November 19, 2020 By Dan Lynch 4 min read

AI is ripe with opportunity. It is now guiding decisions in industries such as transportation, retail, and banking — helping reduce costs while improving services. The Harvard Business Review estimates that AI will add $13 trillion to the global economy over the next decade.[1]

While AI presents great potential, it is anything but simple.  There are a number of obstacles to overcome in the process of collecting, cleaning, organizing, and implementing data and then training high-quality AI models. Many issues stem from using disparate solutions and having a disorganized process. The key to establishing effective enterprise AI is unifying data tools and processes into a singular UI experience to help your data scientists handle AI with ease and efficiency.

Here are three benefits to unifying your data and AI development experience:

1. Reduce IT, administrative and storage costs

Many organizations are hobbled by having too many disparate data tools, solutions and services. Individually, each may offer one or two capabilities for collecting, analyzing, and organizing data, but do not address the entire data and AI lifecycle. This means that time and money is wasted integrating multiple solutions and manually transferring data from siloed locations, further complicating information management. Beyond the associated expense, this constant data movement also exposes data to more security risks and potential storage costs.

Investing in a platform like IBM Cloud Pak® for Data eliminates the need for costly integration and increases efficiency because all of your tools and capabilities are pre-integrated within the platform. This not only saves the time needed to connect different solutions, it also saves money, by reducing costs of managing multiple services and underlying IT infrastructure. Moving data and AI workloads to IBM Cloud Pak for Data can save USD 1.2 – 3.4 million with a 65% – 85% reduction of infrastructure management.[2]

Data virtualization technology in IBM Cloud Pak for Data also minimizes data movement and storage costs, breaking down data silos and giving users easy access to all of their data without having to migrate it to a single repository. A recent Forrester Total Economic Impact study shows that this self-service data collection can reduce extract, transform, and load (ETL) requests by 25% – 65% and data access costs by 70%.[3]

Learn more about how IBM Cloud Pak for Data can help you save IT costs by delivering an all-in-one AI lifecycle management platform.

2. Bridge the knowledge gap and increase productivity between collaborators

Data science is highly technical and can lead to confusion and misunderstandings between various stakeholders and collaborators. This is why having one easy-to use platform is important in bridging the knowledge and understanding gap.

As a platform, IBM Cloud Pak for Data provides a singular, intuitive UI so that a wider range of roles can participate in the end-to-end data to AI lifecycle. Whether the user is a data scientist, data engineer, data steward, or a business analyst — they all utilize the same user experience while enjoying persona-based views and customized experiences for their unique job role. This means that all of the personas working on or with your data can collaborate within a shared experience — bringing together data management, data governance, data science and AI capabilities in a seamless collaborative experience.

This combined collaborative experience comes in handy when organizations use their data to create data visualization dashboards. Wunderman Thompson, for instance, used IBM Cloud Pak For Data to create a dashboard that analyzed COVID-19 risk, readiness, and recovery data to inform decisions on reopening during the pandemic. They were able to combine a team of business leaders, data scientists and engineers to work together using this same dashboard to analyze data on community risk factors, hospital readiness and economic velocity.

IBM Cloud Pak for Data offers organizations an easy way to combine the existing dashboard data with their enterprise data for more customizable insights — allowing all stakeholders to work together on one unified platform experience.

3. Reduce risk by making compliance practices across the AI lifecycle easier to implement

Laws protecting data security and privacy are becoming increasingly important, as companies continue to gather more and more personal information about their customers. In order to stay compliant with regulations, organizations need to control who has access to sensitive data and be aware of and address any platform vulnerabilities to prevent a data breach. Using disparate data tools further complicates the information security process because siloed data requires solutions to be applied individually.

Using one unified platform with built-in compliance features helps organizations manage and automate data governance throughout the AI lifecycle. For instance, IBM Cloud Pak for Data offers the ability to run data discovery processes on both structured and unstructured data sources, eliminating dark data and identifying “risky” data. Furthermore, it provides the essential controls needed to stay compliant with many common industry regulations, from data lineage and access controls to user management and integration.

For example, if a data scientist is accessing a set of data for mortgage loan applicants, sensitive information such as the applicant’s email and social security number is hidden from view. This built-in governance feature enables data scientists to access and use data for their modeling purposes while staying compliant with data privacy regulations.

Because data and users are managed through a single platform with built-in automation and compliance features, IBM Cloud Pak for Data gives users the ability to enforce governance and regulatory policies across their entire organization.

Invest in a unified data and AI platform for maximum results

While implementing AI has its share of challenges, investing in a full lifecycle platform like IBM Cloud Pak for Data streamlines how you manage, govern, and analyze data to ultimately infuse AI throughout your organization. With data virtualization, automated governance and integrated data science tools, it helps to reduce IT and administrative costs and provide a singular intuitive UI that improves collaboration between different stakeholders.

Learn more on how to save on IT costs with a fully integrated data and AI platform.

Read the Forrester Total Economic Impact study to see how IBM Cloud Pak for Data can help your organization achieve a positive ROI.

[1] Tim Fountaine, Brian McCarthy and Tamim Saleh. Building the AI-Powered Organization. Harvard Business Review, July-August 2019.

[2] Forrester Total Economic Impact™ of IBM Cloud Pak for Data. February 2020.

[3] Forrester Total Economic Impact™ of IBM Cloud Pak for Data. February 2020.

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