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

Transcription

1 Survey Results

2 Table of Contents Survey Results... 4 Big Data Company Strategy... 6 Big Data Business Drivers and Benefits Received... 8 Big Data Integration Big Data Implementation Challenges Big Data Implementation Technologies About Talend Page 2

3 Big data represents a significant paradigm shift in enterprise technology and stands to transform much of what the modern enterprise is today. Digital data is everywhere and global data is growing at 40% per year. Companies capture trillions of bytes of information about their customers, suppliers, and operations, and millions of networked sensors are being embedded in the physical world in devices such as mobile phones, energy meters and automobiles, sensing, creating, and communicating data. 1 By collecting and analyzing all this information companies gain insight into new business opportunities and threats. But what exactly is big data? Big data encompasses a complex and large set of diverse structured and unstructured datasets that are difficult to process using traditional data management practices and tools. There is an increasing desire to collect call detail records, web logs, data from sensor networks, financial transactions, social media and Internet text, and then analyze with existing data sources. Conventional data management tools fail when trying to integrate, search and analyze big datasets, which (for now) range from terabytes to multiple petabytes of information. As an example, Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data - the equivalent of 167 times the information contained in all the books in the US Library of Congress. 2 New technologies based on the Apache Hadoop Big Data Platform have emerged as a way to analyze large data sets through a technique called massively parallel-processing (MPP) of information. As with any new successful paradigm, there is a technology adoption curve from innovators and early adopters, to the early majority, to the late majority, to laggards. Early adopters are driven by competitive advantage and innovation, take the biggest risks for success, typically use primitive tools, and build it themselves. Conversely, the late majority and laggards strive for the productivity gains others have received and take less risk by adopting proven technologies backed by robust products and services. In the information arms race, companies that can collect and analyze more information should be able to make faster, better-informed decisions compared to their competitors, e.g. by maximizing customer wallet share, by knowing when and why customers may leave, by efficiently creating and targeting new markets, or by deterring fraud. To date most of the big data discussion has been about big data technology. The goal of this survey and whitepaper is to highlight big data adoption challenges, business objectives and benefits, as well as big data technologies being used. 1 Big data: The next frontier for innovation, competition, and productivity McKinsey & Company, May Page 3

4 Survey Results In the summer of 2012, Talend conducted a big data adoption survey of 231 professionals involved in the delivery of data solutions for their company. Survey respondents were closely split between North America (49%) and EMEA (51%), with 60% of respondents in IT and 36% having business titles. 95 respondents who did have a big data strategy, were then asked a series of questions about their experience. Figure 1: Survey Demographics Other 4% EMEA 51% North America 49% IT 60% Business 36% Key findings from the survey are: 41% of companies have a strategy for dealing with big data, indicating the growing adoption of big data. 48% of big data initiatives are driven by the business, 39% by IT, and 13% cross-functionally. For those without a big data strategy, the main reason (76%) is that they do not distinguish big data from existing corporate data. Increasing the depth and accuracy of predictive analytics was the number one driver for big data, reported by 68% of those who have a big data strategy. Using today s definition of big data (> 10 terabytes), 71% of respondents have big data to manage. 62% indicated that they have achieved big data business benefits with the primary benefit being business process optimization (28%) and improvements in marketing and sales (24%). Page 4

5 However, 24 companies reported not receiving a business benefit which may indicate the need for improved big data skillsets, governance and management. The types (inputs) of big data that are being used today include web and social media (57% of respondents) followed by sales data (54% of respondents). 61% replied that their primary big data challenge was allocating sufficient time, budget and resources, with just over half (52%) reporting a lack of big data in-house expertise. Open source Apache Hadoop and Hadoop-based distributions represented over 60% of big data implementation technologies in use or considered for use. Page 5

6 Big Data Company Strategy It was just over 10 years ago when Doug Laney from the Meta Group (now Gartner) published a report 3 on the growing volume, velocity and variety of data and that organizations need to look beyond traditional approaches. The business model of big data early adopters such as Google and Facebook required that they create a strategy to collect and analyze large volumes of data to scale their business. Some companies have big data collection and analysis in their DNA and have built a separate big data strategy. Others recognize that big data is part of a larger total data management function and incorporate big data tools and practices throughout the business to manage big data as well as enterprise data and discrete data. In 2011 there were 9 companies who offered products based on big data (Apache Hadoop) technologies and now there are over 120 vendors clearly showing signs of momentum. The survey results (Figure 2) showed that 41% of organizations do have a big data strategy and 59% do not. Furthermore, 76% of those without a specific strategy replied that they do not distinguish big data from existing corporate data management practices. Figure 2: Does your organization have a strategy for dealing with big data? (n=231) No Yes Figure 3: If no, why? (n=136) We don't distinguish big data from existing corporate data Other reasons 59% 76% 41% 24% For companies that do have a big data strategy (n=95), it is being driven by several company functions (Figure 4), which indicates that big data as a core strategy has moved past an early adopter stage. 39% indicated that big data initiatives are being driven by IT, or a bottom up approach to be more efficient in collecting and analyzing large data sets. However, 48% of big data strategy is being driven by lines of business or executives, which indicates there are compelling business reasons for big data adoption, such as increased revenue, improved customer satisfaction, or faster time to market. 3 Velocity-and-Variety.pdf Page 6

7 Figure 4: The big data initiative is primarily being driven by: Crossfunctional Team 13% Board of Directors 2% Executive Management 20% IT 39% Business and Consumers of Data 26% Early results show that big data is part of larger corporate data initiatives, and is being driven by business more than IT. Page 7

8 Big Data Business Drivers and Benefits Received Data requirements and benefits vary by industry. For example, communications providers, government, healthcare and retail firms all have larger amounts of unstructured data such as text, audio and video files that can benefit from big data collection and analysis. The number one business driver for big data (68%) is increasing the accuracy and depth of predictive analytics or the ability to analyze current and historical data to make future predictions. Revenue optimization (51%) and new revenue generation (48%) were the second and third highest responses as companies seek to do more in-depth analysis to maximize market and wallet share, e.g. improve cross-selling capabilities. Figure 5: What are the business drivers for big data in your organization? (multiple responses, n=95) 80% 70% 60% 68% 50% 40% 48% 51% 30% 20% 10% 0% 31% 20% 19% 16% 1% Page 8

9 For those that have implemented big data projects, 62% indicated that they have achieved business benefits (Figure 6) with the primary benefit being business process optimization (28%) and improvements in marketing and sales (24%). It is a concern that 38% responded No or Unknown, and may be due to the lack of project governance, data quality, big data skillsets and/or available tooling, which is typical for new paradigms. Figure 6: To date, have you realized any business benefit to big data? (n=95) Unknown 13% Yes, particularly in Marketing and Sales 24% No 25% Yes, particularly in Business Process Optimization 28% Yes, particularly in Crime Prevention and Fraud 5% Yes, particularly in Customer Retention 5% Big data business benefits include business process optimization and marketing/sales improvement; however for some projects it is too early to tell or have failed to deliver a benefit. Page 9

10 Big Data Integration Common big data use cases include marketing campaign analysis, recommendation engines, predictive analytics, sentiment analysis, risk management and fraud detection. IT is integrating existing data warehouses and business intelligences systems with diverse sets of structured and unstructured data for more in-depth analysis. The survey revealed that the most common applications being integrated were financial transactions (48.4%) and social media, clickstream and Internet text (48.4%), followed by web logs (35.8%) and call detail records (28.4%). By looking at social media and internet text, firms can understand who the super users are in any social network or community, i.e. ones that have the most influence over others inside social networks. Also, by correlating financial transactions and call detail records with click streams, one can generate a more complete view of customer buying patterns and behavior. Figure 7: Which applications are driving big data needs at your organization? (multiple responses) 60% 50% 40% 48% 48% 30% 36% 20% 28% 25% 25% 10% 16% 0% Call detail records Financial Science, transactions research data or medical data Sensor data Social Video, media, imaging data click- stream or internet search analytics Web logs 8% Other Furthermore the type of big data that is being used today or considered in the future reinforced the previous response. Web and social media are being used by 57% of respondents today with 23% considering it for the future. Sales data was the second highest for being used today (54%) and Page 10

11 considered for the future (32%) for analyzing buying patterns and sales incentives. Biometric data was the lowest on the list. Figure 8: What type of big data are you involved in or considering making part of your Business Intelligence? Actively Involved Considering for the Future Not Considering 19% 23% 14% 25% 22% 30% 30% 69% 36% 54% 42% 51% 8% 18% 34% Web and social media Machine generated (web logs, twitter data (RFID, GPS, feeds, JSON) phone apps and other machine generated data) Sales data (buying patterns and sales incentives) Biometric Human interactions (Qingerprint, voice/ (e- mails, voice mails, face recognition, DNA) call centers) Web and social media are big data s primary inputs with sales data a close second. Page 11

12 Big Data Implementation Challenges The technical challenges in processing big data involve integrating, searching and analyzing large data sets. However, like any new paradigm, companies must also find the right skillsets, get budget approval, navigate company politics, and manage the unknowns. The United States alone faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to analyze big data and make decisions based on their findings. 4 Many early big data projects are free of explicit project management structure and over time companies will incorporate standards and procedures just as they have with data management projects. In the survey, 61% replied that their primary big data challenge was allocating sufficient time, budget and resources, with just over half (52%) reporting a lack of big data in-house expertise. Also 48% reported data quality challenges, while only 11% reported a challenge getting C-level buy-in for big data projects. Figure 9: What challenges to implementing big data are most hindering your success? (multiple responses) 70% 60% 50% 40% 52% 61% 48% 30% 36% 37% 20% 10% 0% 18% 11% 5% 4% 4 Big data: The next frontier for innovation, competition, and productivity McKinsey & Company, May Page 12

13 Big data processing varies by organization, industry and the types of tools available to process the data. It may be collecting and analyzing terabytes of information or many petabytes of data, and over time it is assumed that the definition of big data will grow. Using today s definition of big data (> 10 terabytes) 71% of respondents (Figure 10) have big data to manage. 46% of respondents have over 100 terabytes of data to manage and 12% had greater than 2 petabytes to manage. Figure 10: What is the total amount of data that exists within your organization? < 10 Terabytes 6% 11% 6% 29% 10 to 99 Terabytes 100 to 499 Terabytes 23% 25% 500 to 1 Petabytes 2 to 5 Petabytes > 5 Petabytes A large majority of companies have over 10 terabytes of data to manage, but the biggest barriers to big data adoption are a shortage of time, budget, expertise and resources. Page 13

14 Big Data Implementation Technologies Many technologies have been developed to integrate, manipulate, manage and analyze big data. Survey respondents were asked which big data technologies they are using or they are considering to use. Apache Hadoop (28%), an open source framework with basic big data constructs (file system, language, and distributed system for managing large datasets) that incorporates MapReduce, was the most popular response. Showing strong support for open source technology, Hadoop and Hadoopbased solutions represented 62% of the responses, The large amount of Other (38%) responses suggest an early adopter, fragmented market, and included selections for big data appliances (e.g. Teradata, Netezza) and NoSQL databases (e.g. Couchbase, MongoDB). Figure 11: Which implementation of big data technology are you considering or are you using already? (n=95) Amazon Web Services 13% Apache Hadoop (own installation) 38% 28% Cloudera (CDH) Greenplum Hortonworks Data Platform 4% 3% 2% 12% MAPR Other Open source Apache Hadoop and Hadoop-based distributions represented over 60% of big data implementations in use or considered for use. Page 14

15 About Talend Talend is the recognized leader in open source integration solutions. The company s holistic integration platform helps organizations minimize costs and maximize the value of data integration, ETL, data quality, master data management, application integration and business process management - while supporting their shift toward big data. More than 3,500 paying customers worldwide, including ebay, ING, The Weather Channel, Deutsche Post and Allianz, subscribe to Talend s solutions and services. With over 20 million downloads, Talend s products are the most trusted integration solutions in the world. The company has major offices in North America, Europe and Asia, and a global network of technical and services partners. Talend s open source approach and flexible integration platform for big data enables users to easily connect and analyze data from disparate systems to help drive and improve business performance. Talend s big data capabilities integrate with today s big data market leaders such as Cloudera, Hortonworks, Google, Greenplum, Mapr, Teradata and Vertica, positioning Talend as a leader in the management of big data. Talend s goal is to democratize the big data market just as it has with data integration, data quality, master data management, enterprise service bus and business process management. Visit to learn more and download your free copy of Talend Open Studio for Big Data. Page 15