AI needs the right information architecture

Tom Ramey, director, z Analytics at IBM.

Tom Ramey, director, z Analytics at IBM.

Analytics has become an overwhelming challenge for many organisations, and demands built-in intelligence and the right architectures, says IBM.

"We say: take the analytics to the data, don’t take the data to the analytics. Copying and extracting data allows it to lose value and context, and causes latency, which makes the data out of date," says Karmjeet Kahlon, VP, Worldwide z Hybrid Cloud, IBM.

At the IBM "Unleash the Heart of your Enterprise" executive forum in Johannesburg this week, IBM shed light on solutions designed to support next-generation analytics in the enterprise.

On the theme "there’s no AI without the right IA", Tom Ramey, director, z Analytics at IBM, said challenges facing enterprises include modernisation of the mainframe, cost, complexity and security, and crucially, leveraging analytics and machine-learning in real-time.

"50% of S&P 500 companies are being replaced every 10 years. The lesson here is you have to continue disrupting and growing your revenue. Machine-learning and artificial intelligence (AI) can be tools to support this disruption."

Ramey said leading CIOs are well aware of the potential for machine-learning and AI. "81% of CIOs believe AI to be very important or extremely important to the future of their organisations."

Analytics challenges have become overwhelmingly complex for many organisations, said Ramey. Data often traverses a complicated journey, involving multiple copies of the data moved off the database.

He said the average mainframe customer moves 1TB of data per day off the platform to somewhere else for analytics at a cost of $10 million over four years.

"Moving data for analytics increases cost, puts the data at risk due to many copies being made, and creates a latency gap, when our goal now should be to get down to real-time for analytics. We need our analytics systems to be just as resilient and available as our transaction systems nowadays."

The data gravity approach, to perform analytics where the preponderance of the data originates, or hybrid transaction and analytical processing, marrying transactions with analytics, has emerged as the way to deliver faster, simpler, more cost-effective analytics in real-time.

"We came up with the Db2 Analytics Accelerator and plugged it in to the mainframe, with the Optimizer deciding the best path to process the query, to deliver [up to] 3 000 times faster query processing that is also super simple, highly secure and saves tons of money.

"Customers are now running reports that took eight hours, in under a second. I would argue that we’ve evolved so far beyond just taking existing queries and speeding them up. Customers have figured out it’s not just about the queries you run today, it’s about all the queries you can’t run today."

The mainframe is a data-serving juggernaut, and Db2 is all about supporting enterprise-scale next-generation applications on the mainframe, he said. Db2 V12 delivers concurrent queries, up to 100 times faster, to provide deeper insights, with enhanced support for cloud and mobile workloads and a 23% lower CPU cost by providing in-memory techniques as well as continuous availability, scalability and security, states IBM.

Ramey said: "Machine-learning is the hot new ticket these days. It identifies historical patterns in data on the mainframe, identifies patterns in that data, and uses statistical algorithms to build a model based on those algorithms. We learn from that data and then we can take that model and we can provide actionable insights by using it against the new data that comes in. We use it to make predictions and recommendations.

"With machine-learning, companies can truly tap into the rich vein of data in their historical system of records. This means instead of only using analytics to run reports on data from the past, we can now predict what will happen in the future."

But there are challenges and as little as 5% of commercial data science projects make it into production, he said.

"Traditional machine-learning processes require significant human intervention, take months to get the data, prepare the data, train a model and finally deploy a model. So we came up with machine-learning for z/OS that dramatically simplifies that long process, utilising data gravity and helping the data scientists."

"Data gravity is a critical concept now," said Martin Blignaut, enterprise and mainframe software sales leader, IBM South Africa. "You need to move the analytics to the data, not the data to the analytics. It’s powerful, as it defines where we are going and how we see the management of information on this platform in future."

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