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.