Is Your Big Data Solution Production-Ready?

Transcription

1 A Custom Technology Adoption Profile Commissioned By CenturyLink Technology Solutions January 2014 Is Your Big Data Solution Production-Ready?

2 1 Introduction This CenturyLink Technology Solutions (formerly Savvis) commissioned profile of enterprise IT big data decisionmakers evaluates the maturity of firms adoption of big data and the challenges they face implementing enterprisewide solutions, based on Forrester s own market data, and a custom study of the same audience. Big Momentum For Big Data Wow, 70% of surveyed IT decision-makers said that that big data analytics is a key priority now or in one year (see Figure 1). Why? Companies seek deeper insights into the massive amount of structured, unstructured, and binary data at their disposal to dramatically improve business outcomes production big data analytics solutions that: Capture and store all data for all business functions. Most firms estimate that they are only analyzing 12% of the data that they already have (see Figure 3). They are leaving 88% of it on the cutting room floor. Repressive data silos and a lack of analytics capabilities are key reasons for this. In addition, it s often impossible to judge what data is valuable and what isn t. In the age of big data, you have to capture and store it all. Data that might seem completely irrelevant to your business now, such as mobile GPS data, might be a gold mine in the future. The effort and cost of capturing and storing all data have often forced decisions on what to store and what to throw away. But, new technologies such as Hadoop have made it possible to capture, store, and analyze lots more data cost-effectively. Support advanced analytics capabilities. Traditional business intelligence (BI) tools can benefit from big data, but firms also want to use advanced visualization tools and predictive analytics to explore data in new ways and discover new patterns. A big data solution is not just about supporting data, it also has to have the compute horsepower to perform advanced statistical and machine learning algorithms used by data scientists. Share customer data quickly and generously with all those who need it. Organizations can use big data to dramatically improve virtually every function of the business, including product research, design, and development; advertising and marketing management; sales; and the customer experience. But, data is often in silos, making it very difficult to share it across the organization. A big data solution must make relevant data available quickly and to everyone who could get value from it and because it may not be obvious who can get value from the information, you should ideally make all of it available. For example, marketing data may be very useful to both R&D and customer service but remains siloed in a CRM system or marketing database. Data combined from multiple silos can help your organization find answers to complex questions that no one would dare ask or know to ask before. Continuously accommodate more data volume and new data sources. An onslaught of new data is being generated by your internal applications, public sources such as social media, mobile platforms, and data services. Your big data solution must scale quickly and cost-effectively to handle the increasing volume, velocity, and variety of data. The bottom line: Your big data solution must provide your business with quick and complete access to all data, and all analytics that it needs now and may need in the near future. FIGURE 1 Big Data Is A Priority For Firms Which of the following time frames best completes the statement below? (Big data analytics is/will be a key priority at our organization ) In 2 years 22% In 3 years 6% In a year 29% Don t know 2% Today 41% Base: 51 North American enterprise IT decision-makers at firms that have adopted or are currently conducting a proof of concept of big data Source: A commissioned study conducted by Forrester Consulting on behalf of CenturyLink Technology Solutions, October 2013

3 2 FIGURE 2 Firms Seek New Ways To Explore Data, Discover Patterns, And Insights What are the main business and technical requirements or inadequacies of earlier-generation BI technologies that lead you to consider new BI techniques and technologies? We don t know what our entire data universe contains; we need new ways to explore data and discover patterns and insights before we even understand what we are looking for Data volumes have grown beyond what we can cost-effectively manage 38% We want to access data that was not accessible for us with existing technologies 36% 43% Analysis requirements change too fast to keep up with 35% The number of data formats that we must be able to deal with exceeds our ability to cost-effectively integrate 35% Data changes or becomes available much faster than we can process in support of business decisions 33% We can achieve (or are achieving) significant cost reductions by changing our data management and analytic architecture 28% The velocity of data is too high for earlier technologies 24% Earlier generation technology is too expensive 20% Don t know 3% Other 2% Base: 246 North American enterprise business intelligence decision-makers Source: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012, Forrester Research, Inc. FIGURE 3 Most Firms Analyze Only A Fraction Of Their Data Enterprise data Analyze only ~12% Base: 418 to 533 business intelligence users using each typology Source: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012, Forrester Research, Inc.

4 3 Firms Struggle To Implement Big Data In Production Implementing production-ready big data solutions is easier said than done. Firms face a hydra of challenges that make it difficult to go from a tidy proof-of-concept (POC) to production (see Figure 4). The number one challenge: integrating big data solutions in a complex, heterogeneous data management environment. This is not surprising since most large enterprises have an enormously diverse set of technologies, tools, architectures, and information security policies. Hadoop is at the center of most big data solutions: 65% of respondents said that they either use Hadoop in one or more big data proof-of-concept (POC) or plan to use it in the future (see Figure 5). But, only 16% of respondents said that they use Hadoop in production. A big wave of production Hadoop implementations is imminent, but Hadoop is not an island unto itself. It must be fully integrated with sources of data such as applications, database warehouses, and external sources of data. It must also be integrated with the tools used by business intelligence professionals, data scientists, and business users who need access to the data and analytical processing power. Finally, it must meet the expectations and rigors of a production environment. FIGURE 5 Most Firms Are Using Hadoop In POC-Mode: A Production Wave Is Coming What best describes your organization s use of Hadoop? We use Hadoop in one or more proof-of-concept (POC) We do not use Hadoop, but we plan to use it in the future We use Hadoop in production 16% We do not use or plan to use Hadoop Don t know 4% 16% 20% Base: 51 North American enterprise IT decision-makers at firms that have adopted or are currently conducting a proof of concept of big data Source: A commissioned study conducted by Forrester Consulting on behalf of CenturyLink Technology Solutions, October % FIGURE 4 Top Challenges In Implementing A Production Big Data Solution What challenges did you face or expect to face in implementing a production big data solution? (Select all that apply) Integrating big data solutions in a complex, heterogeneous data management environment Technical implementation skills among our current staff 49% Meeting business demand for big data analytics 43% 65% Understanding how big data solutions can improve business outcomes 39% Infrastructure budget constraints 37% Difficulty in finding and hiring people with skills 33% None of the above 2% Base: 51 North American enterprise IT decision-makers at firms that have adopted or are currently conducting a proof-of-concept of big data Source: A commissioned study conducted by Forrester Consulting on behalf of CenturyLink Technology Solutions, October 2013

5 4 The Seven Qualities Of Big Data Production-Ready Solutions Big data rookies make the mistake of thinking that implementing a big solution is just a matter of declaring that the POC is now production. Think again. IT leaders need to see the bigger picture. Implementing a big data solution requires planning, architecture, talent, flawless execution, and a supreme command of what it means to be production-ready. Production-ready big data solutions must exhibit these seven qualities: Manageability. Tools that can monitor, diagnose, and orchestrate all solution components. Availability. The readiness of data and analytical services to perform their functions when needed. Performance. The speed to perform queries and analysis that meet business and user expectations. Scalability. Handle increasing and decreasing volumes of data, jobs, and users. Adaptability. The ease with which the solution can be changed or extended to meet new analytical requirements. Security. Support the security properties of authentication, authorization, confidentiality, integrity, and nonrepudiation. Cost. Minimize the cost to build, operate, and adapt the big data solution without compromising business value. Conclusion R. Buckminster Fuller once said, We are called to be architects of the future, not its victims. 1 The good news is that forward-leaning businesses have prioritized big data analytics because they want to do everything possible to profit from the explosion of data. Success, however, will depend on IT leaders who can successfully take big data analytics from science project to production-ready in short order. Be proactive. See the bigger picture. Seek and design solutions that meet the seven qualities of productionready big data analytics solutions.

6 5 Methodology This Technology Adoption Profile was commissioned by CenturyLink Technology Solutions (formerly Savvis). To create this profile, Forrester leveraged its Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q Forrester Consulting supplemented this data with custom survey questions asked of 51 North American enterprise IT decision-makers. Survey respondents were from firms that have adopted or are currently conducting a proof of concept of big data. The auxiliary custom survey was conducted in October For more information on Forrester s data panel and Tech Industry Consulting services, visit Endnotes 1 Buckminster Fuller was an American architect, systems theorist, author, designer, inventor, and futurist. Source: Buckminster Fuller Institute ( ABOUT FORRESTER CONSULTING Forrester Consulting provides independent and objective research-based consulting to help leaders succeed in their organizations. Ranging in scope from a short strategy session to custom projects, Forrester s Consulting services connect you directly with research analysts who apply expert insight to your specific business challenges. For more information, visit forrester.com/consulting. 2014, Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. Forrester, Technographics, Forrester Wave, RoleView, TechRadar, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. For additional information, go to [1-MY1RKJ]