Business Strategy: Actionable Insights Emerge as Healthcare Organizations Invest in Big Data and Analytics

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1 Business Strategy: Actionable Insights Emerge as Healthcare Organizations Invest in Big Data and Analytics IDC Health Insights: Accountable Care IT Strategies BUSINESS STRATEGY #HI Global Headquarters: 5 Speen Street Framingham, MA USA P F Cynthia Burghard Dan Vesset IDC HEALTH INSIGHTS OPINION Big data and analytics (BDA) represent a $100 billion technology and services market. Healthcare represents an estimated 6% of that market. Widely publicized case studies and academic and commercial research on the market have increased, and these highlight the profitability and productivity advantage of healthcare organizations that rely on datadriven decision making. As the evidence mounts for the competitive edge demonstrated by these organizations, big data and analytics have become top agenda items for a growing number of executives. At the same time, hype about big data technology capabilities and inflated promises of outcomes abound. These ignore real challenges faced by healthcare organizations of all sizes. Managers in many healthcare organizations believe they have what it takes to harness the power of big data for improving data-driven decision making, yet they are missing the competency or maturity to address the range of technology, staffing, process, and data requirements involved. With the opportunity to unlock the value of big data to accelerate innovation, drive optimization, and improve compliance using data-driven decision making comes the need to demonstrate value, navigate expanding technology alternatives, recreate business processes, and ensure the availability of appropriately skilled staff. The ability to manage and analyze big data and derive value from these activities, as well as to measure and improve these capacities, will increasingly define an organization's ability to compete or service its constituents. IDC's Health Insights' Big Data and Analytics Maturity Model is a framework of stages, critical measures, outcomes, and actions required for healthcare organizations to effectively advance along the successive stages of BDA competency and maturity toward data-driven decision making. This maturity model will enable organizations to: Assess their BDA competency and maturity Use the baseline to define short- and long-term goals and plan for improvements July 2013, IDC Health Insights #HI IDC Health Insights: Accountable Care IT Strategies: Business Strategy

2 Prioritize BDA technology, staffing, and other related investment decisions Uncover maturity gaps among business units and between business and IT groups #HI IDC Health Insights

3 TABLE OF CONTENTS In This Study 1 Situation Overview 1 The Approach 5 Stages of the Big Data and Analytics Maturity Model... 9 Measures of the Big Data and Analytics Maturity Model Future Outlook 13 Short-Term Big Data and Analytics Trends Long-Term Big Data and Analytics Trends Essential Guidance 15 Actions to Consider Learn More 18 Related Research P 2013 IDC Health Insights #HI241962

4 LIST OF TABLES 1 IDC Health Insights' Big Data and Analytics Maturity Model: Overview of Maturity Stages IDC Health Insights' Big Data and Analytics Maturity Model: Overview of Measures by Stage IDC Health Insights' Big Data and Analytics Maturity Model Guidance P #HI IDC Health Insights

5 IN THIS STUDY IDC Health Insights' BDA Maturity Model will enable an organization to assess its BDA competency and maturity; use the baseline to define short- and long-term goals and plan for improvements; prioritize BDA technology, staffing, and other related investment decisions; uncover maturity gaps among business units and between business and IT groups; and benchmark BDA maturity against an industry benchmark all in the quest to data-driven decision making and to derive desired outcomes based on organizational goals. IDC Health Insights' BDA Maturity Model uses the framework of IDC's BDA Maturity Model. A cross-industry workgroup created a industry-neutral BDA Maturity Model (see IDC Maturity Model: Big Data and Analytics A Guide to Unlocking Information Assets, IDC #239771, March 2013). Organizations will use IDC Health Insights' BDA Maturity Model to maximize their return on investment (ROI) in big data, data warehousing, business intelligence, advanced analytics, and related technology, people, and processes. Additionally, healthcare organizations will be able to use this maturity model as a tool to encourage and improve intra- and intergroup collaboration in defining and executing the BDA strategy and in promoting and encouraging the use of BDA solutions. SITUATION OVERVIEW Healthcare organizations have had mixed results with their investments in analytics. Health plans have made greater investments in analytics than hospitals as hospitals have relied heavily on their core health information systems to meet their analytics requirements. Data quality and the inconsistent reporting of laboratory results and other data quality challenges have created tremendous barriers to successful analytics. Most of the available data has been retrospective structured administrative and financial information. Clinical data has, to date, been used primarily for direct patient care or clinical research and not risk identification, chronic disease, or patient engagement. These three key activities require very specific data and functionality. While health plans have used retrospective claims information to perform these activities, the power of unstructured data and clinical data is proving extremely valuable in determining drivers of outcomes. With the advent of changing financing and care delivery, analytics has taken on new importance. The industry will have to move from reporting facts to discovery of insights, toward becoming data-driven healthcare organizations. The advantages brought by the BDA technology stack (infrastructure, data organization and, 2013 IDC Health Insights #HI Page 1

6 analytics and discovery, and decision support and interface automation) are enormous. Today, healthcare has limited experience with the use of these technologies. All but the very largest healthcare organizations are approaching BDA through pilot or proof-of-concept projects. To date, few healthcare organizations have adopted BDA technology across the enterprise. The biggest challenge for the adoption of BDA across all industries is the inability to determine relevant data (source: IDC's 2012 Global Technology and Industry Research Organization IT Survey). For example, a large integrated delivery system in the West analyzed unstructured clinical data and discovered that the drivers of readmissions for congestive heart failure were drug and alcohol abuse and living situation. When asked what next steps were in the deployment of the BDA technology, the organization did not have an answer. Few healthcare organizations have analytics strategies and so no road map for the future deployment of technology. The IDC Health Insights' BDA Maturity Model will help healthcare executives create a road map of how to progress through the maturity model's five stages: Ad hoc Proof of concept, no formal budget or staff Opportunistic Projects undertaken based on available resources Repeatable More ubiquitous access to data Managed Budgeted, staffed Optimize Fine-tuning technology and processes Table 1 describes the characteristics of the five stages of maturity with their related goals and objectives. Table 1 allows readers to understand the progression of maturity and what each stage will realize. Page 2 #HI IDC Health Insights

7 TABLE 1 IDC Health Insights' Big Data and Analytics Maturity Model: Overview of Maturity Stages Ad Hoc Opportunistic Repeatable Managed Optimized Key characteristic Experimental Ad hoc, siloed proof-ofconcept or pilot projects Undefined processes Lack of resources Individual effort Intentional Defined requirements and processes Unbudgeted funding Project and resource allocation inefficiency Accepted Recurring projects Budgeted and funded program Documented strategy and processes with stakeholder buy-in Measured Project, process, and program performance measurement influences investment decisions Standards emerge Operationalized Operationalized Continuous and coordinated BDA process improvement value realization Goal Information access Data analysis Comprehensive hindsight Actionable insight Enterprisewide foresight Outcome Proof-ofconcept or pilot projects Value through new knowledge, learning Functional deployment Knowledge value grows, business value opportunities become visible Persistent adoption Business value is realized but remains localized to business units Coordinated enterprise use New product and service opportunities transition to business plans Continuous learning and improvement Previously unattainable business value is continuously produced IT efficiency is demonstrated Source: IDC Health Insights, 2013 The maturity model then defines five critical measures that can be assessed against the maturity stages: Intent: The measure of intent includes attributes such as strategy, capital and operational budgets, performance metrics, sponsorship, and project and program justification. Technology: The measure of technology includes attributes such as the appropriateness, applicability, integration, support for standards, and performance of technology and IT architecture to the relevant workloads IDC Health Insights #HI Page 3

8 Data: The measure of data includes attributes such as the quality, relevance, availability, reliability, governance, security, and accessibility of multistructured data. People: The measure of people includes attributes such as the technology and analytics skills, intra- and intergroup collaboration, as well as organizational structures, leadership, training, and cultural readiness. Process: The measure of process includes attributes such as the processes of data collection, consolidation, integration, analysis, information dissemination and consumption, and decision making. In healthcare, the BDA technology stack (from hardware infrastructure to software) is being offered to the market by a few large enterprise vendors such as IBM and Oracle. A number of other technology vendors are providing pieces of the technology stack. For example, SAS is providing a range of analytics and data software but not the hardware. These horizontal technology solutions provide many benefits, including the ability to potentially access multiple new data sets, create customized analyses, move from reporting to discovery, have access to advanced analytics, and data visualization. One of the challenges of deploying the technology across the enterprise is the lack of skills to support both the technology and the analytics. Hospitals are just beginning to understand the importance of hiring staff with advanced informatics degrees (e.g., biostatisticians, epidemiologists). In addition to staffing shortages, the technology itself is quite expensive; this is why only the largest healthcare organizations are able to invest. We have begun to see new business models emerge that offer access to benchmark data on a services basis. For example, IBM and Memorial Sloan Kettering are taking Sloan Kettering best practices for treating various cancers and allowing other healthcare organizations to have access to the results on a subscription or pay-as-you-go basis. A handful of healthcare analytics companies have taken a different approach, which is to use the BDA technology stack and build healthcare analytics on top of the stack. Examples include Explorys, Apixio, Humedica and, recently, MedeAnalytics. The advantages of this approach are that healthcare organizations do not have to have BDA technology expertise, and all the vendors listed above have analytics staff that can help clients optimize the use of the data. Those healthcare organizations that have sophisticated analytics staff in place can take advantage of the power of the BDA technology stack, while the less sophisticated can take advantage of having access to new data sources and new analytics capabilities. Another advantage is that healthcare organizations do not have to face the challenge of mastering data consistency and quality as the vendors have data organization and capabilities that include data integration. Page 4 #HI IDC Health Insights

9 The challenges of this approach are that the reports available from these technology suppliers will not meet all of the analytics requirements of a healthcare organization. THE APPROACH While the promise is great, the need to assess new technologies, analytics methods, and data sources in combination with a lack of appropriately skilled human resources is creating risks and challenges for both IT and business. To remain competitive and to excel, managers can no longer rely only on experience or intuition to make decisions. BDA solutions promise to provide the decision support and decision automation functionality and capabilities needed to lead and succeed. The maturity of an organization's adoption of BDA solutions is uneven both within and across industries. Our recent BDA survey of over 4,000 organizations of all sizes shows that only 25% of those surveyed had a business analytics strategy, and 50% either don't know how or find it too difficult to measure return-on-business analytics project investments. A growing body of evidence lends credence to the fact that more analytically oriented healthcare organizations have a competitive advantage over their less analytical peers. Today's market hype, unfortunately, often drowns out real business and technology issues with which early adopters are struggling. Therefore, a method of assessing BDA maturity is needed as both a planning tool and a way to determine the business progress in adopting BDA technologies and methods. As shown in Table 2, IDC Health Insights' BDA Maturity Model comprises five stages of maturity across five critical measures. These stages and measures are defined in the sections that follow IDC Health Insights #HI Page 5

10 TABLE 2 IDC Health Insights' Big Data and Analytics Maturity Model: Overview of Measures by Stage Ad Hoc Opportunistic Repeatable Managed Optimized Stage description Ad hoc Siloed proofof-concept or pilot projects Undefined processes Lack of resources Individual effort Defined requirements and processes Unbudgeted funding Project and resource allocation inefficiency Recurring projects Budgeted and funded program Documented strategy and processes Stakeholder buy-in Project, process, and program performance measurement influences investment decisions Standards emerge Continuous and coordinated BDA process improvement value realization Intent (strategy, sponsorship, justification) No strategy exists Unbudgeted project based on individual effort Business case undefined Departmentlevel, siloed strategy Project-level budget with localized midlevel support No enterprise support for measurement tools or methods Business-unitlevel strategy exists but not widely accepted Business-unitlevel budget with LOB support Cost-benefit analysis without common measurement tools or methods Crossbusiness-unitlevel strategy Enterprisewide budget with upper support Enterprisewide measurement tools and methods Enterprisewide, documented, accepted strategy Executive support, budgeted and ad hoc funding Widely accepted tools and processes for business case development ROI measurement Page 6 #HI IDC Health Insights

11 TABLE 2 IDC Health Insights' Big Data and Analytics Maturity Model: Overview of Measures by Stage Ad Hoc Opportunistic Repeatable Managed Optimized Data (relevance, quality, availability) Easily available data is utilized, but it is incomplete Data requires substantial manual effort to prepare for consumption Multisourced structured data or unstructured content exists Data lacks timeliness and veracity Data collection, monitoring, and integration processes are in place Consistent data governance and security practices have not been established Metrics to manage data quality, timeliness, and veracity exist Metrics to govern data collection, monitoring, and processes Enterprisewide access to ontime, trusted, and comprehensive multistructured and multisourced data sets Technology (adoption, performance, functionality) On-premise technology requires substantial effort to maintain and tune to derive desired performance Functionality (cloud or onpremise) is limited or too generic to provide appropriate performance Adoption is project specific New projectspecific technology is acquired and deployed This technology is fit for purpose but is not integrated with other deployed technology Adoption is localized Multiple fit-forpurpose technologies have been deployed and are integrated Adoption is selective A wide range of fit-forpurpose technologies have been deployed Performance of these technologies is monitored and tuned as needed Relevant technology broadly adopted A wide range of fit-for-purpose technologies have been deployed and pervasively adopted Software and hardware have been optimized A high level of automation exists in systems for existing workloads and for dynamic scalability 2013 IDC Health Insights #HI Page 7

12 TABLE 2 IDC Health Insights' Big Data and Analytics Maturity Model: Overview of Measures by Stage Ad Hoc Opportunistic Repeatable Managed Optimized People (skills, culture, organizational structure) A few individuals with some but not all the necessary skills are scattered throughout the organization Lack of interest and support Teams with some but not all the necessary skills Lack of intraorganizational coordination Departmental support for projects Skills acquisition, training, and are governed by a stated strategy Internal skills are augmented with external service providers Staff is primarily decentralized Executive support exists for a centralized BDA group A broad range of internal technology skills exist and are augmented with external vendors Analytics skills are mostly decentralized All the necessary expert BDA human resources exist Executive priority is placed on BDA Management encourages and promotes BDA use A centralized group exists with primary responsibility for BDA and for coordination of any decentralized resources and vendors Page 8 #HI IDC Health Insights

13 TABLE 2 IDC Health Insights' Big Data and Analytics Maturity Model: Overview of Measures by Stage Ad Hoc Opportunistic Repeatable Managed Optimized Process (tracking, analysis, decisioning) Focused on creating information repositories and accessing the siloed information therein Lack of support for predictive analytics or scenario evaluation IT and business pursue their own projects with a lack of collaboration Data analysis is emphasized at the expense of data tracking, preparation, and decisionsupport processes IT and business begin to collaborate on defining requirements and funding projects Strategy lays out the need for crossfunctional collaboration between IT and business and among different business groups BDA processes are extended to handling multistructured data First attempts to monitor and document decision processes and decision outcomes Metrics for evaluating process quality and success have been established BDA leadership team is responsible for process coordination Collaboration, workflow, and rules technology augment core information and analysis processes Processes are categorized into performance and experimentation Appropriate support, staffing, technology, and funding exists for each Decision techniques enable continuous process improvement and integration of analytics into business processes Source: IDC Health Insights, 2013 Stages of the Big Data and Analytics Maturity Model IDC Health Insights' Big Data and Analytics Maturity Model includes five stages. Ad Hoc Description: Organizations at the ad hoc stage often launch BDA projects to address a single business question in an environment lacking BDA strategy and interest or support. These are usually unbudgeted proof-of-concept or pilot projects based on individual or small workgroup efforts. Often, the business case for the BDA project is undefined, and the goal is to experiment with technology. Value lies in narrow knowledge about the healthcare organization in pockets of BDA pilots with limited effect on 2013 IDC Health Insights #HI Page 9

14 business outcomes. The data for such BDA projects is sourced from easily accessible internal repositories that present an incomplete view into the subject matter and require substantial manual effort to transform into a usable format for analysis or enduser consumption (e.g., large volumes of structured data hitherto analyzed at various levels of aggregation or unstructured data maintained but not analyzed). At the ad hoc stage, organizations rely on existing technology or experiment with open source or cloud technologies that have low initial costs. In most cases, the technology maintenance and tuning requires substantial manual effort to achieve the desired system performance. The technology lacks specialized functionality to address specific end-user requirements and is adopted by few individuals. There are an insufficient number of employees with only partial BDA skills scattered throughout the healthcare organization. BDA processes are focused primarily on creating information repositories with access only to siloed information therein, without support for such decision-making functionality as predictive analytics or scenario evaluation. IT and business pursue their own discrete projects with a lack of collaboration. Goal: The primary BDA goal of healthcare organizations at the ad hoc stage is to provide decision makers with access to information. This can involve the use of query, reporting, dashboard, and search software to simply expose a defined data set to end users. Opportunistic Description: Organizations at the opportunistic stage take the lessons learned from BDA pilot projects and apply them to newly identified requirements from business groups with project-specific budgets. Business-value opportunities come into department-level view. Project and resource allocation are inefficient because BDA strategies exist only at the departmental level. Although localized midlevel support exists for specific BDA projects, there is no enterprise support for measurement tools or methods to gauge outcomes and effectiveness. BDA projects are based on data from multiple sources and can include combinations of structured, semistructured, and unstructured data. This data, however, lacks timeliness and veracity. The data is available to a limited number of users, and BDA solution adoption is localized. New technology is acquired and deployed for a specific project. These technology components are fit for purpose but are not integrated. A team with some of the necessary technology, analytics, and skills exists, but there is a lack of inter-organizational coordination. The data analysis process is overemphasized at the expense of data tracking, preparation, and decision support processes. IT and business begin to collaborate on defining requirements and funding projects. Page 10 #HI IDC Health Insights

15 Goal: The primary BDA goal of healthcare organizations at the opportunistic stage is data analysis. It will typically lack support from appropriate data preparation and technology and will be based on incomplete historical data. The analysis typically involves the use of multidimensional analysis, query, reporting, and content analytics tools. Repeatable Description: Organizations at the repeatable stage are involved in recurring, budgeted, and funded BDA projects with business-unitlevel stakeholder buy-in. These BDA projects are undertaken within a documented business-unit-level BDA strategy that includes project. There is line-of-business support for BDA projects, but the cost-benefit analysis is performed in an ad hoc fashion, without common measurement tools or methods. The business value realized remains localized because of lack of coordinated use of BDA capabilities. Data collection, monitoring, and integration processes are in place, but consistent data governance and security practices have not been established. Multiple fit-for-purpose technologies have been deployed and are integrated, but their adoption is selective. Technology and analytics skills acquisition, training, and are governed by the stated business-unit-level BDA strategy. Internal staff, which is primarily decentralized, is augmented with external service providers. The strategy is to purport the need for crossfunctional collaboration between IT and business and among different business groups. BDA processes are extended to handle multistructured data. First attempts to monitor and document decision processes and decision outcomes emerge. Goal: The primary BDA goal of healthcare organizations at the repeatable stage is to provide comprehensive insights based on data from multiple internal and external structured, semistructured, and unstructured sources. The analysis can involve the use of multidimensional analysis, query, reporting, content analytics, and predictive analytics tools and the underlying information technology. Managed Description: Organizations at the managed stage experience the emergence of BDA program standards. These organizations have developed a cross-business-unit-level BDA strategy, and there is an enterprisewide budget with upper support. Support grows as BDA is seen as enabling new product and service opportunities. Enterprisewide performance measurement tools and methods guide investment decisions. These include metrics to manage data quality, timeliness, and veracity, which govern data collection, monitoring, and processes. A wide range of fit-for-purpose technologies have been deployed and broadly 2013 IDC Health Insights #HI Page 11

16 adopted. Relevant technology performance is monitored and tuned as needed. Executive support exists for a centralized BDA technology group, but analytics skills are mostly decentralized. Metrics for evaluating BDA project and program success have been established. The BDA leadership team is responsible for process coordination. Collaboration, workflow, and rules technologies augment core information and analysis technology. Goal: The primary BDA goal of healthcare organizations at the managed stage is to provide actionable insight to a range of decision makers within the organization. BDA capabilities are utilized to answer what happened and why it did. Optimized Description: Organizations at the optimized stage ensure continuous and coordinated BDA process improvement and value realization. They have enterprisewide, documented, accepted BDA strategy; executive support; and budgeted as well as ad hoc funding (to address unforeseen opportunities). There are widely accepted tools and processes for business case development and ROI measurement. Trusted and comprehensive information based on data sets combining internal and external structured, semistructured, and unstructured alphanumeric, image, audio, and video content is delivered on time. A wide range of fit-forpurpose technologies have been deployed and pervasively adopted. Software and hardware have been optimized, and a high level of automation exists in systems for existing workloads and for dynamic scalability. All the necessary internal and/or external human resources are in place with expertise in data collection, monitoring,, analysis, and dissemination. Management actively encourages and promotes use of the BDA solution, some of which is embedded in operational processes and applications. A centralized group exists with primary responsibility for BDA and for coordination of work among additional decentralized resources and external vendors. BDA processes are categorized into performance and experimentation, with appropriate support, staffing, technology, and funding for each. Decision techniques (e.g., collaboration, rules and policy, and knowledge retention) supplement business analytics technology, enabling continuous process improvement and integration of analytics into business processes. Goal: The primary BDA goal of healthcare organizations at the optimized stage is to provide foresight to decision makers throughout the enterprise and to relevant external stakeholders. Analytics continue to be deployed operationally through business processes, resulting in predictive capabilities to capitalize on new opportunities and to mitigate risk. Page 12 #HI IDC Health Insights

17 Measures of the Big Data and Analytics Maturity Model At each stage of the maturity model, healthcare organizations should consider five key measures, each of which contributes to the ability to advance toward higher levels of BDA competency and maturity. Successful deployment and use of BDA solutions depend on a multipronged approach guided by a strategy that accounts for not just technology but also human and capital resources, business and IT processes, and the data. IDC Health Insights' BDA Maturity Model defines five critical measures that can be assessed against the maturity stages: Intent: The measure of intent includes attributes such as strategy, capital and operational budgets, performance metrics, sponsorship, and project and program justification. Technology: The measure of technology includes attributes such as the appropriateness, applicability, integration, support for standards, and performance of technology and IT architecture to the relevant workloads. Data: The measure of data includes attributes such as the quality, relevance, availability, reliability, governance, security, and accessibility of multistructured data. People: The measure of people includes attributes such as the technology and analytics skills, intra- and intergroup collaboration, as well as organizational structures, leadership, training, and cultural readiness. Process: The measure of process includes attributes such as the processes of data collection, consolidation, integration, analysis, information dissemination and consumption, and decision making. FUTURE OUTLOOK Short- Term Big Data and Analytics Trends Intent: A small percentage of innovative data and content-savvy organizations build or transform business on the foundations of a BDA strategy. Many BDA projects, though, will continue to be initiated by IT. Whether technically solid or not, many such IT-led projects will demonstrate a lack of connection to business goals. Continued lack of sufficient IT and business collaboration will lead to slow adoption of solutions and difficulty in securing additional rounds of project funding. Despite a period of disappointment with overhyped promises, a sufficient number of successful BDA 2013 IDC Health Insights #HI Page 13

18 projects will spawn a business-driven investment cycle that will, in the long term, push BDA into the mass market, resulting in pervasive adoption. Technology: A growing number of technology options and increasing specialization of technology will address specific use cases with increased performance power. Technology fragmentation, however, will lead to rising user demand for less complexity, improved integration, low-cost maintenance, technology risk, and standardization. Data: As the digitization of society continues, the digital universe will continue to expand rapidly. Most deployment of BDA solutions will incorporate multistructured data from multiple sources. Information, governance, security, and privacy needs will command an increasing portion of the data budget. People: Lack of BDA technology, analytics, and business skills will drive up labor costs and lead to more outsourcing. Faster adoption of cloud-based solutions and technology appliances will result in faster deployments and lower ongoing maintenance costs. Process: Insights from BDA projects will begin to affect business processes by uncovering existing inefficiencies and identifying new ways of interacting with customers, employees, suppliers, partners, and regulatory agencies. In many cases, however, BDA potential will be slowed due to organizations' inability to change business processes fast enough. Optimized business processes will have a greater chance of emerging in new BDA-enabled use cases and operating models. IDC expects a steady but modest improvement in BDA market maturity in the next months. For the market as a whole, we expect each measure in IDC Health Insights' BDA Maturity Model to increase by one stage. Upcoming IDC research will provide an updated and more detailed assessment of BDA market maturity for this period. Long- Term Big Data and Analytics Trends Intent: The body of research that quantitatively evaluates the impact of BDA solutions on desired outcomes will expand rapidly from its current nearly nonexistent state. Technology: Growth in BDA applications will expand rapidly. BDA applications will ensure that analytics functionality is made available to the largest possible number of customer-facing and operational employees as well as managers. Open and consensus set standards will evolve and emerge to ease technology risks and Page 14 #HI IDC Health Insights

19 improve interoperability across the BDA stack and across vendor solutions. Increased strategic value of data as an asset may lead to greater scrutiny of a technology's country of origin as the risk of data loss or contamination increases. Data: A growing percentage of the population will opt to share behavioral, demographic, purchasing, financial, and other personal data in exchange for services enabled by BDA solutions. Policy makers, case law, and business pressures will harmonize data privacy and security provisions across regional jurisdictions. BDA security, privacy, and analytics governance standards will be accepted internationally. At the same time, the growing number of physical objects creating or emitting data will also expand rapidly. Data will become a formally recognized asset class by accounting standards. People: The BDA IT skills shortage will largely disappear as advances in technology expand the use of automation. Training, education, outsourcing, and global labor movement trends will combine to address the shortage of data scientists and managers focused on data-driven decision making. Process: BDA solutions will drive rapid increases in productivity. Data acquisition and preparation processes, which today absorb the bulk of BDA technology and staffing budgets, will be greatly simplified as data interoperability standards become widely available and accepted. Time and effort spent on data quality and governance will be generally recognized as a critical enabler. Solutions will exist that fully automate the processes of hypothesis generation, algorithm (and data) selection, and model development, testing, and execution. ESSENTIAL GUIDANCE Actions to Consider To progress along the stages of the BDA Maturity Model, we recommend: Focusing on each of the five measures of the model in concert with one another Ensuring that there is a coordinated progression along all of the five measures For example, a healthcare organization may be at the managed stage in technology, but only at the opportunistic stage in data. This type of imbalance in competency and maturity stages across the five measures can be an obstacle to sustainable success in BDA efforts IDC Health Insights #HI Page 15

20 To maximize the value of IDC's BDA Maturity Model, we provide the following guidance in various time frames: Now: Assess the business and IT BDA "as is" situation. Identify opportunities to use existing data, technology, and analytics in new ways. As well, explore opportunities to use new low-cost public cloud and open source options as they emerge. Identify relevant technology and analytics skills among existing staff, peers, and vendors. Experiment with proof-of-concept and prototype projects. In the next one to two years (next budget cycle): Use early quantifiable wins to demonstrate potential and justify budget allocations. Evaluate existing technology and its shortcomings. Assess skill gaps and plan to hire and/or externally source professional services. Identify business sponsors and champions that will support and promote BDA projects. Expand projects and begin to define architectural standards. Merge big data into security and governance policies. Incorporate new data sources. Budget for workload-specific technology. In the next three to five years: Ensure that both performance and experimentation and discovery use cases are supported with appropriate technology, staff, data, processes, and funding. Engage in business process reengineering in response to new insights from BDA solutions. Assess progress and adjust internal investment priorities to match evolving requirements. Ensure balanced resource allocation across all dimensions of the solutions. Maintain a closed-loop learning environment based on data-driven decision making and expert judgment. Table 3 shows IDC Health Insights' BDA Maturity Model guidance. Page 16 #HI IDC Health Insights

21 TABLE 3 IDC Health Insights' Big Data and Analytics Maturity Model Guidance Stage Guidance Ad hoc Outcome: Proof-of-concept or pilot projects. Value through new knowledge, learning. Launch a proof-of-concept or pilot project using existing resources. Use existing data with the recognition that it may be incomplete and may lack the necessary quality, which will require substantial manual data preparation effort. Expect significant manual effort to tune the system to reach desired performance. Lack of all the necessary functionality will require workaround and scope control to achieve desired project outcome. Do not spend significant time and effort to try to gain executive support before establishing initial project proof points. Look for support from colleagues with specialized BDA skills. Focus on information-access projects within a specific business domain. Opportunistic Outcome: Cross-functional deployment. Knowledge value grows, business value opportunities become visible. Establish a department-level BDA strategy. Although disconnected from the rest of the organization, it will guide the next round of project investments. Budget for localized projects that have mid- support. Begin to integrate data from multiple sources to move toward high levels of trust in the information coming from the BDA system. Deploy new technology (on-premise or in the cloud) that enables more timely access to information due to system performance improvements. Establish a BDA team with skills in existing and newly deployed technologies and that coordinates work with colleagues in business. Allow data scientists to experiment to uncover new insights. Repeatable Outcome: Persistent use. Business value is realized but remains localized to business units. Develop cross-departmental business-unit-level BDA strategies. Budget for business unit needs. Perform a localized cost-benefit analysis for BDA projects. Continue to expand the availability of and to integrate internal multistructured data sources. Be aware that data governance policies and procedures will be difficult to implement at the single-business-unit level. Expand the availability of fit-for-purpose technology with the understanding that initial adoption will be selective. Minimize shelfware or unutilized subscription licenses via staged rollouts. Assign, train, and hire staff based on the BDA strategy. Augment existing skills with specialized external service providers. Begin to monitor and document decision processes and decision outcomes. Ensure the BDA team has representatives from all stakeholder groups to facilitate collaboration IDC Health Insights #HI Page 17

22 TABLE 3 IDC Health Insights' Big Data and Analytics Maturity Model Guidance Stage Guidance Managed Outcome: Predictable outcomes. New product and service opportunities transition to business plans. IT efficiency is demonstrated. Assign an executive-level leader to coordinate the development of a crossbusiness-unit BDA strategy. Establish metrics and methodology for data governance and metrics by which BDA processes, staff, and outcomes are measured. Deploy fit-for-purpose and workload-optimized technology. Incorporate predictive analytics into technology performance monitoring and processes. Enable broad technology adoption by ensuring that an appropriate technology pricing structure is negotiated with IT vendors. Establish executive support and develop plans for a centralized BDA team. Provide support for decentralized analytics staff within business groups. Optimized Outcome: Continuous learning and improvement. Previously unattainable business value is continuously produced. Develop an enterprisewide BDA strategy that is championed by a C-level executive. Budget for BDA projects. Make available tools and methodology for business case development and performance and outcomes measurement. Make available information about all the data sources for users with the appropriate security rights. Maximize use of the workload-optimized system, automated system performance, and dynamic scalability features of BDA technology. Regularly provide training to all the BDA technology, analytics, and business staff. Maximize BDA staff centralization for functions such as data integration, systems, and report and dashboard development. Ensure that both performance and experimentation and discovery processes are supported with appropriate staffing, technology, and funding. Employ decision- techniques to enable continuous process improvement and integration of analytics into business processes. Source: IDC Health Insights, 2013 LEARN MORE Related Research Perspective: Physician Relationship Management Builds Loyalty and Community (IDC Health Insights #HI241709, June 2013) Business Strategy: Accountable Care Maturity Model (IDC Health Insights #HI240916, May 2013) Page 18 #HI IDC Health Insights

23 IDC Maturity Model: Big Data and Analytics A Guide to Unlocking Information Assets (IDC #239771, March 2013) Business Strategy: Analytics Leads Accountable Care Investment Priority (IDC Health Insights #HI239735, March 2013) U.S. Accountable Care 2013 Top 10 Predictions: Accountable Care IT Strategies, a Period of Maturity Ahead (IDC Health Insights #HI238485, December 2012) Business Strategy: What Approaches to Accountable Care Analytics Will Be Best for Your Organization? (IDC Health Insights #HI236896, September 2012) Perspective: Accountable Care Demonstrates Strong Health Plan Provider Partnerships (IDC Health Insights #HI235842, August 2012) Business Strategy: The Accountable Care Application Portfolio (IDC Health Insights #HI235377, June 2012) Perspective: IBM's Watson in Healthcare It's Not Just Jeopardy (IDC Health Insights #HI233961, March 2012) Business Strategy: ACO Analytics Key to Success (IDC Health Insights #HI232469, January 2012) U.S. Connected Health IT 2012 Top 10 Predictions (IDC Health Insights #HI232666, January 2012) U.S. Health Industry Provider 2012 Top 10 Predictions: Moving on from Meaningful Use to Healthcare Reform (IDC Health Insights #HI231907, December 2011) Synopsis This IDC Health Insights report discusses IDC Health Insights' Big Data and Analytics (BDA) Maturity Model, which provides healthcare executives with a road map to successful BDA deployment. Healthcare organizations are beginning to understand the benefits of having access to new data sources and advanced analytics tools and as a result are looking to invest in the BDA technology stack. Healthcare executives can use the maturity model to determine where their organization currently sits on the maturity model and understand what future investments in people, technology, and data are required. The healthcare supplier market for BDA is using the BDA technology stack as the foundation of their analytics, which provides the benefit of the technology without having to make skill set investments IDC Health Insights #HI Page 19

24 Cynthia Burghard, research director, Accountable Care IT Strategies, cautions healthcare organizations. Burghard says, "The ability for healthcare organizations to digest the results of these new analytics and translate those into actions that improve quality and manage costs provides the greatest hurtle to full maturity." Copyright Notice Copyright 2013 IDC Health Insights. Reproduction without written permission is completely forbidden. External Publication of IDC Health Insights Information and Data: Any IDC Health Insights information that is to be used in advertising, press releases, or promotional materials requires prior written approval from the appropriate IDC Health Insights Vice President. A draft of the proposed document should accompany any such request. IDC Health Insights reserves the right to deny approval of external usage for any reason. Page 20 #HI IDC Health Insights