Will geoscience go for a shared service environment?
As the previous “How can we help geoscience to move their data to shared services” blog noted, unless we align the stakeholders, get a clear line of sight on their needs, and focus on earning trust and demonstrating value, the answer is no. But let’s say we are moving that way. How do we get started to fund such an approach?
Well, first off, the current grant and programmatic funding models are not designed to develop shared services or interoperable data for the geosciences. Today, there are many geoscientists who are collaborating between disciplines and as a result improving the quality of knowledge and scaling the impact of their research. It is also well established that the vast majority operate individually or in small teams. Geoscientists, rightly so, continue to be very focused on targeted scientific objectives and not on enabling other scientists. It is a rare case when they have the necessary resources or skills. With the bright shiny object of data driven science /Big data; do we have the Big Head wagging the body of the geoscientist community? Xentity sees opportunities to develop funding strategies to execute collaborative performance-based cross discipline geosciences. It has been this way since World War II really expanded upon its war-time successful onesy-twosy grants to universities since then. There has been some movement towards hub and spoke grant funding models, but we are still out to get our PhD stripes, get our CVs bigger, and keep working with the same folks. I know it is a surly and cynical view. OK, the real is they are doing amazing work, but in their field, and anything that slows down their work, for greater good, is lacking incentive.
Also, there are few true shared services that are managed and extended to the community as products and services should be. Data driven science, which is out fourth paradigm of science, has been indirectly “demanding” scientific organizations and their systems to become 24×7 service delivery providers. We have been demanding IT programmers to become service managers, scientist to become product managers, or data managers. With a few exceptions, it has not worked. Geoscientists are still struggling to find and use the basic data/metadata, produce quality metadata (only 60% meet quality standards per EarthCube studies) for their own purposes, let alone making the big leap to Big data and analytics. Data driven science requires not only a different business or operating model, but a much clearer definition of the program, as well as scientist’s roles and expectations. It requires new funding strategies, incentive models and a service delivery model underpinned by the best practices of product management and service delivery.
Currently, and my favorite, there is limited to no incentive for most geoscientist to think beyond their immediate needs. If geoscientists are to be encouraged to increase the frequency and volume of cross-discipline science, there needs to be enablement services, interoperable data and information products that solve repetitive problems and provide incentive for participation. We need to develop the necessary incentive and management models to engage and motivate geoscientist, develop a maturity plan for the engineering of shared geoscience services and develop resourcing strategies to support its execution. Is this new funding models, new recognition models, new education, gamification, crowdsourcing, increasing competition, changing performance evaluation? Not sure as any changes to “game” rules can and usually introduces new loopholes and ways to “game” the system.
The concept of shareable geoscience data, information products and commodity or analytical computing services has an existing operating precedent in the IT domain –shared services. Shared services could act as a major incentive for participation. An approach would identify the most valuable cross cutting needs based on community stakeholder input. The team would use this information to develop a demand driven plan for shared service planning and investment. As an example, a service-based commodity computing platform can be developed to support both the Big Head and Long Tail and act as incentive to participation and perform highly repetitive data exchange operations.
How does one build and sustain a community as large and diverse as the geosciences?
The ecosystem of geoscience is very complex from a geographic, discipline and skill level point of view. How does one engage so diverse a community in a sustainable manner? “Increased visibility of stakeholder interests will accelerate stakeholder dialogue and alignment – avoiding “dead ends” and pursuing opportunities.” The stakeholders can range from youthful STEM to stern old school emeritus researchers; from high volume high frequency data producers of macro scale data to a single scientist with a geographically targeted research topic. It is estimated that between 80-85% of the science is done in small projects. That is an enormous intellectual resource that if engaged can be made more valuable and productive.
Here is a draft target value chain :
The change or shift puts a large emphasis on upfront collaborative idea generation, team building, and knowledge sharing via syndication, and new forms of work decomposition in the context of crowd participation (Citizen Science and STEM). The recommended change in the value chain begins to accommodate the future needs of the community. However, the value chain becomes actionable based on the capabilities associated to the respective steps. Xentity has taken the liberty to alliteratively define these four classes of capabilities or capability clusters as:
Encouragement, Engagement, Enablement, and Execution.
Encouragement capabilities are designed to incentivize or motivate the scientist and data suppliers to participate in the community and garner their trust. They are designed to increase collaboration, the quality and value of idea generation and will have a strong network and community building multiplier effect.
How can new scientific initiatives be collaboratively planned for and developed?
How can one identify potential collaborators across disciplines?
How can one’s scientific accomplishments and recognition be assured and credited?
What are the data possibilities and how can I ensure that it will be readily available?
How can scientific idea generation be improved?
Incentives based on game theory
Collaboration, crowd funding, crowd sourcing and casting
Project Management and work definition
Credit for work Services
Engagement Capabilities include the geoscience participant outreach and communication capabilities required to build and maintain the respective communities within the geoscience areas. These are the services that will provide the community the ability to discuss and resolve where the most valued changes will occur within the geosciences community, who else should be involved in the effort?
What participants are developing collaborative key project initiatives?
What ideas have been developed and vetted within the broadest set of communities?
Who, with similar needs, may be interested in participating in my project?
How can Xentity cost share?
Customer Relationship Management
Communications and Outreach
Social and Professional Networking
Enablement capabilities are technical and infrastructure services designed to eliminate acquisition, data processing and computing obstacles and save scientist time and resources. They are designed to solve frequently recurring problems that affect a wide variety and number of geoscience stakeholders from focusing on their core competencies – the creation of scientific knowledge. Enablement services will have a strong cost avoidance multiplier effect for the community on the whole if implemented and supported.
How does one solve data interoperability challenges for data formats and context?
How do I get data into the same geographic coordinate system or scale of information?
How can I capture and bundle my Meta information and scientific assets to support publication, validation and curation easily?
How can I get access extensible data storage throughout the project lifecycle?
Where and how can I develop an application with my team?
How can I bundle and store my project datasets and other digital assets later retrieval?
How can I get scalable computing resources without having to procure and manage servers to complete my project?
Spatial Encoding and Transformation
Execution Capabilities are comprised of the key management oriented disciplines that are required to support shared infrastructure, services or to help evolve a highly federated set of valuable assets “edges” to be more useable and valuable to the evolving community over time.
How do we collectively determine what information might require a greater future investment?
What are the right incentives in the grant processes?
What are the future funding models?
What models should be invested in?
Which technologies should be evaluated for the shared assets?
What upcoming shared data or technology needs are in common to a large number of participants?
IT Service Management (ITSM),
Data Supply Management,
Data Life Cycle Management
Grants and processing
So, why did we develop these classes of capabilities?
They represent, at the macro level, a way to organize a much larger group of business, operating and technical services that have been explicitly discussed in NSF EarthCube efforts over the last 3-4 years. We then been derived these outputs from analysis and associate them to the most important business drivers. Check out this “draft” relationship of capabilities drivers and rational
The best way to create communities and identify common needs and objectives, begin to build trust and value awareness; bring the respective communities into an environment where they can build out their efforts and sustain collaborative approaches.
Agency (how to navigate planned versus emergent change), intellectual property rights, infrastructure winners and losers, agreement on data storage, preservation, curation policies and procedures, incentives to share data and data sharing policies, and trust between data generators and data users.
The best models to incentivize scientist’s and data producers to participate and collaborate. Xentity have developed game theory based approaches and large scale customer relationship management solutions
Social and cultural challenges: Motivations and incentives, self-selected or closely-held leadership, levels of participation, types of organizations, and collaboration among domain and IT specialists)
The most costly data processing obstacles – The lowest common denominator – highest impact problem. A common problem found in shared service environments. We have developed enterprise service analysis tools for cost benefit for the DOI geospatial community, so we have seen this work
80% of scientist data needs can be expressed as standard data product, and 80 % of scientist time is spent getting data into proper form for research analysis
A governance model that will increase the “edge effect” between the legacy and future capabilities and a very diverse set of communities. Simple planning capabilities that empower scientist to work complex cross disciplines ideas amongst themselves, define work and coordinate with the power of the crowd. We have designed collaborative environments and crowd based frameworks for data collection and analysis with corresponding performance management system.
Conceptual and procedural challenges: Time (short-term funding decisions versus the long-term time-scale needed for infrastructures to grow); Scale (choices between worldwide interoperability and local optimization);
So why don’t we do it?
Well, this does introduce an outside approach into a closed knit geoscience community who is very used to solving for themselves. Having a facilitated method from outside consulting or even teaming with agency operations who have begun moving this route for their national geospatial data assets is not seen as something fits their culture. We are still learning of hybrid ways we can collaborate and help the geoscientists setup such a framework, but for now it is still a bit foreign of a concept, and while there is some awareness by the geoscientist community to adopt models that work for other sectors, industries, operational models, the lack of familiarity is causing a lot of hesitation – which goes back to the earn trust factor and finding ways to demonstrate value.
Til then, we will keep plugging away, connecting with the geoscience community in hopes that we can help them advance their infrastructure, data, and integration to improve earth science initiatives. Until then, we will remain one of the few top nations without an operational, enterprise national geoscience infrastructure.