BLOGS

Estimating the Ginormous Growth of Genomics Data and Storage

Genomic data science is a field of study that enables researchers to use powerful computational and statistical methods to decode the functional information hidden in DNA sequences. Our ability to sequence DNA has far outpaced our ability to decipher the information it contains, so genomic data science will be a vibrant field of research for many years to come.

Researchers are now generating more genomic data than ever before to understand how the genome functions and affects human health and disease. These data are coming from millions of people in various populations across the world. Much of the data generated from genome sequencing research must be stored, even if over time, older information is discarded. The demand for storage is colossal with increased and new areas of life sciences research.

New areas of life sciences research are driving up data volumes

We identified four major life sciences research areas that are generating large volumes of data. For each area, we estimated the demand for the instruments that are producing this data. We then estimated average data generated per year by each of the instruments. The total storage demand over the study scope period is obtained by multiplying the number of sequencing instruments with the average data generated by each instrument.

Following areas of life sciences were considered in the data generation estimation:

Next-generation sequencing (NGS) is a massively parallel sequencing technology that offers ultra-high throughput, scalability, and speed.

RNA sequencing (RNA-Seq) uses the capabilities of high-throughput sequencing methods to provide insight into the transcriptome of a cell.

Spatial transcriptomics are methods designed for assigning cell types (identified by the mRNA readouts) to their locations in the histological sections. This method can also be used to determine subcellular localization of mRNA molecules.

Cryogenic electron microscopy (cryo-EM) is a cryo microscopy technique applied on samples cooled to cryogenic temperatures. This approach has attracted wide attention as an alternative to X-ray crystallography or NMR spectroscopy for macromolecular structure determination without the need for crystallization.

Single-cell sequencing technologies refer to the sequencing of a single-cell genome or transcriptome, so as to obtain genomic, transcriptome or other multi-omics information to reveal cell population differences and cellular evolutionary relationships.

How to quantify the colossal storage needs?

We made the following assumptions to estimate the storage needs between years 2021 through 2028.

1. We assumed the information provided in the paper[1] “Big Data – astronomical or genomical” published in 2015 as the basis of our calculation. In the year 2015, the authors working with various subject matter experts surmised that in 2015, there were 2500 gene sequencing instruments and the storage need at that time was 9 Petabytes (PBs).
2. Based on their and other research studies, they looked into two possibilities
   1. Storage needs will double every 12 months (conservative case)
   2. Storage needs will double every 7 months (aggressive case).
3. In our estimation exercise, we assumed that the growth rate will be the average of the above two cases.
4. Based on our discussion with experts, we assumed storage needs per RNA, Spatial transcriptomics, Single-cell sequencing instrument will be 4,8, and 100 times that of an NGS sequencing instrument respectively.
5. We worked with a Grand View Research, a market research company to estimate the global volumes of NGS sequencing, RNA sequencing, Spatial transcriptomics, single cell sequencing and Cryo EM instruments for the period between 2021 thru 2028.
6. The volume of each instrument type multiplied by unit storage requirement for each category provided the total storage need. We also assumed that each year, 80% of the data will be retained (i.e.,20% discarded).
7. The following table provides a summary of our estimate.

From the table, we see that the need for storage for sequencing is around 54 Exabytes in 2028 (assuming a retention of 80%). For this exercise, we have made many assumptions re: growth, sequencing technologies, retention, and size based on what we know today. One thing is certain: storage needs for life sciences and genomics will only increase, and it will be ginormous.

[1] https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002195

Published by: Srini Chari

As the world gathers to fight climate change, let’s recognize the critical role of HPC, AI and Cloud Computing

This week in Glasgow, the COP26 summit will bring global leaders together to accelerate action towards combating climate change. This is happening as energy consumption, mostly with polluting fossil fuels, is at an all-time high. This may seem good for the oil, gas, and coal industries, but it isn’t. The grave reality is that the fossil fuel industry is under immense pressure to mitigate climate change and decarbonize since high levels of energy consumption are causing unsustainable levels of C0₂ and other greenhouse gases.

So, the energy industry is undergoing a profound transition from fossil fuels to renewable and clean energy sources. As oil and gas companies decarbonize, they are looking into new and economically viable solutions including potentially becoming carbon-neutral energy companies.  For this they are investing heavily in physical infrastructure and boosting investments in High-Performance Computing (HPC) and artificial intelligence (AI) to innovate and quickly solve critical problems in this transition.

Oil and gas companies have always used HPC (seismic processing and reservoir simulation) and certain forms of AI technology (analytics) for decades to improve decision-making regarding exploration and production, and to reduce investment risks particularly as fossil fuels become harder to extract. Now as oil and gas companies transition, they must be able to handle newer interdisciplinary workloads in Geophysics, Computer-aided engineering (CAE), Life sciences, Combustion/Turbulence models, Weather modeling, Material science, Computational chemistry, Nuclear engineering, and Advanced optimization.

For this oil and gas companies are increasing their investments in HPC, AI and other innovative and agile solutions to handle exploding compute/storage requirements. As the use of AI and HPC continues to grow in the energy industry, cloud computing is making is easier to process spiky workloads and improve overall user experience. So, many oil and gas companies are using hybrid cloud solutions with several deployment options.

The harmful impact of climate change is becoming more severe day by day, threatening the survival of the planet. The entire world is coming together to fight it.  Oil and gas companies are doing their part and transitioning to renewable energy sources. This transition is hard and expensive but is made easier with novel applications and extensions of proven HPC and AI solutions oil and gas companies have used for years. As the world focuses this week on climate change, let’s also recognize the critical role HPC and AI play in solving one of mankind’s most pressing challenges.

You can learn more by reading this Hewlett Packard Enterprise whitepaper that Cabot Partners recently helped create.

Published by: Srini Chari

HPC and AI enable breakthroughs in genomics for better healthcare

Many Life Sciences organizations are using digital technologies to meet the needs and expectations of patients. These technologies help treat and manage diseases in new ways. HPC and AI solutions are at the forefront of this. They are needed to accelerate breakthroughs in large-scale genomics.

Genomics is a sub-discipline of molecular biology that focuses on the storage, function, evolution, mapping, and editing of genomes. It is a vital and growing field because it can improve the lifestyle and outcomes for patients. In the next five years, the economic impact of genomics is estimated to be in the hundreds of billion to a few trillion dollars a year.

Next-generation Sequencing (NGS), Translational and Precision Medicine

High-performance computing (HPC) and Artificial Intelligence (AI) are essential for Genomics primarily because they help speed up NGS which processes and reduces the amount of raw data into a usable format. NGS helps determine the sequence of DNA or RNA to study genetic variations associated with diseases or other biological phenomena.

After this NGS step, it is important to establish the relationship between genotypes to understand the influence individual DNA variances have on disease and medical outcomes. This is Translational medicine. The final step is Precision or Personalized medicine which customizes disease prevention and treatment for an individual based on their genetic makeup, environment, and lifestyle. This last step uses all the data collected using HPC and AI technology to create a personalized approach for the patient.

How HPC and AI help accelerate genomics

Genomics is difficult, but new HPC technology is making this process easier. NGS deals with complex algorithms that require a lot of memory to assemble and solve. HPC and AI solutions help speed up this process and make it cheaper and more accurate.  Using the cloud and new IT solutions, data sequence analysis also becomes easier and can be done at a much larger scale.

Translational medicine requires HPC solutions that can process a lot of data efficiently. This step looks for relationships between a lot of genes, DNA, and diseases and makes it possible to provide personalized treatment for a patient.

HPC and AI are game changers in life sciences. They make genomics easier and faster for healthcare providers, so they can provide highly effective personalized care for their patients. We expect HPC and AI in Life Sciences to continue grow even more rapidly especially in the post-COVID-era by enabling breakthroughs in vaccines, personalized medicine, and healthcare.

You can learn more by reading this Hewlett Packard Enterprise and NVIDIA whitepaper that Cabot Partners recently helped create.

Published by: Srini Chari

The Promise and Peril of RPA

RPA or Robotic Process Automation emulates human activity when interacting with digital software. It automates tedious and mundane business processes. Artificial Intelligence (AI) when integrated with RPA increases business value. AI can directly be used in bots to execute tasks without human intervention. This results in better efficiency, and improved customer and employee experiences.

RPA software revenue is growing rapidly despite economic disruptions caused by the COVID-19 pandemic and is projected to reach $1.89 billion in 2021 with double-digit rates through 2024.

Automating processes with RPA seems like a great solution in theory, but in practice, this isn’t the case. RPA has been successful for some but disappointing for others. While many organizations are relatively happy with their automation investment, most haven’t fully realized the ROI promised by RPA software vendors. For this reason, clients need to carefully evaluate the various RPA vendors before making this strategic investment.

Read this Cabot Partners paper for more details.

Published by: Srini Chari

TVO Analysis of Federated Learning with IBM Cloud Pak for Data

Analytics and AI are profoundly transforming how businesses and governments engage with consumers and citizens. Across many industries, high value transformative use cases in personalized medicine, predictive maintenance, fraud detection, cybersecurity, logistics, customer engagement and more are rapidly emerging. In fact, AI adoption alone has grown an astounding 270% in the last four years and 40% of organizations expect it to be the leading game changer in business[1]. However, for analytics and AI to become an integral part of an organization, numerous deployment challenges with data and infrastructure must be overcome – data volumes (50%), data quality and management (47%) and skills (44%)[2].

In addition, many companies are beginning to use hybrid cloud and multi-cloud computing models to knit together services to reach higher levels of productivity and scale. Today, large organizations leverage almost five clouds on average. About 84% of organizations have a strategy to use multiple clouds[3].

IBM Cloud Pak for Data is an end-to-end Data and AI platform that reduces complexity, increases scalability, accelerates time to value and maximizes ROI with seamless procedures to extend to multiple clouds. While Cloud Pak for Data and can run on any public or private cloud, it is also modular and composable allowing enterprises to embrace just the capabilities that they need on-premises. So, it is truly a hybrid multi-cloud platform.

Recently, IBM announced enhancements to IBM Cloud Pak for Data (Version 3.5). These enhancements can be broadly grouped into 2 key themes:  Cost Reduction and Innovation to drive digital transformation. Customers can drive down costs through automation, consolidated management and an integrated platform. On the innovation front, Accelerated AI, Federated Learning, improved governance & security and an expanded ecosystem are the key focus areas. In this blog, we primarily focus on the value of Federated Learning.

Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers with local datasets, without transferring them ( Figure 1). The data stays local and it allows for executing deep learning algorithms while preserving privacy and security.   This approach is different from traditional centralized machine learning techniques where all the local datasets are uploaded to one server and deep learning ML algorithms are executed on this aggregated dataset.   

Figure 1: Comparison of Federated Learning and a Standard Approach

Federated learning enables multiple actors to build a common, robust machine learning model without sharing data, thus maintaining data privacy, data security, data access rights and access to heterogeneous data. Many industries including defense, telecommunications, IoT, healthcare, manufacturing, retail and others use federated learning and getting significant additional value from their AI/ML initiatives.

For IBM Cloud Pak for Data, this additional value can be quantified using the Cabot Partners Total Value of Framework.

High Level TVO Framework for Federated learning

TVO analysis is an ideal avenue to quantify and compare the value of Federated Learning compared to the standard approach for Machine Learning.  In the TVO analysis, the Total Value (Total Benefits – Total Costs) of IBM Cloud Pak for Data solution with Federated Learning is compared against IBM cloud Pak for Data solution without Federated Learning

The TVO framework (Figure 2) categorizes the interrelated cost/value drivers (circles) for Analytics by each quadrant:  Costs, Productivity, Revenue/Profits and Risks. Along the horizontal axis, the drivers are arranged based on whether they are primarily Technical or Business drivers. Along the vertical axis, drivers are arranged based on ease of measurability: Direct or Derived.

The cost/value drivers for Analytics are depicted as circles whose size is proportional to the potential impact on a client’s Total Value (Benefits – Cost) of Ownership or TVO as follows:

1. Total Costs of Ownership (TCO): Typical costs include: one-time acquisition costs for the hardware and deployment, and annual costs for software, maintenance and operations. For the case without Federated Learning, the costs associated with data transfer to a central repository need to be considered. 

Figure 2: TVO Framework for Federated Learning with Cost/Value Drivers

• Improved Productivity: The TVO model quantifies the value of productivity gains of data scientists, data engineers, applications developers and the organization. It should also consider the value associated with the availability of additional heterogeneous data due to Federated Learning. Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud and the value associated with this innovation need to be considered for applicable cases.   
• Revenue/Profits: Benefit of Federated Learning is access to a large pool of data, resulting in increased machine learning performance, while respecting data ownership and privacy.  Faster time to value with better performance results in greater innovation and better decision-making capabilities which spur growth, revenues and improve profits. 
• Risk Mitigation: Federated Learning enables multiple actors to build a common, robust machine learning model without sharing data, thus allowing users to address critical issues such as data privacy, data security, data access rights which also allows for improved governance and compliance.  

The above Framework is a simplified pictorial look of TVO analysis. In a rigorous TVO analysis, which is a major offering of Cabot Partners, the elements of the framework are quantified and expressed in easily understandable business terms. In addition, the analysis can be expanded include other innovation features.   

Conclusions

IBM, recently announced enhancements to IBM Cloud Pak for Data (version 3.5). The enhancements focus primarily on cost reduction and Innovation to drive digital transformation. A major element of innovation is Federated Learning. As detailed above, Federated Learning amplifies the value of IBM Cloud Pak for Data through:

• Lower costs – no costs associated with data migration to a central database location   
• Availability of heterogeneous data improves the quality of ML models
• Access to larger pool of data resulting in increased ML performance
• Improved security
• Multiple actors to build a common robust ML model without sharing data, thus allowing to address critical issues such as data privacy and data access rights

[1] https://futureiot.tech/gartner-ai-adoption-growing-despite-skills-shortage/

[2] Ritu Jyoti, “Accelerate and Operationalize AI Deployments Using AI – Optimized Infrastructure”, IDC Technology Spotlight, June 2018  

[3] RightScale® STATE OF THE CLOUD REPORT 2019 from Flexera™

Published by: Ravi Shankar

IBM Storage Simplified for Multi-cloud and AI

A profound digital transformation is underway as High-Performance Computing (HPC) and Analytics converge to Artificial Intelligence/ Machine Learning/Deep Learning (AI/ML/DL). Across every industry, this is accelerating innovation and improving a company’s competitive position, and the quality and effectiveness of its products/services, operations and customer engagement. Consequently, with 2018 revenues of $28.1 billion, the relatively new AI market is rapidly growing at 35.6% annually^[1].

As the volume, velocity and variety of data continue to explode, spending on storage systems and software just for AI initiatives is already almost $5 billion a year and expected to grow rapidly.[2] In addition, many companies are beginning to use hybrid cloud and multi-cloud computing models to knit together services to reach higher levels of productivity and scale. Today, large organizations leverage almost five clouds on average. About 84% of organizations have a strategy to use multiple clouds[3] and 56% plan to increase the use of containers[4]

What’s needed to handle the data explosion challenges are simple, high-performance and affordable storage solutions that work on hybrid multi-cloud environments (Figure 1).

Figure 1: Data Challenges, Storage Requirements and Solutions for HPC, Analytics and AI

Key Storage Requirements

Scalable and affordable: These two attributes don’t always co-exist in enterprise storage. Historically, highly scalable systems have been more expensive on a cost/capacity basis. However, newer architectures allow computing and storage to be integrated more pervasively and cost-effectively throughout the AI workflow.

Intelligent software: This helps with the cumbersome curatorial, data cleansing tasks, and help run and monitor compute and data-intensive workloads efficiently and reliably from the edge to the core to multiple clouds. It also greatly improves the productivity of highly skilled Data Scientists, Data Engineers, Data Architects, Data Stewards and others throughout the AI workflow.

Data integration/gravity: This provides the flexibility to simplify and optimize complex data flows for performance even with data stored in multiple geographic locations and environments. Wherever possible, moving the algorithms to where the data resides can accelerate the AI workflow and eliminate expensive data movement costs especially when reusing the same data iteratively.

Storage Solutions Attributes

Parallel: As clients add more storage capacity (including Network Attached Storage – NAS), they are realizing that the operating costs (including downtime and productivity loss) of integrating, managing, securing and analyzing exploding data volumes are escalating. To reduce these costs, many clients are using high performance scalable storage with parallel file systems which can store data across multiple networked servers. These systems facilitate high-performance access through concurrent, coordinated input/output operations between clients and storage nodes across multiple sites/clouds.

Hybrid: Different data types and stages in an AI workflow have varying performance requirements. The right mix of storage systems and software is needed to meet the simultaneous needs for scalability, performance and affordability, on premises and on the cloud. A hybrid storage architecture combines file and object storage to achieve an optimal balance between performance, archiving, and data governance and protection requirements throughout the workflow.

Software-defined: It is hard to support and unify many siloed storage architectures and optimize data placement to ensure the AI workflow runs smoothly with the best performance from ingest to insights. With no dependencies in the underlying hardware, Software-defined Storage (SDS) provides a single administrative interface and a policy-based approach to aggregate and manage storage resources with data protection and scale out the system across servers. It also provides data-aware intelligence to dynamically adapt to real-time needs and orchestrate IT resources to meet critical service level agreements (SLAs) in parallel, virtual and hybrid multi-cloud environments. SDS is typically platform agnostic and supports the widest range of hardware, AI frameworks, and APIs.

Integrated: A lot of AI innovation is occurring in the cloud. So, regardless of where the data resides, on-premises storage systems with cloud integration will provide the greatest flexibility to leverage cloud-native tools. Since over 80% of clients are expected to use at least two or more public clouds[5], there will be a need for smooth and integrated data flow to/from multisite/multi-cloud environments. This requires more intelligent storage software for metadata management and integrating physically distributed, globally addressable storage systems.

IBM Spectrum Storage provides these attributes and accelerates the journey to AI from ingest to insights.

IBM Spectrum Storage and Announcements – October 27, 2020

IBM Spectrum Storage is a comprehensive SDS portfolio that helps to affordably manage and integrate all types of data in a hybrid, on-premises, and/or multi-cloud environment with parallel features that increase performance and business agility. Already proven in HPC, IBM Spectrum Storage Software comes with licensing options that provide unique differentiation and value at every stage of the AI workflow from ingest to insights

On October 27, 2020, IBM announced new capabilities and enhancements to its storage and modern data protection solutions that are designed to:

• Enrich protection for containers, and expand cloud options for modern data protection, disaster recovery, and data retention
• Expand support for container-native data access on Red Hat OpenShift
• Increase container app flexibility with object storage.

These enhancements are primarily designed to support the rapidly expanding container and Kubernetes ecosystem, including RedHat OpenShift, and to accelerate clients’ journeys to hybrid cloud. This announcement further extends an enterprise’s capabilities to fully adopt containers, Kubernetes, and Red Hat OpenShift as standards across physical, virtual and cloud platforms.

IBM announced the following new capabilities designed to advance its storage for containers offerings:

• The IBM Storage Suite for Cloud Paks is designed to expand support for container-native data access on OpenShift. This suite aims to provide more flexibility for continuous integration and continuous delivery (CI/CD) teams who often need file, object, and block as software-defined storage. This is an enhancement with new Spectrum Scale capabilities.

• Scheduled to be released in 4Q 2020, IBM Spectrum Scale,a leading filesystem for HPC and AI, adds a fully containerized client and run-time operators to provide access to an IBM Spectrum Scale data lake, which could be IBM Elastic Storage systems, or an SDS deployment. In addition, IBM Cloud Object Storage adds support for the open source s3fs file to object storage interface bundled with Red Hat OpenShift

• For clients who are evaluating container support in their existing infrastructure, IBM FlashSystem provides low latency, high performance, and high-availability storage for physical, virtual, or container workloads with broad CSI support. The latest release in 4Q 2020 includes updated Ansible scripts for rapid deployment, enhanced support for storage-class memory, and improvements in snapshot and data efficiency

• IBM Storage has outlined plans for adding integrated storage management in a fully container-native software-defined solution. This solution will be managed by the Kubernetes administrator, and is designed to provide the performance and capacity scalability demanded by AI and ML workloads in a Red Hat OpenShift environment.

• IBM intends to enhance IBM Spectrum Protect Plus, to protect Red Hat OpenShift environments in 4Q 2020. Enhancements include ease of deployment with the ability to deploy IBM Spectrum Protect Plus server as a container using a certified Red Hat OpenShift operator, the ability to protect metadata which provides the ability to recover applications, namespaces, and clusters to a different location, and expanded container-native and container-ready storage support. IBM is also announced the availability of a beta of IBM Spectrum Protect Plus on Microsoft Azure Marketplace

• IBM announced expanded hybrid cloud support for IBM Spectrum Protect, new Gen7 b-type SAN switches and directorswith new IBM SANnav management software, both available in 4Q 2020, and plans for products based on new LTO 9 tape technology designed to enhance cyber resilience, expected in 1H 2021.

Conclusions

As HPC and Analytics grow and converge, clients can continue to leverage these new IBM storage capabilities to overcome the many challenges with deploying and scaling AI across their enterprise. These simple storage solutions – on-premises or on hybrid multi-clouds can accelerate their AI journey from ingest to insights.

[1] https://www.idc.com/getdoc.jsp?containerId=US45334719

[2] http://www.ibm.com/downloads/cas/DRRDZBL2

[3] RightScale® STATE OF THE CLOUD REPORT 2019 from Flexera™

[4]https://www.redhat.com/cms/managed-files/rh-enterprise-open-source-report-detail-f21756-202002-en.pdf

[5] https://www.gartner.com/smarterwithgartner/why-organizations-choose-a-multicloud-strategy/

Published by: Ravi Shankar

Cloudera Introduces Analytic Experiences for Cloudera Data Platform

Cloudera recently announced new enterprise data cloud services on Cloudera Data Platform (CDP): CDP Data Engineering; CDP Operational Database; and CDP Data Visualization. The new services include key capabilities to help data engineers, data analysts, and data scientists collaborate across the entire analytics workflow and work smarter and faster. CDP enterprise data cloud services are purpose-built to enable data specialists to navigate the exponential data growth and siloed data analytics operating across multiple public and private clouds.

Data lifecycle integration enables data engineers, data analysts and data scientists to work on the same data securely and efficiently, no matter where that data may reside or where the analytics run. CDP not only helps to improve individual data specialist productivity, it also helps data teams work better together, through its unique hybrid data architecture that integrates analytic experiences across the data lifecycle and across public and private clouds. Effectively managing and securing data collection, enrichment, analysis, experimentation and analytics visualization is fundamental to navigating the data deluge. The result is data scientists and engineers can collaborate better and more rapidly deliver data-driven use cases. Following are the new enterprise cloud services announcements:

CDP Data Engineering: is a powerful Apache Spark service on Kubernetes and includes key productivity enhancing capabilities typically not available with basic data engineering services. Preparing data for analysis and production use cases across the data lifecycle is critical for transforming data into business value. CDP Data Engineering is a purpose-built data engineering service to accelerate enterprise data pipelines from collection and enrichment to insight, at scale.

CDP Operational Database: is a high-performance NoSQL database service that provides scale and performance for business-critical operational applications. It offers evolutionary schema support to leverage the power of data while preserving flexibility in application design by allowing changes to underlying data models without having to make changes to the application. In addition, it provides Auto-scaling based on the workload utilization of the cluster to optimize infrastructure utilization.

CDP Data Visualization: CDP Data Visualization simplifies the curation of rich, visual dashboards, reports and charts to provide agile analytical insight in the language of business, democratizing access to data and analytics across the organization at scale. It allows Technical teams to rapidly share analysis and machine learning models using drag and drop custom interactive applications. It provides business teams and decision makers the data insights to make trusted, well informed business decisions.

These data cloud services in combination with CDP are purpose-built for data specialists. They deliver rapid, real-time business insights with the enterprise-grade security and governance and will permit Cloudera to continue to be a leader in data science.

Published by: Ravi Shankar

Total Value of Ownership (TVO) of IBM Cloud Pak for Data

The speed and scope of the business decision-making process is accelerating because of several emerging technology trends – Cloud, Social, Mobile, the Internet of Things (IoT), Analytics and Artificial Intelligence/Machine Learning (AI/ML). To obtain faster actionable insights from this growing volume and variety of data, many organizations are deploying Analytics solutions across the entire workflow.

For strategic reasons, IT leaders are focused on moving existing workloads to the cloud or building new workloads on the cloud and integrating those with existing workloads. Quite often, the need for data security and privacy makes some organizations hesitant about migrating to the public cloud. The business model for cloud services is evolving to enable more businesses to deploy a hybrid cloud, particularly in the areas of big data and analytics solutions.

IBM Cloud Pak for Data is an integrated data science, data engineering and app building platform built on top of IBM Cloud Pak for Data – a hybrid cloud that provides all the benefits of cloud computing inside the client’s firewall and provides a migratory path should the client want to leverage public clouds. IBM Cloud Pak for Data clients can get significant value because of unique capabilities to connect their data (no matter where it is), govern it, find it, and use it for analysis. IBM Cloud Pak for Data also enables users to collaborate from a single, unified interface and their IT staff doesn’t need to deploy and connect multiple applications manually.

These IBM Cloud Pak for Data differentiators enable quicker deployments, faster time to value, lower risks of failure and higher revenues/profits. They also enhance the productivity of data scientists, data engineers, application developers and analysts; allowing clients to optimize their Total Value of Ownership (TVO), which is Total Benefits – Total Costs.

The comprehensive TVO analysis presented in a recent Cabot Partners paper compares the IBM Cloud Pak for Data solution with a corresponding In-house solution alternative for three configurations – small, medium and large. This cost-benefit analysis framework considers cost/benefit drivers in a 2 by 2 continuum: Direct vs. Derived and Technology vs. Business mapped into four quantified quadrants: Costs, Productivity, Revenues/Profits and Risks.

Compared to using an In-house solution, IBM Cloud Pak for Data can improve the three-year ROI for all three configurations. Likewise, the Payback Period (PP) for the IBM Cloud Pak for Data solution is shorter than the In-house solution; providing clients faster time to value. In fact, these ROI/PP improvements grow with configuration size; offering clients better investment protection as they progress in their Analytics and AI/ML journey and as data volumes and Analytics model complexities continue to grow.

You can access the full report here.

Published by: Ravi Shankar