| |DECEMBER 20249CIOReviewhelp customers manage their properties and hedge risks in extreme events. We are living in a more connected world than ever.Challenges to BusinessesHowever, more data means more operational costs to businesses. It is a big challenge for businesses to build a Big Data management platform to support the open-ended search needs with efficiency, effectiveness, scalability, resilience, and robustness. Today, many online agencies and IoT suppliers are facing pressures from markets to provide real-time services and sub-second search capabilities through their billions of transactions, which is beyond the traditional data warehousing capabilities. CIOs and CTOs are increasingly relying on Data Science to analyze business operations, identify key data flows, and generate effective data management strategies before investing in data platform or infrastructure. Depends on the business needs, the companies can apply Fog Computing and Edge Computing to manage decentralized computing, or apply Virtual Data Mesh or Fabric strategies to allow flexible data accessibility to distributed data systems, or apply centralized data platform to enhance data efficiency and ease of usability. No matter which strategy, the purpose of an effective data platform is to provide reliable information and remove ambiguity and waste caused by duplicated data feeds or reports. Data Operations ManagementThere are many strategies to optimize the data operational costs. We can optimize the data storage with hot data on fast storage, warm data on slower storage, and cold data on the "deep but cheap" storage tier. Various storage technologies are developed to support business needs including NAS, HDD, SSD, memory fusion, data lake, and cloud resources. ML can analyze the patterns of data usage, which guides the caching strategies and retention policies to optimize the tradeoffs between storage costs and application performance. It also guides data transmission or data streaming strategies. Today the mainstream voices appeal businesses to move operations into cloud or hybrid cloud instead of maintaining multiple data centers on premises. Cloud computing technologies make auto-scaled and auto-managed computing capacity become possible. This enables businesses to focus on marketing strategies and enhance products and services for customers less worries about maintaining IT infrastructure. However, it also requires their technical teams to master cloud technologies and manage data environments properly and efficiently. Data Availability and TransparencyInformation asymmetry and isolation is another cause of operational inefficiency and customer failure. Consumers are often frustrated with long contracts, having no idea of the limitation of a service when they sign contracts with salespeople, and later find what they expected cannot be satisfied. Or they have to wait for next steps but have no clear expectation when issues can be resolved. Agents cannot give clear instructions to customers either because they are not offered enough training, or their internal processes are not transparent. An effective data management strategy raises operational efficiency and reliability by establishing one source of truth and providing access to that data source throughout the organization. Blockchain technologies enable businesses to build consistent and immutable transaction flows to exclude possible manipulations and duplicated efforts, which are typical of traditional business operations. IoT offers retailers better and real-time insights into their supply chain and store operations, allowing them to run more effectively. ML have been applied to automate operations. It also enhances process mining. By proactively detecting operational anomalies and identifying causalities, the issues can be addressed in advance. An effective data management strategy raises operational efficiency and reliability by establishing one source of truth and providing access to that data source throughout the organization
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