Continuous delivery of data drives continuous intelligence

What drives continuous intelligence?

By | 3 minute read | March 7, 2019

Every organization today is trying to analyze large data sets to make predictions and optimize actions so business outcomes are better. They are striving for continuous intelligence in every business process and need real-time analytics to process current and historical data sets.

Continuous intelligence requires a thoughtful and well-architected approach. For example, the vast majority of artificial intelligence (AI) failures are due to inefficient data preparation and data organization, not the AI models themselves. Success with AI models depends on achieving success first with collecting and organizing your data, then analyzing the data to make smarter business decisions.

Continuous delivery of real-time data and continuous intelligence

Continuous Intelligence requires nothing but the real-time availability of data. Gartner predicts that by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions. Moreover, Gartner also predicts that augmented analytics that uses machine learning and AI techniques will transform how analytics content is developed, consumed and shared.

Having real-time data available for enterprise information hubs, data lakes or event processing has often been a challenge. Organizations have collected enormous amounts of data in the last decade, but they often lack robust information architecture that can take advantage of the data. They need mechanisms to bring the data into the right place for continuous intelligence or apply machine learning and AI to gain insights. Often, as organizations consume and drive insights with collected volumes of data, merging operational transaction data (such as customer, product or accounting data) with high-volume data streams (smart devices, social media, web interactions) is critical for contextual insights and improved decision making.

To gain value from analytics efforts and drive artificial intelligence at scale, enterprises must capture information with low impact to systems, deliver those changes to analytics and other systems at low latency, and analyze massive amounts of data in motion.

To build that robust information architecture for AI, organizations should start by accomplishing two things:

1. Bring the right data integration and real-time replication.

Organizations must have the right data integration and real-time replication capabilities. These can incrementally replicate changes captured with lowest operational cost in near-real time, which can facilitate streaming analytics, feeding of data lakes, and use of machine learning and AI to help businesses run more effectively.

IBM data integration and replication offerings help users capture and deliver critical dynamic data across an enterprise to expedite better decision making. Via its data integration capabilities, IBM offers enterprises the ability to consume, transform and deliver data wherever it resides.

Moreover, with IBM real-time replication, organizations can drive continuous intelligence. Making use of low-impact, log-based data capture from transactional systems, IBM data replication delivers only the changed data across the enterprise so organizations can capitalize on emerging opportunities and build a competitive advantage through more real-time analytics.

Up-to-date availability of data is often essential for the consumers of data to make the right decisions at the right time, minimizing latency. With data being delivered in the most efficient and effective way possible, organizations can deliver incremental changes with very low latency to the target systems to drive continuous intelligence.

One multinational bank, for example, is using IBM data replication to replicate changed data in near-real time from disparate source transactional systems including Db2 z/OS and Db2 iSeries into Kafka-based data hubs. Data is then consumed by cloud-based analytics applications, used for real time customer notification services. Data is transferred to a data lake to preserve a history and to enable other analytics.

2. Invest in the right AI-infused analytics solution.

With real-time data, organizations can start to analyze that data to determine what is happening in their businesses, why it’s happening and predict what happens next. Hitting the mark for fast and accurate decisions requires that analytics tools instantly turn data into relevant insights, giving the right people the right information at the right time and removing any obstacles.

Traditional business intelligence (BI) solutions have no hope of making sense of the volume, variety, veracity and velocity of data being created. Harnessing this flood of business data requires a new approach to BI, enabled by the AI features embedded in the all-new IBM Cognos Analytics.

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To learn how you can use IBM data replication to effectively bring real time data for continuous intelligence, read the IBM Data Replication for Big Data Solution Brief. Or find out more about the IBM Data integration family.

Learn more about on how you can collect data, effectively organize this data and analyze it for insights and infuse AI at scale.

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