When Scientist and Engineer Becomes One

I'm so honored to be appointed IBM Distinguished Engineer, Hybrid Cloud for Systems. I will have the opportunities to lead collaboration within IBM Systems and cross-unit with IBM Cloud & Cognitive and Red Hat to develop and deliver Hybrid Cloud & AI use cases, solutions and industry architecture.

Having blazed trails in the fields of science and technology, now I'm ready to march with thousands of IBMers, Red Hatters and partners to deliver innovation that matters: Hybrid Cloud for smart city, edge-to-core smart manufacturing, 5G-enabled autonomous driving, and high performance Data & AI to fight fraud, predict failure, detect pandemics and stop cancer.

For my mentors, coaches, leaders, co-innovators, friends and families, thank you!!

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Looking back:

Growing up, I admired being an engineer, just like my mom who was the chief engineer for a factory.

In college, started in science but trying to figure out what to do

In grad school, majored in molecular biology and found myself liking computers more than test tubes.

As research scientist, use computers to decode human genome

At IBM, designed supercomputers, storage, and software to help scientists worldwide

Today, the scientist and engineer become one, and I know exactly what to do next: #innovationmatters

Getting Your Data & App Ready for Precision Medicine

Biomedical research institutes and healthcare providers are dealing with an enormous growth of data, mainly unstructured, that is flowing from many sources – faster and faster. All types of data need to be captured, labeled, cleaned, stored, managed, analyzed, cataloged, protected and archived. The disparate data sources, types, ownership and governance create silos that impede data access, drive down efficiency, drive up costs, and slow times to insight and discovery.

The volume and complexity of data also drives the adoption of modern analytical frameworks such as big data (Hadoop and Spark) and AI (machine learning and deep learning) and applying it for the thousands of research and business applications (e.g. genomics, bioinformatics, imaging, translational and clinical). Supporting big data demands for rapidly evolving frameworks and workloads along with the collaborative nature of biomedical research requires comprehensive storage and compute capabilities.

Because of these challenges, it is imperative for the infrastructure and underlying architecture to become agile, data-driven and application-optimized – in short – becoming ready to advance precision medicine.

The Art of Possible

In 2014, I created and made public IBM Reference Architecture for Genomics to take on these challenges. It has evolved by taking on more workloads (eg medical imaging and clinical analytics) and expanded to include AI and Cloud. Since 2018, we renamed the architecture to IBM High Performance Data & AI (HPDA). It grew out of healthcare and life sciences industry use cases and leveraged IBM’s history of delivering best practices in high-performance computing, artificial intelligence and hybrid-multi Cloud. In fact, the basic HPDA framework was used to construct Summit and Sierra – currently two of the world’s most powerful supercomputers designed for data and AI. The architecture is designed to help life science organizations easily scale and expand compute and storage resources independently as demand grows, to ensure maximum performance and business continuity. It supports the wide range of development frameworks and applications required for industry innovation with optimized hardware as a foundation – without unnecessary re-investments in technology.

The HPDA reference architecture is deployable into software-defined infrastructure (SDI) that offer advanced orchestration and management capabilities. Currently it can support major computing paradigms such as traditional HPC, data lakes, large-scale analytics, machine learning and deep learning.

These capabilities then become the foundation for developing and deploying applications for fields such as genomics, imaging, clinical, real-world evidence and Internet-of-things. The HPDA architecture can be implemented on-premise in a local data center or off-premise in a private or public cloud. Our team and clients have also demonstrated and deployed advanced use case and platforms in hybrid cloud.

The architecture has a “Datahub” layer designed to manage the ocean of unstructured data that is siloed in disparate systems using advanced tiering functions, peering, and cataloging. The advanced capabilities allow the data to be captured very rapidly, stored safely, accessed easily and be shared globally in the most secured and regulation-compliant way wherever and whenever the data is needed.

The second layer is the “Orchestrator” which brings efficient scalable computational capabilities based on a shared infrastructure to schedule millions of jobs and deploy policy-driven resource management with critical functions like parallel computing and pipelining for faster time to insights and better outcomes. With the advancement of Cloud technologies such as containers and container orchestration, Orchestrator is now fully capable of deploying and manage Cloud-ready or Cloud-native workloads.                                                           

                          

The capabilities of the Datahub and Orchestrator that were designed as two separated abstraction layers can be extended all the way to the cloud. They work together moving data to balance and control the dispatching of workloads avoiding bottlenecks that may cause jobs to run slower. In the latest edition of HPDA, we introduced Unified Data Catalog use case support to further improve the integration of the two layers. Imagine now that every applications will have its data, metadata, provenance and results recorded and tracked automatically. This meta-information will then be fed into a governance catalog or platform to facilitate data exchange, ensure privacy compliance and secure access to data and information.

The logic behind this reference architecture was to free workloads from hardware constraints: assign the optimal resources needed (CPU, GPU, FPGA, VM) and address unpredictable workload requirements. This architecture is portable to different infrastructure providers, deployable to different hardware technologies and allows the workloads to become reusable through validated and hardened platforms.

This true data-driven, cloud-ready, AI-capable solution is based on deep industry experience and constant feedback from leading organizations that are at the forefront of precision medicine.

The Values to Reality

Users and infrastructure providers are achieving valuable results and significant benefits from the HPDA solution.

The key values for users:

• Ease-of-use: self-service App Center with a graphical user interface based on advanced catalog and search engines that allows users to manage the data in real-time with maximum flexibility.
• High-performance: cloud-scale data management and multicloud workload orchestration allows users to place data where it makes sense and provision the required environment for peak demand periods in the cloud, dynamically and automatically, for as long as needed, to maximize performance.
• Low cost: policy-based data management that can reduce storage costs up to 90% by automatically migrating file and object medical data to the optimal storage tier.
• Global collaboration: allows multi-tenant access and data sharing that spans across storage systems and geographic locations enabling many research initiatives around the globe with a common reference architecture to establish strategic partnerships and collaborate.

The key values for infrastructure providers:

• Easy to install: a blueprint that compiles best practices and enables IT architects to quickly deploy an end-to-end solution architecture that is designed specifically to match different use cases and requirements
• Fully tested: IT architecture based on a solid roadmap of future-ready proven infrastructure that can easily be integrated into the existing environment protecting already made investments, especially the hardware purchase and cloud services.
• Global Industry Ecosystem: wide ecosystem to align with the latest technologies for hybrid multicloud, big data analytics and AI to optimize data for cost, compliance and performance expected by end users.

Today, there are over 100 enterprise deployment of HPDA in world's largest cancer center, genome center, precision medicine projects, and research hospitals. Some large pharma and biotech have also started to adopted the architecture in their multi-Cloud infrastructure.

To learn more about HPDA and its use cases and adoption in precision medicine and drug discovery, please download and read my IBM Redbook  at http://www.redbooks.ibm.com/abstracts/redp5481.html

My Second eBook Published: IBM HPDA Referene

Today, my second eBook by IBM Redpaper was published!  

This ublication provides an update to the original description of IBM Reference Architecture for Genomics. It expands the reference architecture to cover all of the major vertical areas of healthcare and life sciences industries, such as genomics, imaging, and clinical and translational research.

The architecture was renamed IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences to reflect the fact that it incorporates key building blocks for high-performance computing (HPC) and software-defined storage, and that it supports an expanding infrastructure of leading industry partners, platforms, and frameworks.

The reference architecture defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data, which can be deployed on-premises, in the cloud, or as a hybrid of the two. IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks that are frequently encountered in personalized healthcare initiatives, and in compute-intensive and data-intensive biomedical workloads.

This reference architecture also provides a framework and context for modern healthcare and life sciences institutions to adopt cutting-edge technologies, such as cognitive life sciences solutions, machine learning and deep learning, Spark for analytics, and cloud computing. To illustrate these points, this paper includes case studies describing how clients and IBM Business Partners alike used the reference architecture in the deployments of demanding infrastructures for precision medicine.

This publication targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing life sciences solutions and support.

Follow this link to download the publication digitally. 

Making Dark Medical Data Visible

According to a recent market survey conducted by HIMSS Media, an average of 66 percent of the unstructured data in healthcare enterprises remains inaccessible and unavailable for patient care decisions. Because a major portion of this data seems to be invisible and hard to manage, many organizations are overwhelmed by the abundance of data, rather than enabled and empowered.

In addition, InfoWorld published that most data scientists spend only 20 percent of their time on actual data analysis and 80 percent of their time finding, cleaning, and reorganizing huge amounts of data – an unfortunate and inefficient data strategy, indeed.

In an industry that is becoming even more data-driven and evidence-based, organizations must embrace new technologies that will help them manage data efficiently and uncover the many meaningful insights still hidden in their mountains of data.

Deploying a robust and holistic data strategy that enables healthcare organizations to leverage all their growing volumes of data usually dispersed across geographic and organizational boundaries is key to accelerating precision medicine and impacting the health of individuals. At the heart of this strategy lies a scalable, agile and sustainable IT foundation that can support the demands of precision medicine and is based on three key principles:

First, the architecture must be based on software-defined infrastructure (SDI) solutions that offer advanced policy-driven data and resource management capabilities. Although this infrastructure is built upon hardware solutions with chips and processors and data that resides in storage systems, we need to remember that the ability to operate, orchestrate, protect and manage data intelligently exists in the software, or in the middleware that sits between the hardware and the applications. This IT architecture must also support an open framework and the hundreds, if not thousands, of applications being developed in the areas of genomics, imaging, clinical analytics, artificial intelligence and deep learning. Many of these applications are isolated in functional and operational siloes, creating the need for a shared compute and storage infrastructure based on advanced software to consolidate and transform static, siloed systems into dynamic, integrated, and intelligent infrastructure, resulting in faster analytics and greater resource utility.

Second, any solution needs to follow a proven reference architecture that has been fully-tested. The deep experience we have at IBM with world-class healthcare and life sciences customers has taught us that software capabilities can easily be dictated by the underlying hardware building blocks (CPU vs GPU, on-premises vs cloud, x86 vs Power9) and even more so by the applications they need to serve. Without a consistent framework and roadmap in the form of a reference architecture, things can fall apart very quickly. Initially, building a robust data strategy and underlying IT infrastructure may take more effort, but the value and benefits that your organization can gain will be much more long-lasting and wide-reaching in terms of speed, scale, collaboration, ease of use and costs.

Finally, the architecture needs to be part of a global ecosystem. We all realize that collaboration does not exist within the four walls of a single organization anymore. We see many research initiatives between top cancer centers, genome centers and large pharma R & D and biotech companies that involve strategic partnerships around the globe. The common reference architecture they all use enables them to easily collaborate and share data.

For example, a research hospital can develop a cancer genomics pipeline and share it with another institution quickly, either by sending the XML-based script or publishing it in a cloud-based portal like an application store. We have also started to see early examples of data sharing using metadata and RESTful APIs. Based on this approach, parallel communities or consortia are being formed for digital medical imaging, translational research and big data analytics. This makes parallel discovery possible.

The journey of the reference architecture

IBM’s high-performance data and AI (HPDA) architecture for healthcare and life sciences was designed to boost medical research efforts. It is based on best practices that have been tested with top healthcare solution providers and partners, and most importantly with customers that are at the forefront of precision medicine, such as Sidra Medicine.

For the first generation of the references architecture that was established in 2013, we designed a “Datahub” as an abstraction layer for handling demanding genomics requirements such as high-throughput data landing, information life cycle management and global namespace regardless of sharing protocol. These requirements could sometimes be met easily on a single workstation or small cluster, but the capability to handle hundreds of servers and petabytes of data is what made the Datahub so unique and essential. And what made the Datahub even more valuable was its intrinsic scalability to start small (or big) and grow and scale out rapidly based on the workloads. As the next-gen sequencing technologies were rapidly advancing, the data and workloads could grow at a rate of 100% every six months. The Datahub fulfills these requirements through software in concert with storage building blocks (flash, disk and tape library) that support tiering based on performance and cost objectives.

We also designed an “Orchestrator” as the second abstraction layer for handling application requirements and mapped it towards the computing building blocks. It has specified functions such as parallel computing and workflow automation which can be fulfilled by software in concert with computing resource such as an HPC cluster or virtual machine farms.

This software-defined blueprint was essential to future-proof the infrastructure and sustain the usability of applications so that the hardware building blocks could be expanded or replaced without impacting the operation of the system, the running of the application, and ultimately the user experience.

The reference architecture has continued to evolve throughout the years to reflect the enormous changes on numerous fronts that healthcare and life sciences organizations have had to face due to disruptive market forces that constantly reshape the industry and the way patient care is delivered.

The main investments we’ve made in the last two years were focused on positioning the reference architecture as a true data-driven, cloud-ready, AI-capable solution that addresses very complex data at scale and the most demanding analytics requirements with cost-effective high-performance capabilities.

At this point, the second generation of the reference architecture has been developed and includes advanced features and solutions based on feedback we’ve received from users in fields of research and clinical practices such as genomics, metabolomics, proteomics, medical imaging, clinical analytics, biostatistics and even real-world evidence (RWE) and the internet of things (IoT).

One of the most exciting things that we observe is that this collaboration across fields has actually started to bring more and more users together to work and share their experiences.

We are fortunate to witness and document these challenges and the needs of leading institutes at the frontiers of precision medicine. Thanks to their HPDA-based solutions, they are experiencing significantly faster times to results, along with many other benefits. The results can be a genomics analysis of clinical variants for patients, or an AI model developed to diagnose Alzheimer’s disease, or new biomarkers for cancer. In all these cases, traditional desktop computing could no longer keep up with the workloads or the data storage. Previously, these users had to wait days or even weeks for data to be transferred and loaded, then even longer for processing and analysis. But after implementing the HPDA reference architecture – not anymore.

Learn more about some of the leading precision medicine initiatives around the globe supported by IBM’s high performance data and AI (HPDA) architecture at my upcoming talk at Bio-IT world.

An Architecture Built for High-Performance Healthcare

“The healthcare industry is exploding with new information, and as we continue to move towards advanced clinical analytics, we know this trend is not slowing down,” states Bryan Fiekers, Senior Director of Research at HIMSS Analytics, in a study that focus on modernizing healthcare technology.

This statement essentially sums up one of the biggest challenges facing healthcare organizations today. And it also highlights one of the most powerful opportunities for those who want to be at the forefront of transforming biomedical research and advancing precision medicine.

Within the literally exabytes of data flooding into medical research and service delivery organizations, these days lie insights that can save lives, lead to new discoveries, and transform patient care – for the organizations willing to optimize the way they leverage, analyze, and utilize their most valuable resource: data.

This is the message that IBM Systems will bring to the 2019 HIMSS Annual Conference and Exhibition (HIMSS19) in Orlando, Florida, on February 11-15. Behind this message exists a proven solution that supports some of the largest leading precision medicine initiatives around the globe – the IBM high-performance data and AI (HPDA) reference architecture for healthcare– a foundation that can help organizations leverage the opportunities inherent in their enormous new data sets.

Whether you are a health insurer that need to provide quality services to help people make better decisions to maintain their health; a university researcher publishing journal articles looking to win your next round of grant funding; a data scientist in commercial R&D organization progressing potential drugs through clinical trials; or a physician in a hospital delivering treatments that will give patients the best clinical outcomes – with HPDA you will experience new records for speed and scale. In the 21st century, healthcare stakeholders need a reliable, flexible, and secure computing platform that meets their diverse application requirements.

Through our work with many international organizations and healthcare industry business partners, IBM has seen that organizations that adopted our HPDA reference architecture for genomics, imaging, EHR, deep learning and other big data initiatives in biomedical research have achieved valuable results and significant benefits.

At HIMSS19, IBM will feature a number of real-world examples of HPDA architectures and components helping organizations transform their capabilities to gain insight from massive datasets.

For example, Aetna improves personalized care by providing real-time access to information and quality services that help tens of millions of people make better decisions to maintain their health and manage costs. The session at the IBM booth #6459 at HIMSS19 will show how the inclusion of high-performance IBM storage and software-defined infrastructure improved the overall performance and speed to delivery of the enterprise data warehouse, allowing Aetna to analyze massive amounts of data faster, while addressing compliance and privacy-related concerns – and cutting costs.

Booth visitors will also learn how Thomas Jefferson University is coupling large collections of diverse biological datasets with high-performance computing to drive new advances in precision medicine. The Jefferson team has generated evidence from processing massive amounts of data to demonstrate that a person’s sex, race/ethnicity, and geographic origin affect predisposition for disease, how a disease might progress, and the therapeutic options that might be most relevant to pursue.

The IBM HPDA architecture is built on IBM’s history of delivering best practices in high-performance computing (HPC). In fact, the basic HPDA framework was used to construct Summit and Sierra – currently two of the world’s most powerful supercomputers designed for data and AI. The architecture is designed to help healthcare and life science organizations easily scale compute and storage resources as demand grows, and to support the wide range of development frameworks and applications required for industry innovation – all without unnecessary re-investments in technology.

Our reference architecture for healthcare consists of key infrastructure components from IBM’s high-performance compute and storage portfolio, and it supports an expanding ecosystem of leading industry partners. The IBM HPDA architecture is based on IBM Storage and IBM Software-defined Storage solutions, software-defined infrastructure, IBM POWER9 servers, and the IBM POWERAI comprehensive AI platform. It defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data within the constraints of limited IT budgets. IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks frequently encountered in personalized healthcare initiatives and other compute- and data-intensive biomedical workloads.

Introducing the new capabilities of the IBM HPDA architecture and presenting customer success stories aren’t the only highlights IBM Systems will feature at HIMSS19. We will also host a breakfast briefing panel discussion covering the critical role that an HPDA architecture can play in helping healthcare organizations solve some of the biggest research and analytics challenges they face. In this panel, Aetna, representatives from Louisiana State University, and Froedtert Health will share how they are accelerating discoveries and improving personalized care by using flexible, scalable, secure, and cost-effective high-performance systems that can leverage the cloud to ensure the highest levels of data availability, reliability, and compliance.

Come meet our IBM Systems HPDA industry experts for a demo at the IBM booth #6459. And before you go, get a glimpse of our full sessions schedule in this blog – Making precision medicine a reality.

HPDA18 Debut

Here is my talk at HIMSS 2018 introducing High Performance Data & AI reference architecture to the public

Bigger and Better Data

Bigger and Better Data - My keynote talk at Wayne State University Big Data Symposium 2018 on 3/22/2018)

Published! My First eBook on Genomics

My first ebook on genomics was published by IBM Redbook last week. It was based on and expanded from a couple of writings I had in 2014 (editorial, solution brief and whitepaper) on the subject of genomics reference architecture (@Genetecture) and its applicability in building IBM genomics infrastructure leveraging the architecture and ecosystem.

Below is the abstract of the publication titled "IBM Reference Architecture for Genomics: Speed, Scale, Smarts":

Genomic medicine promises to revolutionize biomedical research and clinical care. By investigating the human genome in the context of biological pathways, drug interaction, and environmental factors, it is now possible for genomic scientists and clinicians to identify individuals at risk of disease, provide early diagnoses based on biomarkers, and recommend effective treatments. 

However, the field of genomics has been caught in a flood of data as huge amounts of information are generated by next-generation sequencers and rapidly evolving analytical platforms such as high-performance computing clusters. 

This data must be quickly stored, analyzed, shared, and archived, but many genome, cancer and medical research institutions and pharmaceutical companies are now generating so much data that it can no longer be timely processed, properly stored or even transmitted over regular communication lines. Often they resort to disk drive and shipping companies to transfer raw data to external computing center for processing and storage, creating an obstacle for speedy access and analysis of data. 

In addition to scale and speed, it is also important for all the genomics information to be linked based on data models and taxonomies, and to be annotated with machine or human knowledge. This smart data can then be factored into the equation when dealing with genomic, clinical, and environmental data, and be made available to a common analytical platform. 

To address the challenging needs for speed, scale, and smarts for genomic medicine, an IBM® end-to-end reference architecture has been created that defines the most critical capabilities for genomics computing: Data management (Datahub), workload orchestration (Orchestrator), and enterprise access (AppCenter). 

The IBM Reference Architecture for genomics can be deployed with various infrastructure and informatics technologies. IBM has also been working with a growing ecosystem of customers and partners to enrich the portfolio of solutions and products that can be mapped into the architecture.
This IBM Redpaper™ publication describes the following topics:

• Overview of IBM Reference Architecture for Genomics

• Datahub for data management

• Orchestrator for workload management

• AppCenter for managing user interface

You can access and download the ebook here

Airport vs Helipad - A Few Thoughts on Datahub

I was asked often to compare and contrast a software-defined storage (like GPFS/Spectrum Scale) with a closed-in solution like Panasas, BlueArc or Isilon. Instead of providing a product/offering level analysis, I'd just talk about the difference at the architectural level.

GPFS/Spectrum Scale are part of IBM's software defined storage family. It is also one of the solutions under PowerGene Datahub -- a software-based abstraction layer for storage and data management. The scalability, extensibility and flexibility are the top three hallmarks of Datahub. It will be extremely challenging for a close-in storage solution to come close to just two out of these three criteria.

1) Datahub defines a storage management capability that is extremely scalable in terms of capacity and performance. We are talking about possibly exabytes of data with GB/sec performance with trillions of metadata. The I/O should be well managed based on policy and metadata so that linear scalability can be accomplished. As a real example, if we start with one building block delivering 10GB/sec performance, I will challenge any closed-in solution to deliver 300GB/sec performance with 30 building blocks connected together -- almost 100% linear scalability.  We actually proved this could be done with a Federal Lab using Datahub-based GPFS solution.

2) Datahub defines capabilities beyond I/O & storage management for functions such as data movement (policy-based tiering), sharing (policy-based caching or copying) and metadata, each of which can be extended seamlessly from a local storage cluster to a grid and to public cloud. Given the break-neck speed of technological and research advancement in genomic medicine and high-performance computing, any R&D institutions should expect and demand this level of flexibility -- matching use/business cases as much as possible at software/architecture level to minimize locking-in of specific hardware (disk & processor) and vendors (including my own company, IBM).

3) As a software-defined architectural element, Datahub can be leveraged for many other useful infrastructure building blocks -- a) Flash-based building block for high-performance metadata management (scanning 600 million file < 10min at a leading research center in NYC; b) GPFS/tape active archive that can drastically reduce the cost of storage explosion while providing quick/easy access to data under the single global name space for archive and protection -- on this front, Datahub solution can be on par or even beat cloud-based cold storage.

If PowerGene helps building (or leasing) airports (Datahub), planes/engines (Orchestrator) and traveller portal (AppCenter) that can extend from the Ground into the Cloud, these closed-in or NAS-like solutions maybe seen as a pre-assembled helipad -- it's easy to get there but hard to go far and beyond.

Fast and Furious Engine for Computing

As we are designing and building PowerGene Pipeline v2 (PG-P2), I started to document the feature and function that can define a true workflow engine that can empower the world of scientific and analytical computing. If our data scientists, researchers, and even technologists desire a next generation of race car to take them to the next level of competition, then there should be minds thinking about inventing and building a fast and furious engine.

So here are my top 10 list for software defined workflow engine (SDWE). 

Abstraction — Of workloads from their physical implementation, thus decoupling a resource from its consumer. Abstraction enables definition of logical models of application or workflow (flow definition) that can be instantiated at the time of provisioning, thus enforcing standardization and enabling reusability. .

Orchestration - as applied to workflow, going beyond a single server or a cluster such that workloads with various architectural requirement can be optimally linked to available resources that now become transparent to the users and applications.

Automation — Beyond script-based automation, enabling automation of tasks, jobs and workflow across resource domains with built-in policy management for enforcement and optimization.

Standardization - Of workflows by a common set of naming standards, version control, runtime logging and provenance tracking

Customization - Of workloads into functional building blocks then connecting them into logical network, thus enabling workflow to be quickly composed or recomposed from proven workloads or subflow.

Visualization - Of the runtime environment through graphical user interface, as well as the final output using third-party visualization engine.

Scalability - Leverage world-class software defined storage infrastructure for extreme scalability. Supporting hundreds of pipelines that can run in parallel and scales to hundreds of thousands of concurrent jobs in a shared resource pool.

Manageability - The ability to start, suspend, restart and completely terminate the workflow manually (by user), as well as policy-based management of pipeline events (job success, failure, branching, convergence, etc).

Reusability - The ability to rerun the same pipelines (manually or by policy) or redeploy it as an embedded elements in a higher-level workflow.

Accessibility - Fine-grain, role-based access to make the solution available to only those who needs it.