The era of big data has opened up new opportunities for medical innovation. A new wave of research projects is looking to accelerate medical discovery by combining vast amounts of personal and operational healthcare information onto readily available compute and storage for utilizing leading edge analytics tools and techniques.
The opportunities are tremendous but the challenges are many. Rapid progress requires architectures and processes that overcome the biggest challenges of data management in the healthcare sector: rigorous data privacy regulations; diverse information standards; a proliferation of application and data silos and complex integration points; streams of real-time data from monitors, scanners, imaging devices, wearables, and mobile devices; and vast databases that hold data of every conceivable type.
Partners HealthCare is one of the world’s leading medical research organizations. Encompassing both Massachusetts General and Brigham and Women’s – the major teaching hospitals for Harvard Medical School – Partners HealthCare supports thousands of research projects each year. The Enterprise Research and Infrastructure Services (ERIS) group provides enabling technical capabilities for their research and innovation communities, ensuring that teams have access to the technology infrastructure, data, tools, and support resources they need to meet their project and operational goals.
The research and innovation teams at Partners are pioneers in the use of big data technologies. However it was clear that the aforementioned challenges, along with infrastructure limitations and a lack of supporting services, were impeding adoption and progress. Recognizing that their customers required a new approach for service delivery, ERIS worked with Dell EMC Services to architect, build, and operationalize a platform for developing and executing big data medical and translational research projects faster, more efficiently, and at lower cost.
The result of this collaboration is the Integrated Data Environment for Analytics platform (IDEA). The IDEA platform provides the Partners HealthCare community of researchers and innovators with four key service capabilities that are fundamental to the enablement of big data solutions – storage, compute, analytics, and platform.
The IDEA platform is used across the research and clinical innovation enterprise. The scalability and flexibility present allows for their use by both large, well-funded institutions, and small innovation teams with limited budgets. Customers of the IDEA platform include:
These teams, and many others, are only just beginning to explore and understand the power of the IDEA platform, and its potential for supporting medical innovation. Their excitement is palpable. With the support of the ERIS team and Dell EMC, the research teams at Partners HealthCare are shaping the future of healthcare in the big data age.
The post Transforming Medical Research with a Big Data Services Platform appeared first on InFocus Blog | Dell EMC Services.
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By 2020, the Internet of Things will comprise over 30 billion devices, growing to over 75 billion in 2025. It will transform cities, hospitals, factories. The way we work, shop and live will fundamentally change. Such exponential growth means enormous potential, and an avalanche of data from a vast array of sources and sensors. Capitalizing on this paradigm shift requires distilling this data into actionable insights.
Enter Predix: GE’s platform for developing applications for the Industrial Internet is already transforming cities, hospitals and factories. Built on the Pivotal Cloud Foundry platform, Predix offers an operating system for developing, testing and delivering secure software that powers everything from safer jet engines, to more intelligent streets.
Wikibon just released their “2017 Big Data Market Forecast.” How rosy that forecast looks depends upon whether you look at Big Data as yet another technology exercise, or if you look at Big Data as a business discipline that organizations can unleash upon competitors and new market opportunities. To quote the research:
“The big data market is rapidly evolving. As we predicted, the focus on infrastructure is giving way to a focus on use cases, applications, and creating sustainable business value with big data capabilities.”
Leading organizations are in the process of transitioning the big data conversation from “what technologies and architectures do we need?” to “how effective is our organization at leveraging data and analytics to power our business models?”
We developed the Big Data Business Model Maturity Index to help our clients to answer that question; to be able to 1) understand where they sit today with respect to how effective they are in leveraging data and analytics to power their business models, and 2) what is the roadmap for creating sustainable business value with big data capabilities (see Figure 1).
So why do organizations struggle if it’s not a technology or an architecture challenge? Why do organizations struggle when the path is so clear, and the business and financial benefits to compelling?
I believe that organizations fail in creating sustainable business value with big data capabilities because of the Peter Principle.
“Peter Principle”: The Destroyer of Great Ideas
The Peter Principle is a management theory formulated by Laurence J. Peter in 1969. It states that the selection of a candidate for a position is based on the candidate’s performance in their current role, rather than on abilities relevant to the intended role. Thus, employees only stop being promoted once they can no longer perform effectively – that “managers rise to the level of their incompetence.”
There are two key points in this concept that are hindering the wide spread adoption of data and analytics to power – or transform – an organization’s business models:
How do you teach the existing generation of management to “think differently” about how to leverage data and analytics to power their business models? How does one get an organization to open their minds and stop focusing on just “paving the cow path,” but instead focus on data and analytics-driven innovation? Let’s try a little exercise, my guinea pigs!!
Decision Modeling: Predictions Exercise
The Challenge: Can we transform business thinking by changing the verb from “automate” to “predict?” Instead of focusing on automating what we already know, in its place let’s try focusing on “predicting” what is likely to happen and “prescribing” what actions we should take.
“Automate” assumes that the current process is the best process, when in fact; there may be opportunities to leverage new sources of data and new data science techniques to change, re-engineer or even delete the process. Can we drive a more innovative approach by instead of focusing on “automation,” we focus on what predictions (in support of key business decisions) we are trying to make and prescribing what actions we should take?
Let’s demonstrate the process using the Chipotle key business initiative of “Increase Same Store Sales.” (Note: this decision modeling exercise expands upon Step 8 in the “Thinking Like A Data Scientist” methodology).
Table 1 shows the results of this process for one use case (Increase Store Traffic Via Local Events Marketing) that supports the “Increase Same Store Sales” business initiative.
Table 1: Predictions Exercise Worksheet
In the workshop or classroom, we would repeat this process for each use case (e.g., improve promotional effectiveness, improve market basket revenues). This analytics-driven approach can bring more innovative and out-of-the box thinking to the organization.
Summary: The GE Story
A recent article titled “You Can’t Outsource Digital Transformation” discusses what GE is doing to prepare for–if not lead–digital business transformation disruption. To quote the article:
“It’s the threat of a digital competitor who skates past all the traditional barriers to entry: the largest taxi service in the world that owns no cars; or a lodging service without any real estate; or a razor blade purveyor without any manufacturing.”
The author, Aaron Darcy, describes what GE is doing to “think differently” – that is to unlearn and relearn – regarding digital business model disruption. This includes:
Nothing threatens the existence of your business like the Peter Principle. An organization’s unwillingness to “un-education / re-education” will ultimately be the undoing of the organization. Because as IDC believes “By 2018, 33% of all industry leaders will be disrupted by digitally enabled competitors.” Ouch.
The post Peter Principle: The Destroyer of Great Ideas…and Companies appeared first on InFocus Blog | Dell EMC Services.
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The recent deluge of rains in Northern California have flooded streets, brought down trees and plugged storm sewers. As I was trying to make my way around the neighborhood, I thought of a classroom exercise to help my MBA students to identify the use cases upon which they could focus data and analytics. In this exercise, I’m going to ask my students to pretend that they have been hired by the city to “Optimize Street Maintenance” after these rainstorms. In particular, the students need to address the following questions:
These are classic questions that I hear all the time when I meet with clients about their big data journeys. Let’s walk through how I’ll teach my students to address this challenge.
Step 1: Identify and Brainstorm the Decisions
“Where and how to start?” is such an open ended question. How does one even begin to think about that question? We recommend that organizations start by identifying the decisions that need to be made to support the targeted business initiative, which is “Optimize Street Maintenance” in this exercise.
I will break up the students into small groups (3 to 5 students) and ask them to brainstorm the decisions that need to be made with respect to the “Optimize Street Maintenance” initiative. Those decisions could include:
This brainstorming is much more effective when you have brought together the different business stakeholders who either impact or are impacted by the “Accelerate Street Maintenance” initiative (see Figure 1).
Some key process points about Step 1:
Finally, “all ideas are worthy of consideration.” This is the key to any brainstorming session; to create an environment where everyone feels comfortable to contribute without someone passing judgment about his or her thoughts or ideas.
Step 2: Group Decisions Into Use Cases
Next, we want to group the decisions into common subject areas or use cases (which is much easier to do if each decision is captured on a separate Post-It note). I will bring all the students together around the decisions on Post-it Notes, and have them look for logical groupings.
Looking over the decisions captured above, we can start to see some natural “Accelerate Street Maintenance” use cases emerging, such as:
Prioritize Streets and Intersections
Estimate Maintenance Effort
Optimize Maintenance Effort
Minimize Traffic Disruptions
Minimize Maintenance Costs
Improve Resident Communications
Increase Resident Satisfaction
See Figure 2 for an example of how the end point of Step 2 might look.
A key process point about Step 2:
Step 3: Prioritize Use Cases
Not all use cases are equal, and some use cases are dependent upon other use cases. The prioritization matrix takes the different business stakeholders through a facilitated process to prioritize each use case vis-à-vis its business value and implementation feasibility (see Figure 3).
For more details on the prioritization process, check out these blogs:
The news really surprised no one: “MD Anderson Benches IBM Watson In Setback For Artificial Intelligence In Medicine.” From the press release:
“The partnership between IBM and one of the world’s top cancer research institutions is falling apart. The project is on hold, MD Anderson confirms, and has been since late last year. MD Anderson is actively requesting bids from other contractors who might replace IBM in future efforts. And a scathing report from auditors at the University of Texas says the project cost MD Anderson more than $62 million and yet did not meet its goals.”
If big data were only about buying and installing technology, then it would be easy. Unfortunately, companies are learning the hard way that the “big bang” approach for implementing big data is fraught with misguided expectations and outright failures.
Organizations are so eager to realize the business benefits of big data, that they don’t take the time to do the little things first, like identifying and prioritizing those use cases that offer the optimal mix of business value and implementation feasibility. While I applaud all efforts to cure cancer (my mom died from cancer, so I have a vested interest like so many others), sometimes “curing cancer” might not be the best place to start. Identifying and prioritizing those use cases that move the organization towards that “cure cancer” aspiration is the best way to achieve that goal.
The post Decisions Exercise: Identifying Where and How To Start the Big Data Journey appeared first on InFocus Blog | Dell EMC Services.
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