session 4a A Money and Information Business: Insurance E-Business Development and Trend in China Steven Chen, FSA, MAAA, CFA A MONEY AND INFORMATION BUSINESS INSURANCE E-BUSINESS DEVELOPMENT AND TREND

More information

Five Best Practices for Data Management Optimizing the Use of Data for Business Intelligence and Big Data

Five Best Practices for Data Management Optimizing the Use of Data for Business Intelligence and Big Data Ventana Research: Five Best Practices for Data Management Five Best Practices for Data Management Optimizing the Use of Data for Business Intelligence and Big Data White Paper Sponsored by 1 Ventana Research

More information

Analytics 2014. Industry Trends Survey. Research conducted and written by:

Analytics 2014. Industry Trends Survey. Research conducted and written by: Analytics 2014 Industry Trends Survey Research conducted and written by: Lavastorm Analytics, the agile data management and analytics company trusted by enterprises seeking an analytic advantage. June

More information

Big Data Executive Survey

Big Data Executive Survey Big Data Executive Full Questionnaire Big Date Executive Full Questionnaire Appendix B Questionnaire Welcome The survey has been designed to provide a benchmark for enterprises seeking to understand the

More information

IRMAC SAS INFORMATION MANAGEMENT, TRANSFORMING AN ANALYTICS CULTURE. Copyright 2012, SAS Institute Inc. All rights reserved.

IRMAC SAS INFORMATION MANAGEMENT, TRANSFORMING AN ANALYTICS CULTURE. Copyright 2012, SAS Institute Inc. All rights reserved. IRMAC SAS INFORMATION MANAGEMENT, TRANSFORMING AN ANALYTICS CULTURE ABOUT THE PRESENTER Marc has been with SAS for 10 years and leads the information management practice for canada. Marc s area of specialty

More information

Executive Summary. At the end of the twentieth century and. Enterprise Systems for Higher Education Vol. 4, 2002

Executive Summary. At the end of the twentieth century and. Enterprise Systems for Higher Education Vol. 4, 2002 01 Executive Summary At the end of the twentieth century and into the twenty-first, higher education has invested, by a conservative estimate, $5 billion in administrative and enterprise resource planning

More information

2015 Richardson and Training Industry, Inc. All rights reserved.

2015 Richardson and Training Industry, Inc. All rights reserved. 2015 Richardson and Training Industry, Inc. All rights reserved. 1 2015 Richardson and Training Industry, Inc. All rights reserved. Contents Overview...3 Key Findings...3 Best Practices in Design and Delivery

More information

Transforming Big Data Into Smart Advertising Insights. Lessons Learned from Performance Marketing about Tracking Digital Spend

Transforming Big Data Into Smart Advertising Insights. Lessons Learned from Performance Marketing about Tracking Digital Spend Transforming Big Data Into Smart Advertising Insights Lessons Learned from Performance Marketing about Tracking Digital Spend Transforming Big Data Into Smart Advertising Insights Lessons Learned from

More information

The Customer Experience:

The Customer Experience: The Customer Experience: The Holy Grail of Competitive Advantage. 1 A great customer experience has emerged as the holy grail of competitive advantage. Providing a great customer experience has emerged

More information

The State of Analytics Maturity for Healthcare Providers

The State of Analytics Maturity for Healthcare Providers The State of Analytics Maturity for Healthcare Providers The DELTA Powered TM Analytics Assessment Benchmark Report - February 24, 2014 - The International Institute for Analytics and HIMSS Analytics iianalytics.com

More information

Social Business Analytics

Social Business Analytics IBM Software Business Analytics Social Analytics Social Business Analytics Gaining business value from social media 2 Social Business Analytics Contents 2 Overview 3 Analytics as a competitive advantage

More information

CREATING PACKAGED IP FOR BUSINESS ANALYTICS PROJECTS

CREATING PACKAGED IP FOR BUSINESS ANALYTICS PROJECTS CREATING PACKAGED IP FOR BUSINESS ANALYTICS PROJECTS A PERSPECTIVE FOR SYSTEMS INTEGRATORS Sponsored by Microsoft Corporation 1/ What is Packaged IP? Categorizing the Options 2/ Why Offer Packaged IP?

More information

Quantium captures new niche in data analytics market

Quantium captures new niche in data analytics market Quantium captures new niche in data analytics market MapR Distribution for Apache Hadoop and Cisco UCS cut query time by 92 percent, improve accuracy of results With the Cisco-MapR platform, Quantium has

More information

STATE OF THE INDUSTRY: HOW CONTENT MARKETING AND NATIVE WILL DRIVE A NEW ERA OF ENGAGEMENT

STATE OF THE INDUSTRY: HOW CONTENT MARKETING AND NATIVE WILL DRIVE A NEW ERA OF ENGAGEMENT STATE OF THE INDUSTRY: HOW CONTENT MARKETING AND NATIVE WILL DRIVE A NEW ERA OF ENGAGEMENT TABLE OF CONTENTS 3 Introduction 12 4 What you need to know 17 Proving the value of content marketing and native

More information

Data Management in the Cloud Era

Data Management in the Cloud Era In This Paper In cloud environments, using multiple point products for data management often results in diminishing returns Single-vendor solutions enable enterprises to leverage their cloud investments

More information

Mind Commerce. http://www.marketresearch.com/mind Commerce Publishing v3122/ Publisher Sample

Mind Commerce. http://www.marketresearch.com/mind Commerce Publishing v3122/ Publisher Sample Mind Commerce http://www.marketresearch.com/mind Commerce Publishing v3122/ Publisher Sample Phone: 800.298.5699 (US) or +1.240.747.3093 or +1.240.747.3093 (Int'l) Hours: Monday - Thursday: 5:30am - 6:30pm

More information

The Software-defined Data Center in the Enterprise

The Software-defined Data Center in the Enterprise The Software-defined Data Center in the Enterprise A Cloud Report by Ben Kepes This report underwitten by: NIMBOXX The Software-defined Data Center in the Enterprise 02/12/2015 Table of Contents 1. Executive

More information

Data Governance. Unlocking Value and Controlling Risk. Data Governance. www.mindyourprivacy.com

Data Governance. Unlocking Value and Controlling Risk. Data Governance. www.mindyourprivacy.com Data Governance Unlocking Value and Controlling Risk 1 White Paper Data Governance Table of contents Introduction... 3 Data Governance Program Goals in light of Privacy... 4 Data Governance Program Pillars...

More information

Small Business Virtualization Poll APJ RESULTS

Small Business Virtualization Poll APJ RESULTS Small Business Virtualization Poll APJ RESULTS CONTENTS Introduction... 4 Methodology... 6 Finding 1: Small businesses have a strong interest in virtualization... 8 Finding 2: Small businesses are still

More information

Data Management Emerging Trends. Sourabh Mukherjee Data Management Practice Head, India Accenture

Data Management Emerging Trends. Sourabh Mukherjee Data Management Practice Head, India Accenture Data Management Emerging Trends Sourabh Mukherjee Data Management Practice Head, India Accenture Data has always been an important asset for companies as it is the basis for making business decisions.

More information

WHITE PAPER. The 7 Deadly Sins of. Dashboard Design

WHITE PAPER. The 7 Deadly Sins of. Dashboard Design WHITE PAPER The 7 Deadly Sins of Dashboard Design Overview In the new world of business intelligence (BI), the front end of an executive management platform, or dashboard, is one of several critical elements

More information

A Strategic Approach to Customer Engagement Optimization. A Verint Systems White Paper

A Strategic Approach to Customer Engagement Optimization. A Verint Systems White Paper A Strategic Approach to Customer Engagement Optimization A Verint Systems White Paper Table of Contents Introduction... 1 What is customer engagement?... 2 Why is customer engagement critical for business

More information

Optimizing BI and Data Warehouse Performance

Optimizing BI and Data Warehouse Performance Optimizing BI and Data Warehouse Performance New Approaches To Get More from Your Information Assets Executive Summary Aligning Business and IT To Improve Performance Ventana Research 2603 Camino Ramon,

More information

THE ANALYTICS HUB LEVERAGING A SHARED SERVICES MODEL TO UNLOCK BIG DATA. Thomas Roland Managing Director. David Roggen Director CONTENTS

THE ANALYTICS HUB LEVERAGING A SHARED SERVICES MODEL TO UNLOCK BIG DATA. Thomas Roland Managing Director. David Roggen Director CONTENTS THE ANALYTICS HUB LEVERAGING A SHARED SERVICES MODEL TO UNLOCK BIG DATA David Roggen Director Thomas Roland Managing Director CONTENTS Shared Services Today 2 What Is an Analytics Hub? 3 Analytics Hub

More information

COULD VS. SHOULD: BALANCING BIG DATA AND ANALYTICS TECHNOLOGY WITH PRACTICAL OUTCOMES

COULD VS. SHOULD: BALANCING BIG DATA AND ANALYTICS TECHNOLOGY WITH PRACTICAL OUTCOMES COULD VS. SHOULD: BALANCING BIG DATA AND ANALYTICS TECHNOLOGY The business world is abuzz with the potential of data. In fact, most businesses have so much data that it is difficult for them to process

More information

Integrated Risk Management:

Integrated Risk Management: Integrated Risk Management: A Framework for Fraser Health For further information contact: Integrated Risk Management Fraser Health Corporate Office 300, 10334 152A Street Surrey, BC V3R 8T4 Phone: (604)

More information

Large Telecommunications Company Gains Full Customer View, Boosts Monthly Revenue, Cuts IT Costs by $3 Million

Large Telecommunications Company Gains Full Customer View, Boosts Monthly Revenue, Cuts IT Costs by $3 Million Microsoft Business Intelligence Customer Solution Case Study Large Telecommunications Company Gains Full Customer View, Boosts Monthly Revenue, Cuts IT Costs by $3 Million Overview Country or Region: United

More information

Why You Should Consider the Cloud

Why You Should Consider the Cloud INTERSYSTEMS WHITE PAPER Why You Should Consider the Cloud In 2014, we ll see every major player make big investments to scale up Cloud, mobile, and big data capabilities, and fiercely battle for the hearts

More information

BANKING ON CUSTOMER BEHAVIOR

BANKING ON CUSTOMER BEHAVIOR BANKING ON CUSTOMER BEHAVIOR How customer data analytics are helping banks grow revenue, improve products, and reduce risk In the face of changing economies and regulatory pressures, retail banks are looking

More information

Customized Report- Big Data

Customized Report- Big Data GINeVRA Digital Research Hub Customized Report- Big Data 1 2014. All Rights Reserved. Agenda Context Challenges and opportunities Solutions Market Case studies Recommendations 2 2014. All Rights Reserved.

More information

THE REALITY OF CLOUD COMPUTING HAS IT LIVED UP TO THE HYPE?

THE REALITY OF CLOUD COMPUTING HAS IT LIVED UP TO THE HYPE? DATA CENTRE & CLOUD SERVICES WHITEPAPER THE REALITY OF CLOUD COMPUTING HAS IT LIVED UP TO THE HYPE? TABLE OF CONTENTS 1. Introduction......................... 2 2. Key findings........................

More information

BEYOND BI: Big Data Analytic Use Cases

BEYOND BI: Big Data Analytic Use Cases BEYOND BI: Big Data Analytic Use Cases Big Data Analytics Use Cases This white paper discusses the types and characteristics of big data analytics use cases, how they differ from traditional business intelligence

More information

An in-depth look into how today s cloud solution providers create and sustain successful partnerships while empowering customers to move to the cloud.

An in-depth look into how today s cloud solution providers create and sustain successful partnerships while empowering customers to move to the cloud. Partnering in the Cloud 2015 ISV REPORT An in-depth look into how today s cloud solution providers create and sustain successful partnerships while empowering customers to move to the cloud. Partnering

More information

Accenture Human Capital Management Solutions. Transforming people and process to achieve high performance

Accenture Human Capital Management Solutions. Transforming people and process to achieve high performance Accenture Human Capital Management Solutions Transforming people and process to achieve high performance The sophistication of our products and services requires the expertise of a special and talented

More information

Three Reasons Why Visual Data Discovery Falls Short

Three Reasons Why Visual Data Discovery Falls Short Three Reasons Why Visual Data Discovery Falls Short Vijay Anand, Director, Product Marketing Agenda Introduction to Self-Service Analytics and Concepts MicroStrategy Self-Service Analytics Product Offerings

More information

Delivering Real-World Total Cost of Ownership and Operational Benefits

Delivering Real-World Total Cost of Ownership and Operational Benefits Delivering Real-World Total Cost of Ownership and Operational Benefits Treasure Data - Delivering Real-World Total Cost of Ownership and Operational Benefits 1 Background Big Data is traditionally thought

More information

Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014-2019

Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014-2019 MARKET RESEARCH STORE Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014-2019 Market Research Store included latest deep and professional market research report on Big

More information

Turning Big Data into Big Insights

Turning Big Data into Big Insights mwd a d v i s o r s Turning Big Data into Big Insights Helena Schwenk A special report prepared for Actuate May 2013 This report is the fourth in a series and focuses principally on explaining what s needed

More information

VMware Cloud Adoption Study

VMware Cloud Adoption Study VMware Cloud Adoption Study Executive Summary May 2012 Contents About the research 3 Objectives 4 Overview 4 Key Findings 5 European enterprises to spend a third of IT budgets this year on cloud computing,

More information

WHITE PAPER Risk, Cost and Quality: Key Factors for Outsourcing QA and Testing

WHITE PAPER Risk, Cost and Quality: Key Factors for Outsourcing QA and Testing WHITE PAPER Risk, Cost and Quality: Key Factors for Outsourcing QA and Testing In association with: TCS Marianne Kolding December 2012 Ed Cordin IDC OPINION IDC EMEA, 389 Chiswick High Road, London, W4

More information

Strategic Marketing Performance Management: Challenges and Best Practices

Strategic Marketing Performance Management: Challenges and Best Practices Strategic Marketing Performance Management: Mark Jeffery and Saurabh Mishra Center for Research on Technology and Innovation Kellogg School of Management Email: mjeffery@kellogg.northwestern.edu Phone:

More information