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Today, the top 5 largest companies in the world (by market cap) are all based on data and not on a tangible good or service. “Data-based” companies who started 10 to 15 years ago are growing fast and quickly taking market share from more traditional rivals.
Ant Financial in China started about 15 years ago as a financial services company based totally on collecting data and on AI algorithms making decisions. Ant Financial AI algorithms make lending decisions in hours or minutes, depending on the time it takes to collect and analyze data. Ant Financial (20 years old) has a market cap of $150 Billion, half of JP Morgan Chase’s (JPMC) $300 Billion market cap (and point of note, JPMC is 200 years old). Ant Financial has 10% of the staff that JPMC has. Ant Financial has a time-to-decision and cost advantage over JPMC and other more traditional rivals. (P.S.: JPMC is working hard to catch up).
The Fortune 200 in the United States and the Global 2000 worldwide recognize that AI is the mechanism to collect data across all corners of their organizations and to use that wealth of intelligence to make better, more informed decisions faster. And they have the money and staff to do so.
Mid-sized and smaller organizations have exactly the same need for AI applications as do the larger companies, but generally do not have the resources to do so. How do these companies compete?
Enter CPP’s “Data Intelligence” Practice
CPP is helping small and mid-sized firms start their journey into the world of AI. CPP helps these companies find, define, justify, develop, and implement the “AI applications” that make sense. CPP is also helping them develop the mind-set, data awareness, and data skills to compete with the Big Boys. This is a journey, not a destination.
Data Intelligence: The Vertical Market Landscape
Financial organizations deal with massive amounts of data in their day-to-day operations. Fraud can occur at any point of a credit approval process, or during a financial instrument’s life. What data points are needed to help identify that “anomaly”? How fast can you collect the data and what patterns do you look for? How can you identify, execute and continuously re-evaluate the “collection-analysis-take-action” cycle to enact profit-building changes for your organization?
AI in the medical field has a similar solution architecture: collect, analyze and identify a range of possible root causes of a medical condition, make a “best possible” diagnosis, and then proceed to remediation. Beyond the ability to improve patient outcomes, a small increase in “efficiency” within a healthcare organization can justify the investment in the AI app both technically and from a business perspective.
Applying data intelligence and machine learning, state and local government can greatly speed up communication response times to citizens inquiring about any matter online (via intelligent chat bots), by phone or in person. In addition, it can help government authorities monitor huge amounts of data generated by public surveillance devices and social media forums to quickly identify and response to anomalies and threats to public safety.
In manufacturing, collecting data from sensors whichare at every possible point along a manufacturing process enables an AI application to develop an integrated view of the entire process. Product defect identification, equipment maintenance scheduling, and proactive supply chain management all become possible. The ability to discover and learn from patterns can help any manufacturer greatly improve the quality and efficiency of their organization.
Solutions for a data-driven business
CPP’s Data Intelligence Practice can assist businesses of all sizes and across a wide range of industries to transform their legacy warehouses and environments utilizing today’s most innovative data intelligence solutions. CPP offers solutions that tackle AI and data analytics workloads from edge to cloud, enabling your organization to:
- Uncover valuable insights that drive business
- Align business needs across the enterprise
- Automate processes
- Lower operational costs
- Reveal new revenue streams
- Increase security and compliance
- Maintain a competitive advantage