Navigating Government s Big Data Journey. Secure, Effective Public Sector Strategies

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1 Navigating Government s Big Data Journey Secure, Effective Public Sector Strategies

2 Navigating Government s Big Data Journey Secure, Effective Public Sector Strategies 01 Getting Started on a Big Data Strategy 02 Making 24/7 Data Analytics Real 03 Keeping Big Data Secure 04 Delivering Agency Results with Pervasive Analytics 05 Continuing Your Big Data Strategy

3 CHAPTER 1 Getting Started on a Big Data Strategy How to Use Big Data to Anticipate Future Needs Government agencies collect, create, and manage information as part of their core mission. This information takes on many forms, and when taken collectively, can be a powerful and useful resource for public and private sector consumers who leverage the data for nearly limitless purposes. Today, thanks in large measure to faster and more available tools and technologies, there is keen interest in tapping this big data and using it effectively. To this end, federal, state, and local governments are evaluating methods to best use their information assets, improve their decision-support capabilities, and address larger, more diverse data sets and types than previously was possible. With insights from big data, public sector enterprises are better positioned to meet mission requirements, anticipate future needs, and provide optimized citizen services. Each agency must develop its own data-driven strategy to fit its specific mission and operational needs. And over time, as agency missions continue to evolve, so too must their strategies to collect, manage, and defend their data resources. Traditionally, agencies have operated on information that is effectively stored in data silos. If data sits in silos, it can take considerable time and effort for the end-user to explore, analyze, and bring together relevant insight from each individual collection; the analyst assumes the responsibilities of data collection, correlation, and production across these silos, an errorprone and clumsy process at best. Today, by implementing an enterprise data hub (EDH) approach, powered by the open-source Apache Hadoop platform, government professionals Navigating Government s Big Data Journey Getting Started on a Big Data Strategy 3

4 As more devices and systems are connected 24/7 and are engaged in self-reporting, we are entering a new environment in which organizations can afford to collect and continually analyze their data to improve performance, security, and resource allocation.

5 are able to bring together more data, quickly, to address whatever question the immediate mission requires. Using an EDH, workloads become faster to comprehend and more types of information can be analyzed in near real-time. This technology has enabled an accelerated and deep transformation of the agency in which data analytics help to inform decisions across an entire organization, and where analysts can convene past, present, and future-looking analytics across the entire workflow. These analytics leverage an EDH to deliver a greater understanding of operational realities, not only for situational awareness today but also for planning for future needs and anticipating likely outcomes. As an example, the expanding Internet of Things has spawned oceans of new data, and with it, unprecedented opportunities to access and analyze data to enable more current and comprehensive views. As more devices and systems are connected 24/7 and are engaged in self-reporting, we are entering a new environment in which organizations can afford to collect and continually analyze their data to improve performance, security, and resource allocation. These analytics give operators a full view of both historical and real-time data, helping them to make recommendations and predict actions. Really, the adoption of this approach to analytics pervasive analytics is just as much a mindset change, as it is a technology change. To harness this data, agencies need to create and follow a comprehensive strategy that helps the organization determine how to navigate information assets in a secure and optimized way. Especially in government, obtaining actionable information from available data has yet to become a seamless process, and in many instances, the public sector lags behind data strategies in place across the private sector. Many agencies are embarking on the journey to develop and deploy their data strategies while considering the potential for applying data analytics, and the value it can and will deliver to their missions in the future. According to Gartner, 73 percent of organizations are either using or planning to adopt big data platforms and analytical tools to better their operations, outcomes, and opportunities. Organizations throughout the public sector are already employing data analysts to better understand enterprise information resources and use derived knowledge to support their mission requirements. NASA, a leader in open data and analytics, is processing enormous amounts of weather data to forecast the impacts of climate change. Various organizations throughout government are proactively identifying and mitigating insider threat behaviors through continuous monitoring and predictive, behavioral analytics. And, in the public health arena, analytics are being used to improve diagnostic techniques and patient care. For instance, Children s Healthcare of Atlanta, Navigating Government s Big Data Journey Getting Started on a Big Data Strategy 5

6 a leading pediatric health care facility, used data analytics to drive a bedside alarm study that helped to retrain hospital staff. The study assessed neonatal pain, agitation, and sedation scale (N-PASS) scores, putting information in the hands of clinicians, so they could improve pain management outcomes for premature babies. Each chapter of this e-book will focus on an essential element for development of a comprehensive data strategy Chapter 2 Making 24/7 Data Analytics Real How real-time data analytics support mission-critical objectives. Chapter 3 Keeping Big Data Secure Compliance and security measures necessary for effective EDH environments. Chapter 4 Delivering Agency Results with Pervasive Analytics How to connect data assets, decision-support, and outcomes. Chapter 5 Advancing Your Big Data Strategy Looking ahead for design and implementation of an enterprise data hub. In today s hyper-connected world, where more data is created every day, businesses and government need to find real value in data, says Webster Mudge, Senior Director of Technology Solutions at Cloudera. It s the key to effective discovery, analysis, and understanding. While the advantages of a data-centric approach to government are obvious, it can be easy for agencies to get lost along the way. Often government leaders find themselves wondering: What steps should I take when first starting to use an enterprise data hub solution? What resources are available to support data strategy evolution? What are the roadblocks to developing a comprehensive data strategy? How can I realize a pervasive analytics approach for meaningful decision-support? What should I prioritize when it comes to data security? This e-book will help to answer these questions and expand upon how selected agencies are using an EDH to give predictive, operational, and historical insights into daily workloads and longer-term mission support requirements. Big data operations and analysis, deployed thoughtfully, empower better decision-making. At the core, continuing on your big data journey means developing a data strategy that can support a comprehensive and meaningful analytics-driven environment. Cloudera makes pervasive analytics a reality for many government organizations, and it s time to unlock this capability in your agency. To get started, visit Cloudera Government and learn how a data strategy can support decisionmaking and drive results for your agency. Navigating Government s Big Data Journey Getting Started on a Big Data Strategy 6

7 Chapter 2 Making 24/7 Data Analytics Real If you re looking at data five minutes behind, you re six minutes late. Agencies today are using 24/7 data analytics to support mission-critical objectives. Creating data-driven environments are especially important when it comes to supporting defense operations that demand timely and trusted collaboration, information-sharing, logistics management, and heightened security measures. Examples include the security arena, where instilling a data-driven culture and embedding data analytics into all segments of the workflow are key to responding to the constantly changing threat landscape. Increasingly, agency leaders rely on data analytics to help them identify how to meet longer-term mission and business goals, including expanded opportunities for automation, potential areas for cost savings, simplified processes and operations, and streamlined internal and external communications. In the defense community, leaders such as the Defense Information System Agency s (DISA) David Bennett, rely on real-time data to support warfighter operations on a global scale. As Director of DISA s recently formed Implementation and Sustainment Center (ISC), Bennett uses analytics to guide the agency s 24/7 communications and computing operations. DISA has an estimated 1.6 million customers using its enterprise system, with approximately 200,000 users working on classified networks. To deliver communications capabilities to a global user community, ISC is focused on ensuring service that is trusted, reliable, and easy-to-use. Given the size and complexity of these environments, people alone cannot manage these communication networks, Bennett says. In response, his organization uses automation tools to support long-term sustainment of mission-critical platforms, including communications networks. Technology evolution is making it possible to automate applications that use more data sources than ever before. Defense agencies can be inundated with data generated by diverse systems, such Navigating Government s Big Data Journey Making 24/7 Data Analytics Real 7

8 as those supporting supply chain management, security surveillance, communications platforms, and remote sensors. The real challenge, Bennett says, is to make sense of all of this data and develop methods and analytics to use it for specific programs and mission requirements. Bennett says his organization leverages big data analytics to indicate when network issues arise in communication networks, such as . He notes the right analytics are those that are constantly changing and adapting to the environment or the threat. He summarized the challenges of doing this in real-time as follows, if you re looking five minutes behind you... you re six minutes late. When it comes to the ISC s most critical work, Bennett has two primary goals: First, to sustain operations and communications in the field; and second, to reduce costs through smarter application of technology and computing strategies that align resources with evolving DISA mission objectives. In January 2015, DISA announced an agency-wide reorganization to enable the delivery of communications capabilities faster and meet the Director s overall objective to be more timely and responsive to the needs of the warfighter. When it comes to the ISC s most critical work, Bennett has two primary goals: First, to sustain operations and communications in the field, and second, to reduce costs through smarter application of technology and computing strategies that align resources with evolving DISA mission objectives. To achieve these goals, departments have been brought closer together and the work of breaking down organizational information silos has begun. Bennett and his team are taking a data-centric approach that puts essential infrastructure and computing platforms under one data system to support integration and more informed decision-making. So what you have now is the ability to optimize from an agency perspective and have organizations who have the ability to drive change, Bennett says. I ve torn down the barriers that were built up over time. We re now looking at it through a different set of lenses, and we re now looking at it from the perspective of the horizontal view versus a vertical view. This makes it much easier for agencies to put in place an automated process that can save time, money, and resources. Government leaders are particularly excited when considering the possibilities that technology brings to processing big data, Bennett says, but inevitably the use of new technology means change. And, the public sector is not always willing to adopt change, according to Bennett. DoD is not a big change agent. We are slow. We are cautious and if you expect us to move at any sort of measurable speed, good luck with that, he says. Yet every time we talk about new technologies, that s exactly what happens. It s a change to the environment. Navigating Government s Big Data Journey Making 24/7 Data Analytics Real 8

9 For mission-critical communications, for example, success requires automation and removing potential errors from the equation. Automation is central to Bennett s work and should be built into any agency s focus from the start, he says. Navigating Government s Big Data Journey Making 24/7 Data Analytics Real 9

10 At DISA, new technologies have resulted in changes to operating systems, architectures, CONOPS, and more. At any point when a change is implemented, Bennett says, it also introduces the opportunity for error, potentially compromising a system capability. To address this operational reality, one of the key changes to support ISC mission support goals is the automation of networks. By leveraging automation, DISA can improve its monitoring capabilities of complex environments while reducing opportunities for failures in daily operations. Analytics help to drive automation by exposing issues in a timely fashion. This means pushing events and insights to the operator and enabling more reliable services at DISA. How much do we want to ensure that one area doesn t have a problem? Bennett asks. We have to ensure that our change process and our ability to manage things are sound and they re consistent. And that, more importantly, they re repeatable and we get people out of the loop. Success requires automation and removing potential errors from the equation. Automation is central to Bennett s work and should be built into any agency s focus from the start, he says. With 24/7 data analytics capabilities, DISA will enable decision-making that relies on a holistic, pervasive approach. The objective is to obtain actionable information from data that can help DISA leadership evaluate where changes may be needed. Strategies that optimize data for processing, such as an operational data store, built on a Hadoop-based enterprise data hub, eliminate the where and the how questions for employing effective data analytics and accelerate discovery and time-to-action. Having the right data tools is essential in Bennett s view. Big data is a cool scenario, he says. But you ve got to have the right analytics to go with it. And, you ve got to have the right analytics that are constantly changing and adapting to the environment, or to the threat, or adjusting to the questions that you re trying to answer. Navigating Government s Big Data Journey Making 24/7 Data Analytics Real 10

11 Chapter 3 Keeping Big Data Secure Tips on achieving security awareness Information security is a critical concern for government organizations today. For data analytics to be applied successfully, users must have confidence that the technologies and methods they use to collect, store, and exploit data resources also can be trusted to protect enterprise information. The number and type of cyber threats to agency data, networks, and IT infrastructures continue to grow at an alarming rate. Cyber incidents reported to the US-CERT by Federal agencies have escalated from just over 5,000 in 2006 to more than 67,000 in Agencies are struggling to design and implement effective cybersecurity programs that can match the tactics and scope of the expanding threat environment. The effort is so great that a recent report by the US Government Accountability Office stated that during Government Fiscal Year 2014, inspectors general at 22 of the 24 major federal agencies identified identified information security as a major management challenge for their agency. 1 Central to meeting this challenge is an underlying IT architecture that agencies can rely on to be trusted and compliant, yet flexible enough to meet constantly evolving cyber threats. Adopting next-generation architecture is a positive step toward providing this capability. For example, an enterprise data hub (EDH) built with and powered by Apache Hadoop is a productive means for organizations seeking to securely share, process, and analyze big data. By employing an EDH strategy, leading organizations are changing the way that they think about data, protecting it within the greater data management infrastructure. Yet at the same time, to leverage the real value of this data and its analytics, users must trust the integrity, operational security, and governance of an EDH environment. 1 page 243 Navigating Government s Big Data Journey Keeping Big Data Secure 11

12 To be successful, government leaders need to focus on building a secure foundation for Hadoop because, as an open-source software framework, Hadoop operates differently from legacy, closedsource platforms. Updates, modifications, and improvements are continually released, providing users with frequent, innovative features, and security enhancements. Although the framework may operate differently from other platforms, many of the proven security guidelines and best practices for hardening system foundations apply. Agency professionals have the option to customize many effective security measures to their specific requirements, including turning off services that are not required, restricting user access, locking down network ports, using centralized corporate authentication, and enabling encryption in-transit. These intuitive controls can be used to track changes to Hadoop, maintaining effective security and governance without sacrificing access to the latest innovations for data analysis. In addition to being secure, Hadoop delivers flexibility that can help agencies comply with regulatory mandates, which foster industry security standards for protecting agency and personal information systems and data. In addition to being secure, Hadoop delivers flexibility that can help agencies comply with regulatory mandates, which often follow industry security standards for protecting agency and personal information systems and data. Data encryption, periodic risk assessments, and security awareness training are all key components of an effective compliance program, but each program has its own specific requirements and application, which can pose challenges to IT and information security teams who need to manage multiple, disparate systems. Achieving compliance is especially important for agencies handling personally identifiable information (PII) and protected health information (PHI), where a security breach may lead to compromise of individual identity and privacy. Public sector operations change all the time, which is why the Hadoop platform is such a great fit. It s scalable, flexible, and secure, says Eddie Garcia, Chief Security Architect at Cloudera. Keeping in mind just a few key and critical questions will go a long way towards ensuring your agency s data is accessible and safe. Throughout this process, it s important for agency professionals to keep in mind that security is not only about technology, but also must address the relevant people and processes that interact with the data and the systems that host them. Developing strong data governance practices and ensuring personnel undergo regular and comprehensive security training can significantly reduce exposure to risky individual and organizational behavior and help avoid numerous security incidents. You must promote a security culture as an enabler and resolve any challenges that may arise, like performance, Garcia says. You have to encrypt data while maintaining high performance in your workforce. Ultimately, achieving big data security and trusted analytics requires a comprehensive understanding of the data and the systems used to collect, protect, and manage it. Navigating Government s Big Data Journey Keeping Big Data Secure 12

13 Ask these five essential security questions to reconfigure and build a unified platform for big data How do I prepare a secure foundation for my enterprise data hub? Is there perimeter security in place? What type of security regulations must my agency meet? Who are the needto-know users on the Hadoop platform? How do I monitor and audit Hadoop security after it goes-live? To prepare a secure foundation, take a close look at user access, superuser permissions, network ports and protocols, encryption in-transit, and centralized authentication. Establishing perimeter security is important because it restricts users by requiring entry through a secure gateway, over secured networks, and with approved credentials. Just as agencies need multiple data sources and multiple frameworks to truly instill a data-driven workflow within their organizations, government leaders need a secure network system that is agile and can handle a variety of workforce needs. When it comes to compliance, there are those that have to be compliant, and those that want to follow compliance guidelines. Depending upon the agency, compliance can vary. These guidelines establish differentiation, mitigate risks, and promote a culture and mindset of security. There are sub-groups and divisions built into larger agencies, and with increased organizational complexity comes increased difficulty in monitoring and accessing data. The power to bring data together, like that of a Hadoop-powered EDH, also comes with a challenge: Who are the need-to-know users within a larger organization that require access to key and critical data? Auditing your Hadoop platform is the final key piece of an effective and secure data practice. Simply running a data audit and indexing data can help inform you of new data and security permissions and policies that need to be applied. Navigating Government s Big Data Journey Keeping Big Data Secure 13

14 In summary, meeting today s critical infrastructure threats can be enhanced by employing an EDH approach that is secure, compliant, and flexible. It has the core features that Federal IT professionals can employ with confidence while increasing organizational capabilities to process and analyze its mission-essential data. Navigating Government s Big Data Journey Keeping Big Data Secure U.S. Navy photo by Mass Communication Specialist 2nd Class Christopher Lindahl/Released

15 Chapter 4 Delivering Agency Results with Pervasive Analytics Practical insights into developing relevant analytics Without analytics, data is little more than an enormous collection of records, and without this context, data has minimal value for government agencies. It is with analytics that government leaders can bring together big data and its disparate data types and sources to formulate actionable information as never before and provide this needed context. But, how can large enterprises with a complex range of data systems and sources tackle this kind of analysis? Answer: By designing and implementing a pervasive analytics strategy. With this strategic framework in place, enterprise data can be used to inform decisions by incorporating historical, current, and future data points and insight into their processes. A recent Forrester Research survey reported that a majority of IT executives and leaders 57 percent ranked data-related projects as their first or second priority for new investment. Decision-makers increasingly appreciate the value of data analytics to help them understand the norms and exceptions to daily operations and to meet business and mission objectives. Data analytics have gone well beyond the review of historical reports and single-purpose dashboards. Instead, analytical methods and tools today allow data scientists and program management professionals to create inquiries and correlate data for more complex and revealing comparisons. These reports lead to insights previously left unexplored by traditional tools and approaches. For example, predictive analytics employ techniques from statistics, modeling, machine learning, and data mining, and can be used by public and private sector organizations to anticipate future needs, trends, and operational outcomes. With this type of approach, government professionals can make Navigating Government s Big Data Journey Delivering Agency Results with Pervasive Analytics 15

16 Listen to the Data-Driven podcast series By developing a data strategy to drive pervasive analytics, government agencies can obtain information of unprecedented quality and derive valuable, actionable insights. For more information on how to get started, tune into Cloudera s Data-Driven podcast series available online. What are the types of data analytics? Traditional Analytics Traditional analytics make use of historical data and follow processes where the user decides which data is important. With traditional analytics, a model is created based on a hypothesis and then tested, and the data analytics process requires manual assembly of the data selected. Predictive Analytics With predictive analytics, the user removes preconceived notions of what data is important. In essence, the data speaks for itself, and in this capacity an analyst runs all relevant data and builds a predictive model. The data is parsed to provide answers and machine triage focusing on the highest value data insights. Predictive analytics provides agencies with information that cannot be easily discovered using traditional analytics. Real-time Analytics Real-time data analytics, as the name suggests, examines current and immediate data for timely insight, yet is useful to continuously build predictive analytics models and generate up-to-date traditional analytics. And by nature, it s constantly changing. The analytics can come from sensor data and provide situational awareness that is actionable. Pervasive Analytics With pervasive analytics, organizations embrace a comprehensive data strategy that combines the power of real-time, predictive, and traditional analysis techniques. By bringing together these data analysis types, the requestor or analyst is able to make informed decisions, where the insights are both found by the user and surfaced by the application to the decision making process throughout the workflow. plans and invest in infrastructure, personnel, and other resources based on an informed forecast derived from their own operational data. Additionally, some agencies rely on real-time analytics, populated with data collected from a host of remote sensors. This kind of decision-support enables timely adjustments and actions by agency officials, such as first responders and public safety officials, as events develop. And, as the Internet of Things continues to expand and mature, organizations are exploring more sophisticated ways to evolve their business processes and mission capabilities using the available data as the fuel. From this environment this array of analytic and data capabilities emerges the strategy of pervasive analytics. Pervasive analytics is not a strict definition of computing, but rather the mindset of an operator and a data-driven culture at an agency. This mindset embraces and fosters the capture and use of any and all data to provide the right information at the right time. Think of pervasive analytics as the strategy and business process of embedding analytics past, present, and future-looking into the very fabric of the agency, helping to shape daily workflows and support mission accomplishment at every point within the knowledge chain. Federal agency professionals increasingly rely on this kind of analytical thinking to optimize funding, technology, and personnel resources. It is only through a strategy of pervasive analytics that agencies are able to turn volumes of data that they create, collect, store, and manage into practical, useful data-driven applications for all users and constituents. But how are analytics used in practice to achieve the mindset of pervasive analytics? Consider the following industry perspectives and recommendations on how to leverage analytics in an effective enterprise data management strategy: Move from the what to the why The power of analytics is enormous and it is important to look beyond the initial analytic capabilities. Instead, agency professionals need to focus on what can be done to make their programs operate more effectively by asking: How can we make our data meaningful? The more you can provide the context as you develop your dashboards, the more actionable the information is, the more meaningful it is when you get to reporting, says Emma Antunes, Special Assistant to the Chief Information Officer, NASA Goddard Space Flight Center. Because that s the purpose of doing analytics to help you derive decisions, and to do things differently within your program. Develop analytics that fit your business model When investing in a pervasive analytics strategy, it is critical that agencies understand how these Navigating Government s Big Data Journey Delivering Agency Results with Pervasive Analytics 16

17 Pervasive analytics are deployed to provide the right information at the right time to the most appropriate recipient(s), helping to shape daily workflows and support mission accomplishment. Federal agency professionals increasingly must rely on this kind of analytical thinking to optimize funding, technology, and personnel resources. Navigating Government s Big Data Journey Delivering Agency Results with Pervasive Analytics 17

18 systems can help them meet mission requirements. I don t think a lot of organizations understand that when they pay $30 million for a license, or more, what exactly they re contributing to the bottom line of their mission, says Stephen Dennis, Innovation Director at the Department of Homeland Security. To address this concern, Dennis suggests agencies develop practical return on investment (ROI) statistics to enable agency leaders to effectively measure progress and ensure that information from data analytics efforts is helping meet their business and programmatic objectives. Understand your information needs The quantity of data available for analysis is massive, but analytics are only as useful as they are consumable. In order to move beyond production analysis to exploratory analysis, agency professionals first need to determine exactly what information they need to extract from their data. Then, they need to present and communicate the insights they gain in a manner that is easily digestible. Everybody has a different way of assimilating information and gaining something from it, says David Bennett, Director of the Implementation and Sustainment Center (ISC), Defense Information Systems Agency (DISA). We ve got to get to the point where in leveraging automation, we cannot only see, consume, and react to data faster. We also have to be able to visualize and display it in a way that s meaningful to the data consumer. Emphasize imagination and pragmatism When developing a data strategy for pervasive analytics, agencies must not inhibit the individual and collective imaginations of their workforce. As technology continues to develop, the seemingly impossible may be a reality in the relative near-term. That said, much of the existing agency IT infrastructure works extremely well and may not need to be replaced or reinvented when new complementary technologies are introduced. Leverage the infrastructure you have and take the pragmatic path to achieving what may be a very compelling and visionary dream, says Murthy Mathiprakasam, Principal Product Marketing Manager at Informatica. Navigating Government s Big Data Journey Delivering Agency Results with Pervasive Analytics 18

19 CHAPTER 5 Advancing Your Agency s Big Data Strategy Considering your enterprise data hub options in the cloud and the build vs. buy scenario The ultimate goal of any big data strategy is to bring about transformational change to the delivery of government information and services. To do this, it s important that agency professionals establish an approach to data management and analysis that meets their operational and business requirements. Government leaders need to be thinking about their big data strategy as it evolves. The primary question: How can an enterprise data hub (EDH) truly benefit my agency in the long run? An enterprise data hub has the flexibility to run a variety of enterprise workloads for government while enabling compliance with agency-specific security requirements. When combined with cloud computing, the resulting infrastructure extends far beyond the ability to provision infrastructure capabilities in an agile manner. It also provides numerous operational efficiencies, including the flexibility to run batch processing, interactive business intelligence, search, and advanced analytics together within the same infrastructure. As the public embraces Hadoop-powered big data deployments in the cloud, agencies also should be thinking about how to maximize the flexibility that comes with the adoption of Federalsupported cloud services. The ecosystem is quite large now. Cloud technology allows government to solve real world problems, says Robert Morrow, Senior Systems Engineer at Cloudera. And, data management run on cloudbased platforms can help with a whole subset of problems that government is faced with today. The first real advantage of using the cloud for enterprise data management is that it reduces the time Navigating Government s Big Data Journey Advancing Your Agency s Big Data Strategy 19

20 it takes to deliver data-driven insights. Cloud-based systems offer on-demand flexibility that leads to faster Hadoop deployment, effectively putting big data to work quicker. Cloud also streamlines processes such as infrastructure selection and procurement, giving agencies a quicker route to leveraging Hadoop. Flexibility is key here, Morrow says. One of the first challenges to figure out is what s the specific problem and how do you build to scale? Cloud-based systems offer ondemand flexibility that leads to faster Hadoop deployment, effectively putting big data to work quicker. Cloud also streamlines processes such as infrastructure selection and procurement, giving agencies a quicker route to leveraging Hadoop. A scalable data management strategy is critical for government. Without cloud services, it can take weeks or even months to add nodes to an existing data cluster, Morrow says. The challenge of learning how to optimally manage the volume, variety, and velocity of data is not going away anytime soon, which is why he suggests agencies implement an EDH-cloud approach. Equally important for an effective big data strategy is computing power. Cloud services can help agencies meet the processing demands of daily workloads and mission, and by moving data to the cloud, agencies have an alternative to an on-premise solution that may have higher upfront cost and longer lead time to implement. When conceptualizing an EDH solution, it s important for agencies to recognize that an EDH offers much more than data warehousing, Morrow says. There are a variety of storage, computing, security, compliance, and other options for an agency. Of course, a big data strategy is not static it should be evolving continuously. A data strategy can thrive only if analysts understand what s working, what s not, and what s needed to meet emerging mandates. Ultimately, an effective data strategy will lead to better operational efficiencies, improved security, and more customer-centric service delivery. An EDH provides the needed foundation for this strategy. Starting the Data Journey Developing Developing a comprehensive big data strategy has implications well beyond how technology processes are designed and managed. Government leaders often must address the question of whether or not to build or buy data management applications and systems. But, is this really an either/or question? According to U.S. Government Systems Architect, Skip McCormick, You don t really get to choose between build or buy. It s a lie... They re two sides of the same problem What we re really saying is do both. In the buy scenario, agencies rapidly take advantage of commercially available cloud-based storage. Cloud computing gives data managers the freedom to build to scale and process big Navigating Government s Big Data Journey Advancing Your Agency s Big Data Strategy 20

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