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14. Semantic Data Lakes Dives In For HealthcareThe Healthcare industry has been having a hard time trying to save time and dollars toimproving patient outcomes for healthcare organizations through the use of data analytics.Many health systems find it difficult capturing and using the data from its patients to make areal impact on their businesses, especially considering the enormous amount of redundanciesand unanswered questions when it comes to dealing with healthcare.“Making sense out of big data is a challenge, particularly in the healthcare industry whereinformation comes from a variety of sources and in different forms including structured,unstructured, images, temporal, geo-location and signal data,” says Jans Aasman, PhD, CEO ofFranz, Inc., specializing in semantic web technologies.The solution may have finally appeared in the form of SDL’s (Semantic Data Lakes) saysAasman. In collaboration between The Montefiore Medical Center in Bronx, New York, Franz,Inc., Intel, Cloudera, and Cisco, SDL is a system that allows you to get all the data together fromdifferent silos for analytics. It will then be used to transform statistical databases, such asspreadsheets, into interactive graph databases that can be used to make better informed andpredictive healthcare decisions.http://bigcommunity.net/big 5. The Data Lake Concept Is lake-concept-is-maturing/fulltext

16. The Potential of Data Lake Technology in the HealthcareIndustryData lake technology has been named the future of big data. Unlike data warehousingtechniques, data lakes offer a different type of data management better suited to handling andcollaborating various forms of data in a way which doesn’t confuse systems. But how exactly dodata lakes work? What are their benefits in the healthcare system?Data lakes embrace a semantic approach to data storage, retaining enormous amounts of rawinformation (whether structured or unstructured) in its original state within a single centralisedlocation. This technology, often referred to as graph databases, enables analysts to select whichdata to use when necessary, giving them the opportunity to reuse it when required. Analystsalso have the option of combining seemingly incompatible sources of information in wayspreviously thought impossible with data ndustry17. 6 WAYS TO PROTECT MEDICAL DATA LAKES AT THE FACILITYLEVELWith the global push towards putting EHRs and the unified medical record in the cloud,hospitals and clinics are finally getting on board this big data ride. EHRs (Electronic HealthRecords) and Patient Portal use has been rising steadily over the past three years with moreand more patients having access to their medical data thru Patient Portals and mobile devices.As providers finally begin to roll out their EHR and Patient Portal programs, both patients and

doctors are enjoying the new found access to the data they need to make medical decisions,and it’s literally at their fingertips. This has given the patients a new sense of ownership of theirmedical data and perhaps a feeling of control over their destiny – especially in an environmentwhere ACA has left them feeling somewhat limited. In today’s connected society, medical dataof all kinds are being gathered and gleaned from a myriad of sources; from EHRs to mobiledevice apps to telemetry at the office level and to test results of all kinds. Medical data lakes,collections of unstructured medical data, are growing and thriving; but what is the risk of thisaccess? What are the purposes of data lakes and how can we protect ct-medical-data-lakes/18. Dealing with Big Data: The ascendency of data lakesThe data lake concept occupies a central place of prominence in contemporary big datainitiatives. The past two years have unveiled numerous headlines, vendor solutions (includingrepackaging of former solutions) and enterprise use cases for the utility of this centralizedapproach for accumulating, analyzing and actuating big data.The fervor for this method of managing big data is based on a simple premise that promisesvalue for organizations regardless of size or vertical industry. Data lakes provide a singularrepository for storing all data – unstructured, semi-structured and structured – in their nativeformats, granting access and insight to all without lengthy IT preparation.Moreover, the data lake movement is largely spurred by adoption rates for Hadoop. AsHadoop’s presence increases, its function as an integration hub for all data delivers morecredence and traction to the notion of data lakes. The data lake concept may be relatively new,but the association of Hadoop and big data is nearly as ubiquitous as big data itself.The combination of these two factors, Hadoop’s deployment as a data lake and the storage andaccess benefits this method produces, is largely responsible for the widespread attention datalakes have garnered. A recent post from Gartner reveals that data lake interest is “becomingquite widespread.” Forbes indicates that “one phrase in particular has become popular for themassing of data into Hadoop, the ‘Data ith-big-data-the-ascendency-of-data-lakes/

19. Making Data Lakes Usable: Why we need a semantic layer –and why it should be open sourceBig Data platforms, like Cloudera’sData Hub, Hortonworks’ DataPlatform, MapR’s Converged Data Platform, and others including IBM, Oracle,Pivotal, promise to easily bring diverse sets of application data together into one data clusterrunning on Hadoop. This collection of data sets is called a data lake.It is a wonderful thing to be able to bring data together so easily with Hadoop’s schema on readability rather than the schema on write required by traditional database systems. But we needto remember that along with the data comes all the data’s associated data problems. Bringingdata into the data lake does not, unfortunately, wash away all the problems of non-standard,non-integrated, redundant, and inconsistent data that are buried in application data.For data lakes, with great ease of data access comes a great need for data management. Butthis not what I want to talk about today (perhaps I’ll do so in a later blog post). I want to talkabout how we need to make it easy for users to access data in a data lake when there is nonintegrated and redundant it-should-be-open-source/20. Implementing Personalized, Precision Medicine with ArtificialIntelligence and Semantic Graph TechnologyPersonalizing healthcare services for individuals creates several demands on data-drivenfunctions in the medical field. Healthcare organizations are tasked with integrating structured,unstructured and semi-structured data, storing and cataloging them in relevant ways across usecases and locations, and leveraging emerging AI techniques for predictive capabilities whichcould potentially save lives.Most of all, this process must occur in time to make a difference for patients.According to Montefiore Health System System Senior Vice President and Chief Medical OfficerAndrew Racine, who spoke at a recent event for the unveiling of Intel’s Xeon ScalableProcessors, all of these measures must be implemented so providers can: “use information inreal time to make clinical decisions that are going to allow us to intervene with patients andprevent them from having adverse outcomes.”Montefiore is currently engaged in such an undertaking with a Semantic Data Lake forHealthcare(SDL). The SDL is powered by Franz’s AllegroGraph, architected by Intel, and fortifiedby Cloudera’s Hadoop distribution. By merging a unique set of data management techniques

with some of the most pertinent technologies across the data landscape, Montefiore is seekingto tailor its medical treatment and diagnoses for individual telligence-semantic-graph-technology/21. Big Data and Healthcare PayersWith the implementation of the Affordable Care Act, the advent of Healthcare InformationExchanges (HIE), the introduction of new provider models, such as Accountable CareOrganizations (ACO), and the transition to a more member-centric relationship model,Healthcare Payers face seismic changes in their business models. As with many large-scale,business transformations, there are challenges to navigate as well as opportunities to realizearound improving patient outcomes, reducing cost, and increasing revenue. Capitalizing onthese opportunities will depend on an organization’s capability to leverage information. Theability to capture, integrate, and interrogate large information sets will be foundational inrealizing objectives, such -healthcare-payers/22. The Bright Future of Semantic Graphs and Big Connected DataThe big data revolution is generating a mess of unruly data that’s difficult to parse andunderstand. This is to be expected–explosions don’t generally occur in a nice, orderly fashion,after all. But if the folks at Cloudera and Franz have their way, the world of connected data willbecome more accessible and useful when viewed through the lens of semantic graphtechnologies.Semantic graph technology is shaping up to play a key role in how organizations access thegrowing stores of public data. This is particularly true in the healthcare space, whereorganizations are beginning to store their data using so-called triple stores, often defined bythe Resource Description Framework (RDF), which is a model for storing metadata created bythe World Wide Web Consortium /

22. Empowering personalized medicine with big data andsemantic web technology: Promises, challenges, and use casesIn healthcare, big data tools and technologies have the potential to create significant value byimproving outcomes while lowering costs for each individual patient. Diagnostic images, genetictest results and biometric information are increasingly generated and stored in electronichealth records presenting us with challenges in data that is by nature high volume, variety andvelocity, thereby necessitating novel ways to store, manage and process big data. This presentsan urgent need to develop new, scalable and expandable big data infrastructure and analyticalmethods that can enable healthcare providers access knowledge for the individual patient,yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big dataand the role of semantic web and data analysis for generating “smart data” which offeractionable information that supports better decision for personalized medicine. In our view, thebiggest challenge is to create a system that makes big data robust and smart for healthcareproviders and patients that can lead to more effective clinical decision-making, improved healthoutcomes, and ultimately, managing the healthcare costs. We highlight some of the challengesin using big data and propose the need for a semantic data-driven environment to addressthem. We illustrate our vision with practical use cases, and discuss a path for empoweringpersonalized medicine using big data and semantic web ument/7004307/?reload true23. How the Search for Smart Data Drives Healthcare ITInvestmentThere’s no question that the healthcare industry has become extremely “data rich” over thepast few years. Thanks to the rapid pace of electronic health record adoption, the vast majorityof healthcare organizations are now sitting on an enormous nest egg of big data, includingpetabytes of clinical, administrative, demographic, and even genomic data on thousands ormillions of their patients.But having data and knowing how to use it are two very different things. Despite the keeninterest in adopting a growing collection of big data analytics, predictive analytics, and riskstratification tools, few organizations have really cracked the secret of how to turn a wealth offresh, unfiltered data into the spendable coin of actionable information.Some fall into the trap of buying new products to solve each individual problem as it arises, notrealizing that they are creating a patchwork of competing technologies, or developing ad hocworkarounds and an endless array of user interfaces that produce more headaches than /08/search-smart-data-drives-healthcareinvestment/

24. Data Lake Management: Do You Know the Type of “Fish” YouCaught?“Different types of fish live in a community and when you understand theirrelationship to each other, you have a better chance of catching what youwant.” thompsonadvertisinginc.comYou navigated your way to the lake and read up on the fundamentals of fishmanagement and introduced the data lake management principles. As you drove up tothe lake, you were thinking about what type of fish are you looking to catch or areyou looking for a trophy fish or to eat a fish? Without knowing the type of fish you’regoing to catch, you don’t know how far off-shore you have to boat for a catch.The primary charter for any data lake initiative is the ability to catalog all the data,enterprise-wide regardless of form (variety) or where it’s stored, whether on Hadoop,NoSQL or an enterprise data warehouse, along with the associated business,technical, and operational metadata. To carry on with our analogy, cataloging fishinto off-shore, near-shore or bottom fish can determine the type of fish you catch andhow far out you go fishing.The catalog must enable business analysts, data architects, and data stewards toeasily search and discover data assets, data set patterns, data domains, data lineageand understand the relationships between data assets – a 360 degree view of thedata. A catalog provides advanced discovery capabilities, smart tagging, data setrecommendations, metadata versioning, a comprehensive business glossary, and drilldown to finer grained data-lake-management-know-type-fishcaught/#fbid L81Agt5pe2b

Located in the Bronx, Montefiore Health System is the first hospital to implement a semantic data lake as part of the New York City Clinical Data Research Network (NYC -CDRN), an association of seven hospitals in the NYC area that are sharing data. As the pioneer, Montefiore is working with several technology providers, including Inteland