Overcoming data complexity challenges.

Companies today are struggling to manage and maintain vast amounts of data spanning public, private, and on-premises clouds.

70% of global respondents for IBM’s Global AI Adoption Index 2021 stated their company is pulling from over 20 different data sources to inform their AI, Business Intelligence (BI), and analytics systems. In addition, one third of respondents cited data complexity and data silos as top barriers to AI adoption.

Further compounding the complexity of these hybrid data landscapes is the fact that the lifespan of that data – the time that it is most relevant and valuable to a company – is drastically shrinking.

IBM’s Global AI Adoption Index 2021 Stats

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Companies pulling from over 20 different data sources in IBM report

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Companies exploring AI according to responders to IBM report

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Increasing data complexity and data silos top barrier of AI adoption

Data Management and Data Quality Concerns.

Almost every organisation has data sitting in many sources: applications, clouds, and systems of record. Pulling data together from multiple sources and formats is often difficult.

In fact, a Forrester report found that 37% of businesses lack confidence in connecting data from multiple sources. This same report also found that for 58% of organisations, data quality problems is the number one data challenge.


Complexity in data management is made worse when there isn’t a holistic data governance practice. If a data-driven customer experience is to be sought after, then data itself needs to be treated as a legitimate business asset – in other words, a governance practice is essential.

A NewVantage survey reports that only 39% of enterprises manage their data as a proper business asset.

A Data-Driven Strategy is Key

Greater flexibility, security, and control over data.

A data-driven strategy means successful businesses can leverage their exclusive enterprise knowledge to constantly redefine their customer experience, products and services, and ultimately transform the nature of their industry.

Businesses need an agile and resilient cloud-native solution that enables them to predict and automate outcomes with trusted data and AI.

One solution comes in the form of IBM Cloud Pak for Data.

This is a new kind of data and analytics platform that simplifies and unifies how you collect, organise, and analyse data to accelerate the value of data science and AI, then infuse it across the enterprise.

This containerised hybrid cloud platform delivers a broad range of core data microservices, with the option to add more from a growing services catalogue.

Without having to rearrange data storage systems and spend time and expense moving data around, Cloud Pak for Data helps you experience greater flexibility, security, and control over data and analytics.

Fuelled by data, AI is empowering organisations to transform and deliver value.

Deploy in the Cloud or On-Premises

IBM Cloud Pak for Data can run anywhere.

It can be co-located where your infrastructure investments are being made. This means Cloud Pak for Data can be deployed on every major cloud vendor’s platform, including Azure, AWS, Google Cloud Platform, and IBM Cloud – and on-premises where your business needs a high degree of control over your data.

Plus, on IBM Cloud, you can subscribe to Cloud Pak for Data as-a-Service (CP4DaaS) if you require a fully managed option; only paying for what is consumed.

IBM’s Cloud Pak offerings all share a common control plane, which makes administration and integration of diverse services easy.

Cloud Pak for Data is built on the foundation of Red Hat OpenShift providing the flexibility to scale across any infrastructure using the world’s leading open-source steward.

Red Hat OpenShift is a Kubernetes-based platform for the enterprise that allows for the deployment of software through a container-based model delivering greater agility, control, and portability.

The CP4D solution includes a set of pre-integrated data services that allows analysts to collect information from any repository – for instance databases, data lakes, data warehouses, data lakehouses, or anything else – but to the end user it feels like it’s all in one location.

Benefits of Cloud Pak for Data

Data analysis tools right out of the box.

Cloud Pak for Data offers a wealth of data science capabilities that cater to all skill levels, meaning no-code, low-code, and all code use cases.

Users can quickly grab data from the catalogue and instantly start working towards generating insights in a common workflow built around the “project” concept.

For additional capabilities, a large set of extended services are available that present more specialised data management and analytics capabilities.

IBM Cloud Pak for Data Benefits

  • Extended Services – ecosystem of open-source, partner, and IBM services
  • Base Data Services – integrated self-service data analytics tools
  • Cloud Pak Control Plane – administration tools, services catalogue, and the central user experience
  • Red Hat OpenShift – container-based software deployment platform that runs CP4D services on underlying infrastructure
  • Deployment flexibility – deployable on any vendor’s cloud platform or on-premises hardware; available as managed services

Interested in a Free Trial?

IBM offers a free trial of Cloud Pak for Data as a Service with access to all Lite services, and many new services from the catalogue with no commitment. See for yourself the power of the product with a small pilot project!