Harnessing Big Data to Solve Complex Problems: The Cloud Analytics Reference Architecture

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1 Harnessing Big Data to Solve Complex Problems: The Cloud Analytics Reference Architecture

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3 Table of Contents Introduction... 1 Cloud Analytics Reference Architecture... 1 Using All the Data... 3 Better Questions and Answers... 3 A Deliberate Approach to Unlocking the Promise of Big Data... 5 A Strong Foundation... 5 The Data Lake... 6 The Analytics... 7 Visualization and Interaction Conclusion... 13

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5 Harnessing Big Data to Solve Complex Problems The Cloud Analytics Reference Architecture Introduction The ability to compete and win in the information economy will come from powerful analytics that draw insights and value from data, and from high-fidelity visualizations that present those insights in impactful, intuitive ways. Government and business organizations are increasingly looking to big data to create new opportunities and solve their most complex real-world problems. They hope to tap into the many rich new sources of information that are emerging from online consumer behavior to social networking to the growing use of electronic health records. At the same time, organizations are building up immense databases of their own, using rapid advances in cloud storage. Despite this new wealth of information, the key to unlocking its value seems to be missing. Organizations are discovering that the size and diversity of big data make it difficult to use in a meaningful way. They are never able to explore all of the information at once, and so are unable to track overall trends, or find the kinds of larger, unexpected patterns that can lead to valuable knowledge and insight. The problem is that organizations are limited by computing techniques developed long before big data arrived on the scene. With these conventional techniques, only narrow slices of data can be accessed at any time. Datasets and analytics are highly structured, and must be torn down and rebuilt with each new line of inquiry. Information that does not neatly fit into such rigid structures such as Twitter and video feeds often cannot be used. While organizations are collecting more information than ever, the data tends to reside in silos that are difficult to integrate. Cloud storage, despite its benefits, has not eliminated the data silos it has simply made them fatter. In addition, few of the world s IT systems are ready for the technology revolution happening as organizations seek to transform how they use data. As illustrated in Exhibit 1, their infrastructures. face three major challenges: Exhibit 1 Data Challenges in the Era of Big Data Volume Not enough storage capacity and analytical capabilities to handle massive volumes of data Source: Booz Allen Hamilton Variety Data comes in many different formats, which can be difficult and expensive to integrate Velocity Inability to process data in real time in order to extract the most value from it To help organizations overcome these hurdles and prepare for what s next, Booz Allen has pioneered an entirely new approach for the implementation of big data in the digital enterprise a way of using technology, machine-based analytics, and human-powered analysis to create competitive and mission advantage. This innovative approach, known as the Cloud Analytics Reference Architecture, removes the conventional constraints, enabling organizations to integrate all of their available data, along with information from multiple outside data sources. This powerful capability makes it possible for organizations to find value, guide strategy, and solve mission and business problems long considered too complex. Cloud Analytics Reference Architecture The Cloud Analytics Reference Architecture has been proven in high-stakes environments. It was developed through/during an ongoing collaboration between Booz Allen and the US government to leverage big data in the search for terrorists and other threats. Intelligence analysts are currently using the Cloud Analytics Reference Architecture to integrate the wide entire spectrum of intelligence sources, and apply sophisticated analytical tools to find hidden 1

6 connections and patterns. Similarly, the US military is using the Cloud Analytics Reference Architecture to provide information on insurgents and others who are planting improvised explosive devices (IEDs) and other bombs. The capability of the Cloud Analytics Reference Architecture to analyze a vast array of disparate data sources is providing military commanders with unprecedented situational awareness. Commanders have reported that the approach is saving lives. In another example, Booz Allen and a large hospital chain in the Midwest have demonstrated how the Cloud Analytics Reference Architecture can also save lives in medicine. By analyzing a large volume of electronic health records, researchers have discovered unexpected patterns over time in the vital signs of former patients whose serious, often hospital-acquired infections suddenly became life-threatening. Using those insights, the hospital system has begun a program to monitor current patients with infections, watching whether their vital signs are following the same patterns. This procedure is providing doctors with an early warning that their patients conditions may be deteriorating. Booz Allen is now adapting the Cloud Analytics Reference Architecture for the larger government and business communities. This groundbreaking approach can be applied to a broad range of critical problems, such as: Looking across large populations of internal and external network users to identify those most likely to steal information and commit fraud. The Cloud Analytics Reference Architecture can achieve this by integrating data from sources as varied as social media sites, public records and even users patterns of computer behavior. Uncovering threats to the stability of the US financial system, by discovering hidden patterns in the combined data of an array of government regulators and private financial institutions. Exhibit 2 Booz Allen s Cloud Analytics Reference Architecture Visualization, Reporting, Dashboards, and Query Interface Services (SOA) Human Insights and Actions Enabled by customizable interfaces and visualizations of the data Analytics and Discovery Views and Indexes Streaming Indexes Analytics and Services Your tools for analysis, modeling, testing, and simulations Data Lake Data Management The single, secure repository for all of your valuable data Source: Booz Allen Hamilton Metadata Tagging Data Sources Infrastructure/Management Infrastructure The technology platform for storing and managing your data 2

7 Enabling two or more government investigative agencies with a shared mission to integrate their intelligence and create a common operating picture while precisely adhering to the restrictions, authorities and security issues pertaining to each organization s data. The Cloud Analytics Reference Architecture represents not an incremental step forward, but rather an entirely new approach one specifically designed to solve organizations real-world problems, and provide them new opportunities, by harnessing the power of big data. Using All the Data The Cloud Analytics Reference Architecture takes advantage of the immense storage ability of the cloud, but in a completely new way. An organization s repository of information is no longer stored in rigid, regimented data structures, but rather is consolidated in a vast pool, or data lake. Every inquiry can make use of this entire pool, along with information from multiple outside data sources and it is all available at once. Users no longer need to move from database to database, pulling out specific information. And because there are no data silos, there is no need to integrate them. What results is not chaotic or overwhelming. Rather, the rich diversity of information in the data lake becomes a powerful force. The data lake is more than a means of storage it is a medium expressly designed to foster connections in data. And the Cloud Analytics Reference Architecture explores those connections to search for valuable correlations and patterns. This actually reduces the complexity of big data, making it manageable and useful, and creating efficiencies. The crucial role of the data lake can be seen when the Cloud Analytics Reference Architecture is viewed in layers (see Exhibit 2). The data lake is supported from below by the cloud storage infrastructure, and in turn supports the computer analytics. All of these elements support the final phase, the visualization and interaction, where human insight and action take place. Better Questions and Answers With the conventional approach, we do not really ask questions of the data we create hypotheses, and then test the data to see whether we are right. In order to pose these hypotheses, we have to guess in advance what the answers might be, often a difficult proposition. We also need to be familiar with the data we are considering, including where it is (in what specific datasets or databases), what format it is in, and even to a large extent what the data itself contains. That level of knowledge might be achievable when we are working with a limited number of datasets or databases, but not with the vast amounts of information now becoming available to us. We often have to put aside, or assume away, factors that we might actually believe are critical. And so we end up settling for marginal questions, and marginal answers. Because the data lake removes the need for rigid data structures, all of these constraints are removed. We no longer need to pose hypotheses of defined data, and so can ask more big-picture, intuitive questions. The Cloud Analytics Reference Architecture also allows us to more readily look for unexpected patterns it lets the data talk to us, so to speak. While we can look for patterns with the conventional approach, we can only do so within our narrowly defined datasets and databases, and we have to know in advance what patterns we might be looking for. With the Cloud Analytics Reference Architecture, we can discover unexpected patterns that naturally emerge in the data. This capability creates opportunities to predict the future by looking at the past. Because the Cloud Analytics Reference Architecture can store and analyze vast amounts of information, organizations can look for patterns in historical data, and see whether such patterns are repeating today. This can, for example, help regulators determine whether financial institutions are repeating the mistakes of the past. And it can help medical researchers look for patterns in the historical heath records of thousands of previous patients, to help treat patients today. 3

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9 A Deliberate Approach to Unlocking the Promise of Big Data Booz Allen s Cloud Analytics Reference Architecture provides a holistic approach to people, processes, and technology in four tightly integrated layers, as depicted in Exhibit 3. By design, these layers work seamlessly together to: Allow distributed storage and replication of bytes across networks and hardware that are assumed to fail at any time Allow for massive, world-scale storage that separates metadata from data Support a write-once, sporadic append, read-many usage structure Store records of various sizes, from a few bytes up to a few terabytes in size Allow compute cycles to be easily moved to the data store, instead of moving the data to a processer farm. The Cloud Analytics Reference Architecture has an inherent flexibility that enables organizations to pursue new analytical approaches with few if any changes to the underlying infrastructure. For example, the data lake is easily expandable. Because it stores information so efficiently, it can accommodate both the natural growth of an organization s data, as well as the addition of data from multiple outside sources. At the same time, the Cloud Analytics Reference Architecture replaces the current, custom-built analytic and visualization tools with ones that can easily be adapted for almost any number of inquiries. A Strong Foundation With the conventional approach, organizations must continually reinvest in infrastructure as analytic needs change. Building bridges between silos, for example, typically requires reconfiguring and even expanding Exhibit 3 Layers within the Cloud Analytics Reference Architecture Human Insights and Actions LAYER 1 LAYER 2 Human Insights and Actions Building on results and outputs from various analytical methods, multiple data visualizations can be created in your new cloud analytics solution. These are used to compose the interactive, real-time dashboard interfaces your decision makers and analysts need to make sense of your data. Analytics and Services Both traditional and Big Data tools and software can operate on the information stored in your Data Lake, producing advanced specific analysis, modeling, testing, and simulations you need for decision making. Analytics and Services Data Management LAYER 3 LAYER 4 Data Management Your Data Lake is a secure, distributed repository of a wide variety of data sources. Security, metadata, and indexing of Big Data are enabled by distributed key value systems (NoSQL), but the Architecture allows for traditional relational databases as well. Infrastructure This foundational layer allows for quick, streamlined, low-risk deployment of the cloud implementation. The plug-and-play, vendor-neutral framework is unique to Booz Allen. Infrastructure Source: Booz Allen Hamilton 5

10 the infrastructure. With the Cloud Analytics Reference Architecture, the infrastructure becomes a stable platform to support all aspects of cloud computing. With the top-to-bottom flexibility of the Cloud Analytics Reference Architecture, organizations do not need to continually rebuild and reconfigure their infrastructure. Their initial investment in infrastructure is both enduring and cost-effective. The Data Lake With the conventional approach, the computer finds information by looking in a particular database. With the data lake, information is located in an entirely different way by tags, or details that have been embedded in them for sorting and identification. For example, an investor s portfolio balance (the data) is generally stored with identifying information such as the name of the investor, the account number, one or more dates, the location of the account, the types of investments, the country the investor lives in, and so on. This metadata is what gets tagged, and is located by the computer during inquiries. The tags themselves are also a way of gaining knowledge from the data. In the example above, the tags might allow us to look for, say, connections between investors countries and their types of investments. The basic data the portfolio balance might not even be part of the inquiry. Such connections can be made with the conventional approach, but only if the custom-built databases and computer analytics have already been designed to take them into consideration. As illustrated in Exhibit 4, with the data lake, all of the data, metadata and identifying tags are available for any inquiry or search for patterns. And, such inquiries or searches can pivot off of any one of those pieces of information. This greatly expands the usability of the data available to an organization. It actually makes big data even bigger. In addition, the data lake smoothly accepts every type of data, including unstructured data information that has not been organized for inclusion in a data base. An example might be the doctors and nurses notes that accompany a patient s electronic health records, or information from social networking sites. Exhibit 4 Data Management Architectural Model Human Insights and Actions Analytics and Services Batch Structured Unstructured Source: Booz Allen Hamilton 6

11 Two other critical emerging data types are batch and streaming. Batch data is typically collected on an automated basis and then delivered for analysis en masse for example, the utility meter readings from homes. Streaming data is information from a continuous feed, such as video surveillance. Much of the flood of big data is unstructured, batch and streaming, and so it is essential that organizations have the ability to make full use of all types. With the data lake, there is no second-class or third-class data. All of it, including structured, unstructured, batch and streaming, is equally ingested into the data lake, and available for every inquiry. This mass of information is not random and chaotic, but rather is purposeful. The data lake is like a viscous medium that holds the data in place, and at the same time fosters connections. Because the data is all in one place, it is, in a sense, all connected. As an example, cybersecurity experts trying to identify internal and external network users most likely to steal information and commit fraud might consolidate a broad range of disparate information into a data lake. In addition to unstructured information about individuals from social media sites, the data lake could include thousands of public records sources, from bankruptcy and criminal histories to trajectories of zip codes. These might show out-of-the ordinary improvements or declines in personal finances (possibly indicating that an individual has committed fraud or is in dire straits and may have motivation to do so). The data lake might also include users computer behavior, enabling the analytics to look for anomalies. Is the customer or employee staying on the network far longer than usual, or visiting new and different parts of the network, or engaging in activities uncharacteristic of prior use? With conventional methods, each potential data source would have to be examined separately and the results would be difficult to integrate. A data lake would remove these constraints, making it possible for the analytics to look for patterns and connections in all of the available data at once, and to compile sophisticated risk scores on every internal and external user of the system. The Analytics The data lake supports a two-step process to analyze the data. In the first step, the pre-analytical tools filter and organize information from the data lake. That sets the stage for computer analytics in the next layer up to search for valuable knowledge. Extracting the Data Pre-analytics use the metadata tags to locate the relevant data from the data lake and give it an underlying organization. For example, in the collaboration between Booz Allen and the Midwest hospital system, the electronic health records of more than a thousand previous patients with serious infections were ingested into a version of a data lake. Special pre-analytics pulled out the patients vital signs, and then using the time-and-date stamps embedded in the records organized them in chronological order. That enabled analytics, in the next step, to search for patterns in the way the patients vital signs changed over time. Although pre-analytical tools are commonly used in the conventional approach, they are typically part of the rigid structure that must be torn down and rebuilt as inquiries change. Because such work is resourceintensive, only a limited number of such tools can be built, severely hampering an organization s ability to make full use of its data. By contrast, the pre-analytics in the Cloud Analytics Reference Architecture are designed for use with the data lake, and so are not part of a custom-built structure. They are both flexible and reusable, giving organizations almost endless windows into their data. Moreover, they are designed to be interoperable from the moment they come on-line, creating a set of easily shared services for all users of the data. 7

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13 Exhibit 5 Analytics and Services Architectural Model Human Insights and Actions Time Series Social Network Analysis R, SAS, Matlab, Mathematica MapReduce, Hive, Pig, Hama Source: Booz Allen Hamilton Finding Connections and Patterns Once the data has been prepared by the pre-analytics, the search for knowledge and insight can begin. As with the other elements of the Cloud Analytics Reference Architecture, computer analytics are used in an entirely new way (see Exhibit 5). Two key types of analytics are: Ad hoc queries. These are the analytics that ask questions of the data. While in the conventional approach the analytics are part of the narrow, custom-built structure, here they are free to pursue any line of inquiry. Machine learning. This is the search for patterns. Because all of the data is available at once, and because there is no need to hypothesize in advance what patterns might exist, these analytics can look for patterns that emerge anywhere across the data. Giving Computers More Work A key feature of the Cloud Analytics Reference Architecture is that it allows computers to take over much of the work humans are doing now. Conventional methods require that people play a large role in processing the data including selecting samples to be analyzed, creating data structures, posing hypotheses, and sifting through and refining results. That intense level of effort may be workable for small amounts of data, but no organization has the personnel or resources to use such methods to process big data. The Cloud Analytics Reference Architecture solves this problem by giving a great deal of that work to the computers, particularly tasks that are repetitive and computationally intensive. This reduces human error, and substantially speeds up the work. When we use the Reference Architecture to pose more intuitive questions, or to find patterns, we are essentially asking 9

14 the computer to take us as close as it can to finding the answers we want. It is then up to us, using our cognitive skills, to find meaning in those answers. By separating out what the computer can do the analytics and what only people can do the actual analysis the Cloud Analytics Reference Architecture greatly eases the human workload. It is a division of labor that frees subject matter experts to look at the larger picture. At the same time, the Reference Architecture rapidly highlights areas that analysts should not waste their time exploring enabling them to focus their resources in the right direction. For example, agencies that investigate consumer complaints against financial institutions often do not know which individual complaints are indicative of a broader pattern of consumer abuse, and so deserve the most attention. Investigators rarely have the time to sort through the vast array of sources that might provide valuable clues, such as blogs and social media sites where consumers commonly air their grievances. With a data lake that included all such available information, the Reference Architecture s analytics could quickly identify patterns, such as consumer abuse affecting large numbers of people. Investigators could then focus their resources on the most serious cases. Security of the Data In its ability to integrate disparate data sources, the Cloud Analytics Reference Architecture makes it possible for organizations to easily share information, confident that security, privacy and other rules governing the data will be strictly maintained. With the conventional approach, the primary obstacle to information-sharing is not technology, but rather the concern that secure information will be compromised. Investigative agencies, for example, worry that confidential sources will be inadvertently revealed. Hospitals and doctors are concerned that patient privacy will be violated. Those concerns go away with the Cloud Analytics Reference Architecture. As information is put into the data lake, the relevant restrictions, authorities, and security issues are tagged. All or portions of documents are tagged as well, indicating the security and privacy levels of specific information. Using these tags, organizations can establish rules regarding which information can be shared, with whom, and under what circumstances. If new information agreements are instituted, organizations do not need to re-tag the data they simply change the rules regarding the tags already in place. The security of data in the Cloud Analytics Reference Architecture has been proven to work in very secure environments within the US government, where the highest levels of precision in security and privacy are required. Visualization and Interaction Decision makers may be understandably concerned that big data will be overwhelming, and lead to information overload. Quite the opposite is true. The Cloud Analytics Reference Architecture addresses the issue head-on by incorporating the visualization how the knowledge is presented to us into the analytics from the outset. That is, the analytics not only conduct the inquiries, they help contextualize and focus the results. This enables analysts to more easily make sense of the information, to frame better, more intuitive inquiries, and to gain deeper insights. Building the visualization into the analytics has another advantage it provides the ability for quick and effective feedback between the two layers, so that the presentation of the findings can be continually refined for the decision maker. The visualization tools also make it possible for different organizations to tailor how they see the same data. For example, two agencies of the Department of Homeland Security Immigration and Customs Enforcement (ICE) and Customs and Border Protection (CBP) may want to visualize certain data in their own way. ICE, which has an investigative focus, might prefer that the visualization show how individuals are connected to one another. CBP, which is interdiction focused, may want the same data displayed geographically and temporally, to understand where 10

15 Exhibit 6 Human Insights and Actions Architectural Model Monitoring Exploratory Geospatial Line Chart Analytics and Services Source: Booz Allen Hamilton and when most activity occurs. The Cloud Analytics Reference Architecture easily accommodates both views of the data and any number of others, as illustrated in Exhibit 6. Another important breakthrough is that analysts, or subject matter experts, can explore the data without the need for computer experts to serve as intermediaries. Because of the high level of computer expertise needed to design custom data storage structures and analytics, much of the analysis in the conventional approach is conducted by computer scientists, computer engineers, and mathematicians acting as agents for the subject matter experts. They are typically the ones who translate the overall goals of the business and government analysts into the language of the machine. Whenever there is a middleman in any field, things tend to get lost in the translation, and data analysis is no exception. Here, it leads to a disconnect between the people who need knowledge and insight (the subject matter experts) and the data itself. It also substantially slows the process. In the top layers of the Cloud Analytics Reference Architecture, the middleman syndrome disappears. The ability to ask intuitive questions, and to look for patterns, provides the analysts with direct access to the data. That gives them the flexibility they need to experiment and explore, and allows the system to reach maximum velocity. The computer scientists, computer engineers and mathematicians still play a key role, but now are no longer the ones who drive the inquiries into the data. For example, investigators who suspect that credit card fraud may be occurring are often hampered by the need to go through computer experts to query the data. Their request may be one of many, and by the time they get back the information they need to act, the criminals have often made large purchases on the credit cards. With the Cloud Analytics Reference Architecture, however, investigators could query the data themselves, quickly pinpoint the fraud, and take action in time to stop the activity. Subject matter experts in other fields, such as financial analysts, medical researchers and policy experts, can have similar direct access to the data. 11

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17 With the Cloud Analytics Reference Architecture, the flood of information is not overwhelming it is readied for action as never before. This breakthrough in visualization could have as profound an effect on decision making as bar graphs and pie charts did in the 1950s and 1960s, when statistics became widely used in business. Those visuals presented all the essential information at a glance, changing the nature of decision making. The Cloud Analytics Reference Architecture will do the same but this time with big data. Conclusion The opportunities offered by the Cloud Analytics Reference Architecture will not emerge on their own conscious effort and deliberate planning are needed. Unless organizations make the right infrastructure decisions, they cannot hope to build a data lake. Unless they make the right data management decisions, they will never break free from the rigid data and analytic structures that are so limiting. The Cloud Analytics Reference Architecture can be seen as a road map for that decision making, one that shows the importance of a holistic, rather than piecemeal, haphazard approach. Each element is closely tied to each of the other elements, and so all must be considered together. The Cloud Analytics Reference Architecture is no more expensive to build than one based on the traditional approach, and is considerably more cost-effective in the long run. Because the elements of the Cloud Analytics Reference Architecture are largely reusable, they can scale an organization s big data in an affordable way. In this time of doing more with less, the Cloud Analytics Reference Architecture enables organizations to leverage the substantial investment the US government has already made in this area. Many of the same data challenges business and government organizations currently face are being successfully addressed by military and non-military agencies. Organizations now have an opportunity to take advantage of the advanced technologies and best practices that have led to that success. It is impossible to harness big data with approaches and techniques designed for small data. But by reimagining how data can be stored, analyzed and visualized, the Cloud Analytics Reference Architecture gives organizations a powerful tool to solve their most complex problems, and drive mission and business success. See our ideas in action at boozallen.com/cloud. 13

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19 About Booz Allen Hamilton Booz Allen Hamilton has been at the forefront of strategy and technology consulting for nearly a century. Today, Booz Allen Hamilton is a leading provider of management and technology consulting services to the US and international governments in defense, intelligence, and civil sectors, and to major corporations, institutions, and not-for-profit organizations. In the commercial sector, the firm focuses on leveraging its existing expertise for clients in the financial services, healthcare, and energy markets, and to international clients in the Middle East. Booz Allen Hamilton offers clients deep functional knowledge spanning strategy and organization, engineering and operations, technology, and analytics which it combines with specialized expertise in clients mission and domain areas to help solve their toughest problems. Contacts Josh Sullivan, Ph.D. Vice President Jason Escaravage Principal Peter Guerra Senior Associate The firm s management consulting heritage is the basis for its unique collaborative culture and operating model, enabling Booz Allen Hamilton to anticipate needs and opportunities, rapidly deploy talent and resources, and deliver enduring results. By combining a consultant s problem-solving orientation with deep technical knowledge and strong execution, Booz Allen Hamilton helps clients achieve success in their most critical missions as evidenced by the firm s many client relationships that span decades. Booz Allen Hamilton helps shape thinking and prepare for future developments in areas of national importance, including cybersecurity, homeland security, healthcare, and information technology. Booz Allen Hamilton is headquartered in McLean, Virginia, employs approximately 25,000 people, and had revenue of $5.86 billion for 12 months ended March 31, Fortune has named Booz Allen Hamilton one of its 100 Best Companies to Work For for eight consecutive years. Working Mother has ranked the firm among its 100 Best Companies for Working Mothers annually since More information is available at (NYSE: BAH) 15

20 Principal Offices Huntsville, Alabama Sierra Vista, Arizona Los Angeles, California San Diego, California San Francisco, California Colorado Springs, Colorado Denver, Colorado District of Columbia Orlando, Florida Pensacola, Florida Sarasota, Florida Tampa, Florida Atlanta, Georgia Honolulu, Hawaii O Fallon, Illinois Indianapolis, Indiana Leavenworth, Kansas Aberdeen, Maryland Annapolis Junction, Maryland Hanover, Maryland Lexington Park, Maryland Linthicum, Maryland Rockville, Maryland Troy, Michigan Kansas City, Missouri Omaha, Nebraska Red Bank, New Jersey New York, New York Rome, New York Dayton, Ohio Philadelphia, Pennsylvania Charleston, South Carolina Houston, Texas San Antonio, Texas Abu Dhabi, United Arab Emirates Alexandria, Virginia Arlington, Virginia Chantilly, Virginia Charlottesville, Virginia Falls Church, Virginia Herndon, Virginia McLean, Virginia Norfolk, Virginia Stafford, Virginia Seattle, Washington The most complete, recent list of offices and their addresses and telephone numbers can be found on Booz Allen Hamilton Inc E

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