Post by account_disabled on Mar 10, 2024 11:11:33 GMT 5
cover: What is your data strategy? Today, the ability to manage numerous data is one of the critical elements that determines the success of a company . Although specialized figures are emerging in this management, such as chief data officers (CDOs), most companies remain behind the scenes. Key data of the article: Defensive data and offensive data differ based on the business objectives to which they lead and the activities undertaken to direct the company towards achieving the objectives A good data strategy requires that the data possessed by a company's SSOT (single source of truth) are of high quality and standardized and that the MVOT (multiple versions of the truth) are carefully controlled and derived directly from the SSOT itself According to a Harvard Business Review study, less than half of organizations use data actively in strategy planning and decision-making; More than 70% of employees have access to data that is outside their scope of work, and 80% of analysts' time is dedicated to data discovery , which is far too much.
Having figures such as the CDO (chief data officer) within the company India Mobile Number Data is already a start, but it is not enough in the absence of a coherent organizational strategy that organizes, manages, governs and analyzes information. In this article we will show you a new way that will allow you to create a robust data strategy, which can be applied to multiple types of businesses and at different levels of data maturity . Data strategy: defense vs attack Single source, multiple solutions What are the benefits of a data architecture? Data strategy: the importance of having quality data Offensive data or defensive data? Success is in the middle ground New call-to-action Data strategy: defense vs attack The framework we propose addresses two key issues: helping companies clarify the primary purposes of data and guiding them towards strategic management .
Unlike other types of common approaches, in this case it is necessary to consider the data distinguishing between those that can be used by your company to implement a defense system and those that will instead allow you to implement an attack strategy. Defensive data and offensive data differ based on the business objectives to which they lead and the activities undertaken to direct the company towards achieving the objectives. Defensive data mainly concerns risk minimization : it is used for activities that concern insurance, rules and regulations to be respected (government rules on privacy), it is used to limit fraud, and create systems that stem theft. Defensive data also ensures the integrity of the data flowing within the enterprise system through the identification, standardization and authoritative management of sources.
Having figures such as the CDO (chief data officer) within the company India Mobile Number Data is already a start, but it is not enough in the absence of a coherent organizational strategy that organizes, manages, governs and analyzes information. In this article we will show you a new way that will allow you to create a robust data strategy, which can be applied to multiple types of businesses and at different levels of data maturity . Data strategy: defense vs attack Single source, multiple solutions What are the benefits of a data architecture? Data strategy: the importance of having quality data Offensive data or defensive data? Success is in the middle ground New call-to-action Data strategy: defense vs attack The framework we propose addresses two key issues: helping companies clarify the primary purposes of data and guiding them towards strategic management .
Unlike other types of common approaches, in this case it is necessary to consider the data distinguishing between those that can be used by your company to implement a defense system and those that will instead allow you to implement an attack strategy. Defensive data and offensive data differ based on the business objectives to which they lead and the activities undertaken to direct the company towards achieving the objectives. Defensive data mainly concerns risk minimization : it is used for activities that concern insurance, rules and regulations to be respected (government rules on privacy), it is used to limit fraud, and create systems that stem theft. Defensive data also ensures the integrity of the data flowing within the enterprise system through the identification, standardization and authoritative management of sources.