No matter the location, our customers across the globe face similar challenges on their journey to AI. Their goals are shared—to determine the time and cost to deploy AI, and discover common industry use cases that can apply to them.

Most clients don’t have the massive compute power that AI solutions require, and the cost and complexity of integrating AI within legacy systems long-term is daunting to many. Added to these issues is the fast and ever-growing amount of data used by AI that needs to be collected, organized, and analyzed as well as the challenge of infusing AI throughout the business.

Solution integrators need to be aware of these challenges and be prepared to help clients succeed. As an IBM Business Partner, Tech Mahindra is approaching the journey to AI with our clients as a full cycle process—from collecting data to operationalizing AI capabilities throughout the business—with design thinking at the core.

Our clients who are investing in AI are demanding a flexible and agile enterprise architecture strategy, definition and design that can extend to all architecture layers including their information architecture. That’s why technology solution partners and enterprise architecture advisors need to start with information architecture (IA) as a part of the roadmap. The mantra “there is no AI without IA” is critical for data intensive, data dependent businesses.

The ideal platform for our customers to sustain, grow and lead has a set of standard APIs that consumes data feeds from various sources, it can push outcomes from machine learning- or deep learning-powered models to any data storage, and can extend services to prescriptive and predictive analytics. We recommend IBM Cloud Pak for Data to our clients who are considering machine-learning based data analytics to get the best ROI out of the large volumes of data they have accumulated over the years.

Here’s why.

Independent tools or programs may fulfill many of the requirements necessary to help our clients succeed—but a single, all-in-one, data and AI platform model such as Cloud Pak for Data offers a comprehensive solution, using an available and optimized hardware footprint. Disparate tools generally do not provide for all features and do not fulfill all tasks in a model life cycle management whereas in a single platform. All the required programs are in-built and available out-of-box.

Cloud Pak for Data offers a single platform approach. This means the data scientist is independent of the CDO role and the hand-off or lineage of data between the data curator, data scientist, and data engineer is like a well-oiled machine: maintained with a clear audit trail. A data virtualization environment available in a single platform deployment ETL – extract, transform, load – is a separate building block when different tools or programs are used, which can help you eliminate data silos.

Another advantage? Data governance capabilities—a feature almost all independent programs lack. This is also where Cloud Pak for Data shines. With data governance being a strategic factor for Enterprises, this stand-out feature of immense value is not available in individual tools used for analytics.

Investment in a Cloud Pak for Data implementation is a key to success in a data driven economy

Cloud Pak for Data as a single platform, brings powerful insights out of data spread across the enterprise, helps set the direction for business, and leads the competition in their domain.

The unique design of data virtualization principles and the choice of APIs available for integration out of the box, the hardware and OS independence on its deployment and above all, the flexibility to move projects between different types of cloud environments (multicloud approach) through a simple mechanism makes it the best choice for data estate management and data governance.

We’ve already seen the power of Cloud Pak for Data in action with one of our clients, a department directly reporting to the head of the government office in an Asian country. This department is now using analytics to make policies and roll out social welfare and citizen benefit schemes for the country’s entire population.

There’s a huge volume of citizen data that is available across large warehouses and data lakes in both structured and unstructured formats. This data is brought into the virtualized data platform of Cloud Pak for Data and analyzed in multiple dimensions.

We worked with the client to first create and train models – and then afterwards to gain an understanding of effectiveness of current public policies and welfare measures. The result? The client has been able to apply prediction and derive the outcomes to redefine polices and schemes for more effectiveness and greater benefit, which can then be expanded to a larger stakeholder base.

Using Cloud Pak for Data, our client could utilize existing large technology infrastructure investments that could scale as their workloads increase. They started with a pilot concept and then extended this service to all government agencies and finally, applied all the required control, governance and compliance elements within one integrated solution.

Business-critical capabilities such as collecting, governing, and managing data is essential for any AI solution to work, since data collection and curation typically consume 70 to 80 percent of a project’s time. The need for a unified platform comes mainly from challenges in the Collect, Organize, Analyze and Infuse phases of the AI journey.

Having a single platform which supports multicloud environments and collects the client’s data and AI capabilities into one collaborative workflow, meets a critical need for our clients.

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