The potential list of use cases that a full-featured data platform can address is nearly limitless. Looking at the intersection of industry type, data sources, business function, and value alone is too large a list to document. The following list gives some sense of the common use cases that Dell Technologies sees most often:

• Customer 360 analytics
• Retail inventory and sales analysis
• Manufacturing operational analysis
• eCommerce fraud prevention
• Network security intelligence
• Data warehouse consolidation
• Discount pricing optimization
• Financial services
• Insurance industry predictive analytics
• Recommendation engines
• Social media analysis and engagement

A good business practice is to maintain an active list of potential use cases where the availability of a data platform can enhance development. Assess the list so that you do not tackle too many use cases that are high-priority and high-investment too early.

This document describes two of the use cases in more detail: financial services and manufacturing.

Financial services encompass a wide range of business models, including:

• Consumer and commercial banking
• Individual wealth management
• Primary or secondary capital markets

The importance of relationship management is shared across all these businesses and therefore has been a key focus area for analysis. Virtually all midsized and larger financial services organizations have one or more data platforms. The intense pressure to compete with other players makes finding, securing, keeping, and nurturing relationships with customers a priority that drives profit. There is also a requirement to manage investment risk and assure compliance with all regulatory requirements, which often involve multiple, overlapping jurisdictions.

While personal relationships still matter, data driven modeling and reporting across multiple channels including mobile, online, phone, or branch agent are a must-have for these organizations. Organizations that build trust by arming the organization with data-driven information increase the confidence of their customers, along with wallet share and lifetime value. To achieve that on a global scale, you must leverage big data and predictive analytics using a proven and modern hybrid data platform.

Industry 4.0 is an emerging term that means smart manufacturing. Advanced technologies are combined with traditional manufacturing and industrial practices to improve operational efficiency across the board. The innovations and documented successes of Industry 4.0 initiatives are encouraging more manufacturing companies to adopt Industrial IoT (IIoT) concepts and technology. Such adoptions transform product development, supply chains, and manufacturing operations.

Many recent case studies show that connecting analysis of smart products, design engineering, factory floor operations, and customer experience enable faster time-to-market, improved product quality, and scaling production output while reducing waste and operating costs. Connected products are a key initiative of Industry 4.0. The connectivity these products provide drives customer satisfaction and revenue while reshaping the relationship between people and products.

Achieving these benefits requires the abilities to ingest, process, and analyze sometimes massive volumes of IoT data. This data processing scale enables manufacturers the access to near real-time customer feedback to identify product quality issues. Another growing area of Industry 4.0 is intelligent supply chain management. Disruptions and delays in a critical supply chain will ripple through an organization from sales to operations.

Many manufacturers are using near real-time data, analytics, and machine learning to ensure that supply chains are functioning well while risk is managed end-to-end. Combined with a modern data platform that supports advanced analytics, including machine learning capabilities, required investments to take advantage of these latest innovations in manufacturing include:

• Special purpose sensors
• GPS
• RFID
• Production stream data