Data intelligence at the edge

With companies all over the world grappling with exponential data growth, data intelligence at the edge is a way for organizations to manage the increasing flow of data in a more efficient manner, optimize information use, reduce costs and improve their business performance. An IBM marketing cloud study indicated 90% of global data was created in the last two years, growing at 2.5 quintillion bytes of data per day. In order to keep up, companies are now deploying smart-connected sensors and assets daily, requiring machine to machine and internet of things (IoT) networks to alleviate costly inefficiencies of data management in the cloud. IoT networks must embrace centralized architecture, where processing raw data begins at the network edge. Companies have utilized data flow, the cloud, or cores or racks of equipment purchased to perform certain calculations and storing these results to bill data center operating expenses. Rather than run up costs by processing and storing data for stretches of time, companies are beginning to use artificial intelligence (AI) and machine learning (ML) to help eliminate wasteful data while streamlining relevant data meaningful to the business. AI-powered edge analytics can aid in improving response and efficiency rates, reducing operating costs and more quickly identifying raw data that hold little to no value for the business. Tune into this webinar, presented by industry thought leader, Dr. Sue Tripathi to learn: 1. The use of AI/ML at the edge, requiring increased processing power and logic to be built into edge devices. 2. Key components to protect critical infrastructure, including security algorithms, encryption keys, and scan updates. 3. Data management at the edge of the network. 4. Examples of real-time application of data intelligence at the edge with good business outcomes and timely and informed decision making.