Big Data: Moving Beyond the Buzzword

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1 by Michael Garzone Solutions Director, Technology Solutions Big Data seems to have become the latest marketing buzzword. While there is a lot of talk about it, do we really understand what Big Data means to healthcare? Big Data: Moving Beyond the Buzzword Executive Summary Big Data. It is a buzzword we have all heard. However, while there is a lot of talk about it, do we really understand what Big Data means to healthcare? According to IBM, 90 percent of the data in the world today has been created in the last two years alone. A staggering proportion of this data, 80 percent 1, is unstructured. So what is driving the creation of this new data? In the healthcare industry, we all understand the impact of the Health Information Technology for Economic and Clinical Health (HITECH) Act. HITECH mandates that eligible professionals and hospitals must successfully attest to demonstrating meaningful use (MU) of certified electronic health records (EHRs) in order to qualify for incentive payments through the Medicare EHR Incentive Program administered by the Centers for Medicare and Medicaid Services (CMS). In addition to MU, the healthcare industry has witnessed continued science and technology advancements, regulatory mandates such as the 5010 and ICD-10 requirements, and the expansion of the social media-driven and connected consumers all of which drive additional data elements. Greater and more detailed clinical documentation will be required to derive accurate codification from expanded ICD-10 diagnosis codes. The continued expansion of biomedical and bedside devices will result in increasing amounts of streaming data (e.g., diagnostic measurements, images,.wav files, video, etc.). Recent advances in medical and human genetics have healthcare organizations on the doorstep of personalized medicine, where genomic sequencing will be applied to the medical care of individual patients. The amount of sequenced data produced alone will be tremendous. 1 Watson: Learning Its Way to Saving Lives. Jon Gertner, Fast Company. October 22, Accessed on April 23, 2013, at

2 Natural Language Processing (NLP) technology not only simplifies the dictation process, but technology advancements are creating new use cases for capturing the spoken word as a data asset. With the introduction of secure messaging between clinicians, and between clinicians and patients, the content of these messages has new meaning and will continue to produce tremendous amounts of unstructured data. The resulting increased volume and detail will continue to feed explosive data growth for the foreseeable future. According to the McKinsey Global Institute, if U.S. healthcare were to use Big Data in a creative and efficient manner in their efforts to drive efficiency and quality, the healthcare sector could create more than $300 billion in value every year (two-thirds of which would be recognized in an approximate 8 percent reduction in overall healthcare expenditures) 2. A fundamental prerequisite to launching a Big Data initiative is the establishment of a data- driven culture. This often necessitates changes to an organization s strategic goals and core business processes as well as the key skills needed by its employees. Big Data has moved beyond just being a buzzword in healthcare. It is here to stay. The question then becomes how organizations will leverage this mountain of data as a strategic asset. Building a Foundation for Big Data Business Intelligence (BI) technology is advancing. New tools are entering the market and existing tools continue to evolve to make better use of this wave of growing data. But tools alone won t make a BI project successful. Success lies in the effective use of these tools in order to drive useful information out of this new and collected data. A fundamental prerequisite to launching a Big Data initiative is the establishment of a datadriven culture. This often necessitates changes to an organization s strategic goals and core business processes as well as the key skills needed by its employees. Establishment of such a culture is a top-down process requiring ongoing support from the executive team to implement two vitally important programs: Data Governance and Master Data Management. Data Governance Data Governance establishes the roles, responsibilities, and workflow for managing the organization s enterprise-wide reference data (e.g., Patient, PCP Provider, DRG, or Location). The ultimate objective is to establish Data Owners who are ultimately accountable for one or more of these Master Data types. Data Owners are supported by Business and Technical Data Stewards who prepare data definitions and algorithms for metrics that roll up to a Key Performance Indicator (KPI). Data Stewards also play a vital role in data quality assurance, which is absolutely essential if information stakeholders are to maintain high confidence in the data they need to make decisions. Data Ownership and Stewardship ensure the consistency of the organization s data definitions, metrics, and KPIs. This consistency is an important requirement in the management of information as a strategic corporate asset. 2 Big data: The next frontier for innovation, competition, and productivity, May McKinsey & Company. Accessed on April 16, 2013, at 2

3 Master Data Management Master Data Management (MDM) is the enabling technology for Data Governance. It provides the technical infrastructure that allows Data Owners and Stewards to easily maintain the organization s Master Data assets. Without an MDM strategy, many Data Governance initiatives fail to achieve their objectives as the sheer amount of effort required to maintain the Master Data is far too onerous. It is important to note that Big Data environments are as dependent upon Master Data as enterprise data warehouses or data marts. A source of truth for patient and provider data is every bit as important in a Big Data environment, and Data Governance/MDM provide the requisite organization structure, workflow, and tools. One of the first decisions that organizations must make as they consider a Big Data initiative is to determine where it fits in their existing data architecture. It is imperative to develop a strategy for incorporating Big Data rather than allowing it to become a vast unstructured data silo. One of the first decisions that organizations must make as they consider a Big Data initiative is to determine where it fits in their existing data architecture. Big Data most often complements, rather than replaces, the platforms that have already been implemented to analyze structured data. It is imperative to develop a strategy for incorporating Big Data rather than allowing it to become a vast unstructured data silo. Tools for Success The traditional relational database model, with pre-computed business logic, still has a place in healthcare and will continue to have one. These databases are a foundation for driving analytical measures and producing operational performance management dashboards. Relational database architectures work best when predictable queries are run over relatively structured data. This lies at the heart of most BI and data warehousing initiatives. Performance management relies heavily on structured data with precomputed calculations and measures to support fact-based analysis and decision making. The architecture of these traditional models is fundamental for tracking KPIs and providing the reporting and dashboards that organizations require to manage enterprise operation. EHR companies recognize the importance of data. While most EHR applications are transactional in nature, most vendors understand the future importance of analytics and are providing the capability for business (clinical) intelligence. The market is also beginning to see emerging third-party entries offering cloud-based analytical platforms to augment the EHR environment. Here are a few examples: Epic Systems Corporation has recently launched Cogito, an open analytics environment consisting of a library of pre-built registries and analytical data marts for prevalent chronic conditions, MU metric collection with standard reporting to satisfy most compliance regulations, and benchmarking programs. Cerner Corporation provides PowerInsight, a structured data warehouse that combines clinical, management, operational, and financial data into a central decision support platform. Health Catalyst couples optimization services with its Catalyst platform, a combination Late-Binding Data Warehouse and source data mart that links master reference data across source marts. Health Care DataWorks KnowledgeEdge is an enterprise data warehouse solution that normalizes enterprise data for use with pre-built dashboards, reports, and applications. 3

4 Some of them do a very good job of reporting well-known critical performance indicators. All appear to be continuing to improve upon their functionality and usability. The questions organizations are asking today are: Are these relational solutions enough to address today s healthcare business issues? How do these solutions integrate with our overall data architecture? And probably the biggest unknown, will they address my Big Data issues ahead? With Volume Comes Complexity Data is getting more complicated. The need to query very large amounts of information with new kinds of processing is clear and not just transactional processing, but largescale data analysis. With growing volumes of data, traditional data collection, storage, and analysis methods will most likely reach their functional capacity in the near future. Data is getting more complicated. This huge volume of largely free-text and unstructured data does not fit nicely into tables, and even if it did, the questions posed today require queries to inspect the natural language within the unstructured data. SQL doesn t have syntax for that. The need to query very large amounts of information with new kinds of processing is clear and not just transactional processing, but large-scale data analysis. Addressing the Big Data Analysis Dilemma In determining how healthcare will address the issues posed by Big Data, we can look at how other industries have addressed the same. In the early 2000s, Google was ingesting and indexing the entire internet every day or two. This exponentially growing mountain of unstructured data had pushed traditional processing to its limit. In 2004, Google created MapReduce, which took this large volume of data and broke it into manageable chunks and spread it across a large array of commodity servers (Map). Each server had dedicated storage and computational capacity. MapReduce then leveraged the CPU of each server to act on its individual block. When each CPU completed its action, MapReduce assembled, or reduced, the result. Essentially, Google took what was once a very large and long serial indexing process and broke it into many smaller parallel indexing processes, also known as data parallelism. It is this data parallelism that enables companies like Google, Yahoo, Facebook, and Twitter to function at this exponential data scale every day. Hadoop is the open source incarnation of Google s MapReduce architecture, consisting of: The Hadoop Distributed File System (HDFS), a large-scale distributed file system which is the software infrastructure that combines multiple servers and manages the distribution of data across each. The MapReduce component, which is the software infrastructure to push the code to each server to enable parallel data analysis all servers. Both components allow scalability through the addition of servers as volume increases potentially even to a Google-like proportion. Hadoop is flexible and elastic a true cloud computing model that allows support redundancy to prevent server or disk loss outage. Hadoop will not replace existing traditional models regardless of the EHR provided, thirdparty, or home-grown data warehouse at least not yet. But it can and will be combined with NoSQL-based products such as Cassandra, Voldemort, and Couchbase using distributed hash tables and columnar storage to address complex data issues. Combining these new technolo- 4

5 gies with your overall data architecture will provide the advanced capability of managing transactional assets while also ingesting unstructured complex data. Making the Best Choice for Your Organization While most healthcare provider organizations share common goals and must all adapt to changing regulatory mandates, science and technology advancements, the evermore connected consumer, and other market demands not yet known, few organizations are identical. Each has slightly different application portfolios, lines of business, and stages of MU and data maturity. Before making architecture decisions, we recommend the following: One of the first About the Author Michael decisions Garzone that provides delivery oversight and coordinates organizations the development must of comprehensive advanced make as they consider technology solutions that address enterprise strategic, a Big Data initiative is financial, operational, clinical, and health reform objectives to determine where it for our clients. He fits specializes in their in existing integration and interoperability, infrastructure, data architecture. health information exchange, business intelligence, It is imperative data warehousing, to report development, data develop a strategy for center infrastructure, business and care continuity, security, incorporating Big Data and cloud computing. Define your information management strategy. Create a roadmap as a guideline for data-related decisions. Routinely revisit your roadmap to consider new technology and advancements. Implement a data governance program. Remember that garbage in equals garbage out. Data governance ensures greater accountability for information quality as well as more consistent definitions and business rules. Choose technology that is aimed at a specific problem or need. Do not try to shoehorn data into poorly planned architectures. Plan an architecture that is scalable. Only organizations that create and manage to scale-out systems will be able to continue to address the ongoing rapid data growth. Don t let security be an afterthought. Big Data is introducing new and never-before addressed access challenges and security vulnerabilities. Without proper controls, Big Data can result in a Big Breach. Evidence of the advancement of data is all around us, but the full impact of Big Data in health care is still to come. Organizations that manage data as an asset and embrace the usability of large unstructured data pools will be positioned to reap both financial and performance improvements. rather than allowing For More Information it to become a vast Michael Garzone Solutions Director, unstructured data silo. Technology Services