Three best practices for modernizing your data science

By | 3 minute read | September 23, 2020

When it comes to gaining the most value from data, whether uncovering insights or driving higher revenue, one of the first actions an enterprise should take is modernizing incumbent applications and infrastructure. Modernizing systems can often reduce operational costs, enhance governance and security, and increase efficiency. For organizations investing resources in data science, a recent Forrester report notes that infrastructure modernization can improve application management efficiency 65 – 85% and increase productivity by about 50%.

Optimizing multicloud adoption and integration are primary focus areas for modernizing data science efficiency within an organization. A Data and AI platform like IBM® Cloud Pak® for Data provides an integrated infrastructure equipped with capabilities for data management and governance, including DataOps and real-time business analytics — all built on the open foundation of Red Hat® OpenShift®. This approach helps dissolve data silos, speed time to value, and protect against vendor lock in. Forrester projects that organizations adopting IBM Cloud Pak for Data can gain data science, machine learning and AI benefits of USD 1.2 – 3.4 million, as well as container and container management efficiencies totalling USD 12.5 – 14.4 million.

Developing best practices and the infrastructure that drives data science initiatives are critical to modernizing your AI platform. Learn more about the exclusive modernization upgrade program available to users of IBM SPSS® Modeler and IBM Decision Optimization (CPLEX).

Why modernize your data science?

Let’s explore three common barriers that can diminish AI value and consider best practices to help overcome them.

1. Siloed data and AI tools impede the ability to scale AI

At many organizations, the information architecture for AI encompasses a complex maze of disparate analytical tools, databases and data storage technologies spread across multiple clouds. Complex challenges posed by integrating these systems can inhibit the ability to scale AI.

Best practice #1: To accelerate AI benefits, turn to a unified data and AI platform. Employ capabilities like data virtualization and data governance to simplify access to the right data. Build and deploy machine learning and optimization models on any cloud or on-premises.

2. Legacy architectures slow down decision intelligence implementations

Decisions with far-reaching consequences — like production planning, route optimization and workforce scheduling — are made every day. But the inability to inject decision intelligence into applications by integrating foundational technologies like predictive analytics and optimization can reduce data science returns. ESG notes that ROI from decision intelligence systems can increase as decision complexity increases.

Best practice #2: Simplify decision intelligence implementations by capitalizing on the combined power of discovery, prediction and optimization technologies within a unified environment. IBM clients have increased ROI significantly by optimizing their predictive analytics to build plans, schedules and resource allocations. For instance, a leading transportation company used optimization and predictions to save millions of dollars annually by reducing transportation mileage.

3. Vendor lock in hinders innovation

With data dispersed across multiple environments and cloud platforms, the lack of flexibility to deploy AI models in the most appropriate infrastructure environment can hinder innovation.

Best practice #3: Build multicloud support into your data and AI stack so your teams can experiment and deploy machine learning (ML) and optimization models anywhere.

Modernize your data science with Watson Studio Premium

For current users of SPSS Modeler and CPLEX, IBM offers IBM Watson® Studio Premium for Cloud Pak for Data Modernization Upgrade. This solution helps modernize data science investments at their own pace while benefiting from proven standalone workhorses like IBM SPSS Modeler and CPLEX Optimization engine. With this offer, clients can benefit from services within IBM Cloud Pak for Data like AutoAI and Watson Studio to build, run and manage production AI with trust and confidence. This unified platform can increase ROI through modeling efficiency by up to 40% by combining predictions and optimization.

To take the next step on your modernization journey, read the Modernization Upgrade white paper, or take a deeper look with the on-demand Watson Studio Premium Upgrade webinar.

To learn more about IBM Cloud Pak for Data and the comprehensive benefits of integrating data management, analytics, data science, AI and machine learning tools into a single platform, read the Forrester TEI report.

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