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Business leaders today recognize data as a highly valuable resource. From providing competitive insights to balancing the books, data is the lifeblood pumping through any company. However, data’s value varies due to its potential impact. That impact typically depends upon the type of analytics used on it.

For a few years now, real-time and predictive analytics have been creating value on a range of use cases not available with traditional ad hoc reporting or operational data analysis — things like:

• Location-based advertising
• Consumer sentiment analysis
• Market prediction for fraud and risk
• Patient sensors and medical image interpretation

These and many other AI examples are appearing across industries. But let’s set aside for a moment all the ways we can use data and the variances in impact, and remember one universal rule: more data usage improves business outcomes. When you leverage multiple sources of data, you can take advantage of competitively differentiating technologies like AI and cognitive computing.

Putting your data to work

If data is the organization’s lifeblood, what’s the best way to cater to it so as to derive optimal value? The IBM DataFirst Method provides a practical approach based upon the premise that more data usage improves outcomes. The DataFirst Method says, just start somewhere and build repeatable use cases to expand upon the value. Rather than worry about what data analytics projects will provide the maximum business opportunity, you try to find an initial one that will have high impact, realizing you have lots of high-impact use cases from which to choose.

Consider the following recommendations when thinking about where your organization can get started:

• How do you collect data, and could this be done more efficiently or for greater impact?
• Are there mundane business processes you can optimize or revolutionize?
• How can you anticipate your customers’ wants and serve that to them on a platter?

We quickly realize that if data is a precious resource, AI is at the heart of rendering data’s value. It’s no surprise then that according to a Narrative Science report quoted in TechRepublic, 61 percent of businesses said they implemented AI in 2017, with the most widely used solutions being predictive analytics, machine learning and natural language processing.

Understandably, since data is the lifeblood of AI, many organizations that begin their AI journey quickly face data and data usage related challenges such as storage, resilience, integration and other infrastructure issues.

This is where we can help.

A reference architecture for AI

To help address common infrastructure challenges on the AI adoption journey, this year IBM introduced a reference architecture for AI, along with IBM PowerAI Enterprise, a new platform with a package of deep learning frameworks optimized to run on the IBM Power Systems architecture.

The reference architecture for on-premises AI deployments is designed to help organizations jump-start AI projects and accelerate the process of moving from AI experiments into production. The IBM Reference Architecture for AI Infrastructure is an integrated solution built on IBM Power Systems servers, with IBM Spectrum Scale and Elastic Storage Servers, and PowerAI and Spectrum Conductor Deep Learning Impact software to simplify data science and data storage functions while rapidly analyzing data. The reference architecture expedites building a high-impact AI-based data-first enterprise.

Supplementing the IBM Reference Architecture for AI Infrastructure, IBM Systems Lab Services’ experienced consultants have helped further expedite numerous clients’ AI adoption and successful AI project completion by helping them select initial use cases, identify and address infrastructure gaps and build an optimal infrastructure for quickly deploying AI and deep learning workloads.

Contact IBM Systems Lab Services today if you’re looking for support on an AI project in your business.

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