{"id":4151,"date":"2016-06-07T16:47:14","date_gmt":"2016-06-07T16:47:14","guid":{"rendered":"http:\/\/www.odbms.org\/blog\/?p=4151"},"modified":"2016-06-07T16:47:14","modified_gmt":"2016-06-07T16:47:14","slug":"on-data-interoperability-interview-with-julie-lockner","status":"publish","type":"post","link":"https:\/\/www.odbms.org\/blog\/2016\/06\/on-data-interoperability-interview-with-julie-lockner\/","title":{"rendered":"On Data Interoperability. Interview with Julie Lockner."},"content":{"rendered":"

“From a healthcare perspective, how can we aggregate all the medical data, in all forms from multiple sources, such as wearables, home medical devices, MRI images, pharmacies and so on, and also blend in intelligence or new data sources, such as genomic data, so that doctors can make better decisions at the point of care?”–\u00a0Julie Lockner.<\/strong><\/p><\/blockquote>\n

I have interviewed\u00a0Julie Lockner.<\/strong>\u00a0 Julie leads data platform product marketing for InterSystems<\/a>. Main topics of the interview are\u00a0Data Interoperability and InterSystems` data platform strategy.<\/p>\n

RVZ<\/p>\n

Q1. Everybody is talking about Big Data \u2014 is the term obsolete?<\/strong><\/p>\n

Julie Lockner:\u00a0<\/strong>Well, there is no doubt that the sheer volume of data is exploding, especially with the proliferation of smart devices<\/a> and the Internet of Things<\/a> (IoT). An overlooked aspect of IoT is the enormous volume of data generated by a variety devices, and how to connect, integrate and manage it all.<\/p>\n

The real challenge, though, is not just processing all that data, but extracting useful insights from the variety of device types. Put another way, not all data is created using a common standard. You want to know how to interpret data from each device, know which data from what type of device is important, and which trends are noteworthy. Better information can create better results when it can be aggregated and analyzed consistently, and that\u2019s what we really care about. Better, higher quality outcomes, not bigger data.<\/p>\n

Q2. If not Big Data, where do we go from here?<\/strong><\/p>\n

Julie Lockner:\u00a0<\/strong>We always want to be focusing on helping our customers build smarter applications to solve real business challenges, such as helping them to better compete on service, roll out high-quality products quicker, simplify processes \u2013 not build solutions in search of a problem. A canonical example is in retail<\/a>. Our customers want to leverage insight from every transaction they process to create a better buying experience online or at the point of sale. This means being able to aggregate information about a customer, analyze what the customer is doing while on the website, and make an offer at transaction time that would delight them. That’s the goal \u2013 a better experience \u2013 because that is what online consumers expect.<\/p>\n

From a healthcare<\/a> perspective, how can we aggregate all the medical data, in all forms from multiple sources, such as wearables<\/a>, home medical devices<\/a>, MRI images<\/a>, pharmacies<\/a> and so on, and also blend<\/a> in intelligence or new data sources, such as genomic data<\/a>, so that doctors can make better decisions at the point of care? That implies we are analyzing not just more data, but better data that comes in all shapes and sizes, and that changes more frequently. It really points to the need for data interoperability.<\/p>\n

Q3. What are the challenges software developers are telling you they have in today\u2019s data-intensive world?<\/strong><\/p>\n

Julie Lockner:\u00a0<\/strong>That they have too many database technologies to choose from and prefer to have a simple data platform architecture that can support multiple data models and multiple workloads within a single development environment.
\nWe understand that our customers need to build applications that can handle a vast increase in data volume, but also a vast array of data types \u2013 static, non-static, local, remote, structured and non-structured. It must be a platform that coalesces all these things, brings services to data, offers a range of data models, and deals with data at any volume to create a more stable, long-term foundation. They want all of these capabilities in one platform \u2013 not a platform for each data type.<\/p>\n

For software developers today, it\u2019s not enough to pick elements that solve some aspect of a problem and build enterprise solutions around them; not all components scale equally. You need a common platform without sacrificing scalability, security, resilience, rapid response. Meeting all these demands with the right data platform will create a successful application.
\nAnd the development experience is significantly improved and productivity drastically increased when they can use a single platform that meets all these needs. This is why they work with InterSystems.<\/p>\n

Q4. Traditionally, analytics is used with structured data, \u201cslicing and dicing\u201d numbers. But the traditional approach also involves creating and maintaining a data warehouse, which can only provide a historical view of data. Does this work also in the new world of Internet of Things?<\/strong><\/p>\n

Julie Lockner:\u00a0<\/strong>I don\u2019t think so. It is generally possible to take amorphous data and build it into a structured data model, but to respond effectively to rapidly changing events, you need to be able to take data in the form in which it comes to you.<\/p>\n

If your data platform lacks certain fields, if you lack schema definition, you need to be able to capitalize on all these forms without generating a static model or a refinement process. With a data warehouse<\/a> approach, it can take days or weeks to create fully cleansed, normalized data.
\nThat\u2019s just not fast enough in today\u2019s always-on world \u2013 especially as machine-generated data<\/a> is not conforming to a common format any time soon. It comes back to the need for a data platform that supports interoperability.<\/p>\n

Q5. How hard is it to make decisions based on real-time analysis of structured and unstructured data?<\/strong><\/p>\n

Julie Lockner:\u00a0<\/strong>It doesn\u2019t have to be hard. You need to generate rules that feed rules engines that, in turn, drive decisions, and then constantly update those rules. That is a radical enhancement of the concept of analytics in the service of improving outcomes, as more real-time feedback loops become available.<\/p>\n

The collection of changes we describe as Big Data<\/a> will profoundly transform enterprise applications of the future. Today we can see the potential to drive business in new ways and take advantage of a convergence of trends, but it is not happening yet. Where progress has been made is the intelligence of devices and first-level data aggregation, but not in the area of services that are needed. We\u2019re not there yet.<\/p>\n

Q6. What\u2019s next on the horizon for InterSystems in meeting the data platform requirements of this new world?<\/strong><\/p>\n

Julie Lockner:\u00a0<\/strong>We continually work on our data platform, developing the most innovative ways we can think of to integrate with new technologies and new modes of thinking. Interoperability is a hugely important component. It may seem a simple task to get to the single most pertinent fact, but the means to get there may be quite complex. You need to be able to make the right data available \u2013 easily \u2013 to construct the right questions.<\/p>\n

Data is in all forms and at varying levels of completeness, cleanliness, and accuracy. For data to be consumed as we describe, you need measures of how well you can use it. You need to curate data so it gets cleansed and you can cull what is important. You need flexibility in how you view data, too. Gathering data without imposing an orthodoxy or structure allows you to gain access to more data. Not all data will conform to a schema a priori.<\/p>\n

Q7. Recently you conducted a benchmark test of an application based on InterSystems Cach\u00e9\u00ae. Could you please summarize the main results you have obtained?<\/strong><\/p>\n

Julie Lockner:\u00a0<\/strong>One of our largest customers is Epic Systems<\/a>, one of the world\u2019s top healthcare software companies.
\nEpic relies on Cach\u00e9<\/a> as the data platform for electronic medical record<\/a> solutions serving more than half the U.S. patient population and millions of patients worldwide.<\/p>\n

Epic tested the scalability and performance improvements of Cach\u00e9 version 2015.1<\/a>. Almost doubling the scalability of prior versions, Cach\u00e9 delivers what Epic President Cark Dvorak<\/a> has described as \u201ca key strategic advantage for our user organizations that are pursuing large-scale medical informatics programs as well as aggressive growth strategies in preparation for the volume-to-value transformation in healthcare.\u201d<\/p>\n

Qx Anything else you wish to add?<\/strong><\/p>\n

Julie Lockner:\u00a0<\/strong>The reason why InterSystems has succeeded in the market for so many years is a commitment to the success of those who depend on our technology. A recent Gartner Magic Quadrant report<\/a> found we had the highest number of customers surveyed \u2013 85% \u2013 who would buy from us again. That is the highest number of any vendor participating in that study.<\/p>\n

The foundation of the company\u2019s culture is all about helping our customers succeed. When our customers come to us with a challenge, we all pitch in to solve it. Many times our solutions may address an unusual problem that could benefit others \u2013 which then becomes the source of many of our innovations. It is one of the ways we are using problem-solving skills as a winning strategy to benefit others. When our customers are successful at using our engine to solve the world\u2019s most important challenges, we all win.<\/p>\n

——————-<\/p>\n

Julie Lockner<\/strong> leads data platform product marketing for InterSystems. She has more than 20 years of experience in IT product marketing management and technology strategy, including roles at analyst firm ESG as well as Informatica and EMC.<\/em><\/p>\n

—————–<\/p>\n

Resources<\/strong><\/p>\n

– \u201cInterSystems Unveils Major New Release of Cach\u00e9,\u201d Feb. 25, 2015.<\/a><\/p>\n

– \u201cGartner Magic Quadrant for Operational DBMS, Donald Feinberg, Merv Adrian, Nick Heudecker, Adam M. Ronthal, and Terilyn Palanca, October 12, 2015, ID: G00271405.<\/a><\/p>\n

– White Paper:\u00a0Big Data\u00a0Healthcare:\u00a0Data Scalability with InterSystems Cach\u00e9\u00ae and Intel\u00ae Processors (LINK to .PDF<\/a>)<\/p>\n

Related Posts<\/strong><\/p>\n

–\u00a0A Grand Tour of Big Data. Interview with Alan Morrison. ODBMs Industry Watch,\u00a0February 25, 2016<\/a><\/p>\n

– \u00a0RIP Big Data.\u00a0By\u00a0Carl Olofson,\u00a0Research Vice President,\u00a0Data Management Software Research,\u00a0IDC. ODBMS.org,\u00a0JANUARY 6, 2016.<\/a><\/p>\n

– What is data blending.\u00a0By\u00a0Oleg Roderick, David Sanchez,\u00a0Geisinger Data Science. ODBMS.org, November 2015<\/a><\/p>\n

Follow us on Twitter: @odbmsorg<\/a>
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