Customer-Centric Opportunities in Big Data

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1 Customer-Centric Opportunities in Big Data

2 Customer-Centric OPPORTUNITIES in BIG DATA Companies across most every commercial sector are scrambling to capitalize on opportunities created by the massive wave of Big Data (see chart 1). With total data growing at 40 percent per year (EMC), firms as diverse as IBM, Citigroup, Amazon and Wal-Mart have all made managing and analyzing Big Data central to their strategies. The potential payoff is so great that Splunk, a start-up helping companies make sense of troves of machine-generated data, had an IPO in April 2012 that valued the company around $2.5 billion on just $120M in revenue. The various missions of the US Government are similarly affected by Big Data forces. Chart 1 From the Department of Defense to the Department of Homeland Security, from the National Weather Service to Medicare and Medicaid, federal customers already hold some of the largest data repositories in the world. As these data stores explode in both volume and complexity, federal agencies face an urgent and unprecedented need to capitalize on opportunities to manage and use available data effectively. The recent White House Big Data R&D initiative recognizes the challenge and is devoting $200 million specifically to helping agencies address Big Data challenges. (Endnote: White House Press Release, March 29, 2012.) But this is just a drop in the bucket of Big Data-driven opportunities. Many agencies lack the internal knowledge, talent, technology and data-driven culture necessary to cope with, much less fully exploit, the data they are mandated to manage. Firms that support any federal agency should therefore think carefully and creatively about how data volume and complexity will affect their customers. Defining Big Data Data Complexity: Four key variables describe the term: Volume: Size of individual files and data sets requiring analysis; growing quickly Variety: Type of files, focused on types that impact analysis capability needed Structure: Refers to the degree which data has been stored/managed to enable efficient use Velocity: The rate at which data changes or is added Analytic Complexity refers to the process of obtaining a decision based on existing data and has three important characteristics: Mathematical Complexity: Advanced statistical and math techniques required to generate useful insight Decision Cycle: The rate at which processing must occur and decisions must be made(real time to static) Subject Matter Expertise: The extent to which area knowledge must be included to interpret results Customer-Centric Opportunities in Big Data 1

3 Being proactive on how to help government customers address their Big Data needs can create multiple business opportunities and deepen trusted relationships. Conversely, hesitating to embrace or, worse yet, standing in the way of new data-driven approaches is likely to lead customers to bring in others who have more creative visions and more innovative answers for emerging data and analytic needs. Big Data provides a ready entry point for a host of non-traditional contractors Avascent suggests three initial steps for companies preparing to address Big Data and analytics opportunities and defend against emerging competitive threats: 1) Understand the Big Data Landscape 2) Think Ahead of Your Customer 3) Recognize Opportunities and Threats DEFINING THE BIG DATA AND ANALYTICS LANDSCAPE FOR GOVERNMENT CUSTOMERS With the volumes and types of data being collected, generated, stored and used changing daily, it is not surprising there is no single, accepted definition of Big Data or Advanced Analytics. The types and uses of data and analytics differ widely across all sectors, and especially the US Government: what is Big Data to the National Weather Service may not be to the NSA, the SEC or the IRS. At the same time, any company seeking to build a coherent approach to help Federal customers define and address emerging data and analytics needs must first understand the landscape. Customer-Centric Opportunities in Big Data 2

4 One way to do this is by constructing a high-level Big Data Opportunity Framework, which uses both data complexity (volume, variety, structure, velocity) and analytical complexity to identify four main groups of opportunities (see Chart 2): Traditional Analytics (I): Challenges involving relatively straightforward data sets for which the analytic toolsets are already well developed somewhere in the market. Although they are traditional, the challenge can be daunting: for example, the ongoing digitization and merging of DoD and VA health records. Advanced Analytics (II): Challenges where the data itself may not be overwhelming the customer, but where applying advanced analytic techniques and tools to existing data repositories could bring much greater value or mission fulfillment e.g., fraud detection by the SEC. Chart 2 Big Data Opportunity Framework Big Data Management (III): Where just keeping pace with the need to store, tag and process petabytes and exabytes of 3. Big Data Management Big Data Analytics increasingly complex data is the challenge Hadoop Architecture often involving investments in new systems Development and architectures for example, Unmanned Aerial System (UAS) video and sensor data. Big Data Analytics (IV): These are the hardest challenges faced by customers involving issues of both managing large volumes of heterogeneous data and the need to derive new meaning and insights from it for example, terrorist threat detection by National Security Customers. Data Complexity Complex Basic Data Set Development for Health Analytics 1. Edge Capture Control for UAVs Traditional Analytics Medicare Fraud/Waste/ Abuse Reduction Advanced Analytics Basic Complex Analytical Complexity 4. Decision Support Automation for Targeting Analysis Cost Savings Analysis 2. THINKING AHEAD OF YOUR CUSTOMER: WHAT DO THEY REALLY NEED TO PERFORM THEIR MISSION EFFECTIVELY? Regardless of the type of challenge faced, it is important to remember that solutions must be tailored to support the customer s specific mission needs. To effectively serve a customer requires a contractor to understand their mission objectives, define in detail the associated data and analytics opportunities and challenges (ideally before the customer does), and to have knowledge of and access to current and emerging Big Data technologies and tools. Customer-Centric Opportunities in Big Data 3

5 Examples of opportunities include: Traditional Analytics - Data Set Development for Healthcare Analytics: Government customers responsible for healthcare data are increasingly being pressured to standardize records, cut costs and provide rapid, useful data outputs. While the problem is significant, much of the solution can be found in selecting from existing strategies and processes for data collection, organization and storage and implementation of supporting information systems often involving commercially available analytic tools. The MHS-VA joint iehr program is working on the Cherry Architecture to collect necessary healthcare data. Advanced Analytics Medicare Fraud, Waste and Abuse Reduction: Medicare, and all Federal programs disbursing funds, faces the constant problem of fraud, waste and abuse. The data management requirements may remain relatively static, but the tools, techniques and experience applied to prevention and detection remain works-in-progress. Customers able to use, tailor and apply new analytics tools and subject-matter knowledge to their specific situations would gain immediate value and quick payback on their investment. Big Data Management Opportunities - Edge Capture and Control and Edge Analytics for UAVs: Video files are among the largest and most complex data collected by the Government. As the volume of image-driven data captured by Unmanned Aerial Systems continue to grow, balancing the capture and storage requirements is an ongoing challenge. Suppliers able to identify solutions for tagging and manipulating non-urgent data for later analysis, while keeping urgent data available for rapid analysis, would solve a longstanding urgent need with multiple additional opportunities available. Big Data Analytics Opportunities - Decision Support Automation (e.g., Targeting Analysis): A separate but related mission need combines both Big Data management and Advanced Analytics. Thousands of analysts are currently required to support key counter-terrorism missions. Many of these are performing important but tedious and error-prone monitoring tasks. The ability to automate even a small portion of these missions would result in tremendous cost savings to national security customers. But it is not easy. Automated systems must be able to handle large amounts of diverse data and support rapid analytic timeframes, and account for still-immature strategy and mission system architectures. RECOGNIZE OPPORTUNITIES - AND THREATS These are just a few examples that highlight the importance of understanding existing and potential customer data needs in this very dynamic market and in being proactive about creating and shaping related opportunities. Customer-Centric Opportunities in Big Data 4

6 Conversely, however, Big Data creates real threats to existing providers: Potential Disruption to Human Capital Intensive Analytical Processes: The possibility of replacing (or at least reducing) large numbers of services contractors with lower-cost, automated tools and processes can be Big Data s most appealing aspect to some customers. This emerging shift is specifically threatening to traditional labor-intensive business models of some current contractors. For incumbents with both deep IT capabilities and large services businesses, this presents a difficult decision on whether to embrace or fight developments that may cannibalize their existing services businesses. New Competitors Mastery of Analytics May Undermine the Traditional Mission Intimacy Advantage of Traditional Contractors: The value of an incumbent s deep knowledge of a customer s mission and operations may be diminished as analytics specialists are able to grow critical customer relationships and mission insight through the strength of their technological offerings. This was one reason Palantir, NetApp and SAS quickly established themselves in the Federal market. The movie Moneyball provides an apt analogy for how deep analytics can quickly render the inherited wisdom of traditional approaches obsolete. Trapped Capabilities that must be unlocked through effective Organizational Management Perhaps the hardest part of developing an effective Federal Big Data strategy is the organizational challenge. Most Federal contractors actually have strong data analytics and big data capabilities and qualifications. However, these often reside within disparate business units or teams. A clear strategy for identifying relevant capability across the entire organization and delivering it to the right customers is a central management challenge. For example, how well is your organization able to harness the Big Data qualifications across Navy-facing organizations and commercial groups to meet an upcoming IRS requirement? Federal contractors need to move quickly to define their particular approach and market position to address the many Big Data opportunities and threats. By their very nature, Big Data problems are often massive and complex, and choosing a path forward is difficult. However, better understanding the market, customers specific needs, as well as competitive and internal threats can help put companies on a path to success. Key Points Big Data represents a big opportunity for federal contractors: Understanding the nature of the opportunity is key Now is the time to think strategically about Big Data or risk falling behind new entrants and traditional contractors Customer-Centric Opportunities in Big Data 5

7 ABOUT THE AUTHORS Chris Meissner is an Associate at Avascent specializing in growth strategies for technology companies operating in government driven markets. Chris has deep expertise if a range of high technology markets, including: cyber, intelligence and classified markets, data analytics, cloud computing, data center consolidation, border security and related defense and homeland security markets. In over 75 projects with Avascent, Chris has advised systems integrators, technology companies and their financial sponsors on identifying and capturing opportunity in these highly distinct markets. Chris work played a key role in developing the growth strategies of over a dozen leading cyber providers in the federal market. Chris is a graduate of Georgetown s Walsh School of Foreign Service and The George Washington University (summa cum laude and Phi Beta Kappa). Arun Sankaran is an Associate at Avascent focusing on high technology markets, including: big data, cyber, biometrics, enterprise management systems, IT infrastructure and related markets. In over 60 projects with Avascent, Arun has supported strategic plan development, adjacent and core market analysis, M&A strategic diligence and go-to-market planning for systems integrators and technology firms. Prior to joining Avascent, Arun was an IT consultant with CGI Federal where he implemented enterprise management systems for Federal Civilian agencies including the Department of Justice, Department of Treasury and Environmental Protection Agency. As a result, Arun brings a deep understanding of technology to Avascent client engagements. Arun holds a Master of Public Policy degree from Georgetown University and dual Bachelor of Science degrees in Business Information Technology and Economics from Virginia Tech. ABOUT AVASCENT With offices in Paris and Washington, DC, Avascent ( is the leading strategy and management consulting firm serving clients operating in government-driven markets. Working with corporate leaders and financial investors, Avascent delivers sophisticated, fact-based solutions in the areas of strategic growth, value capture, and merger and acquisition support. With deep sector expertise, analytically rigorous consulting methodologies, and a uniquely flexible service model, Avascent provides clients with the insights and advice they need to succeed in complex market environments. US Office 1615 L Street, NW, Suite 1200 Washington, DC Tel: Europe Office 59, rue des Petits Champs Paris Tel: Customer-Centric Opportunities in Big Data 6