Business Data Alignment-همراستاییِ دادهها با اهداف سازمانی
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- 2. Business Data Alignment طاهری مهدی سید دکتر مرکزی کتابخانه رییس و علمی هیئت عضو طباطبائی عالمه دانشگاه http://www.smtaheri.ir taherismster@gmail.com
- 3. What is business data? • is data that is captured and stored by a business as a digital asset that may support strategy, decision making and day-to- day operations. • This includes source data that a business collects and data that has been processed such as calculated metrics and forecasts. • Business data can be stored in databases that are machine- readable or represented as information intended for human consumption such as a user interface, document or report. 3
- 4. The following are common types of business data. Audit Trail Customer Data Dark Data Knowledge Machine Data Market Research Master Data Metadata Metrics Product Catalog Qualitative Data Quantitative Data Reference Data Transactional Data 4
- 5. The following are common examples of business data Leads & Opportunities • Lists of potential customers. Customer • Customer details such as name and address. Transactions • Records of commercial transactions such as customer purchases. Interactions • Records of interactions with customers and other stakeholders such as investors, employees and the media. For example, records of visits to your website. Social Media • Data regarding your target markets or reputation that is collected from social media sources. 5
- 6. Business Data Repository (BDR) • A centralized storage facility, such as a proxy server or file server, where business transactions, contact information, files and other data is kept. Also called “Business Data Archive”. 6
- 7. Business data management • Business data management is an essential activity in all types of companies. The four basic steps in business data management: Data creation, data storage, data processing, and data analysis. 7
- 8. Enterprise data management • (EDM) is an organization's ability to effectively create, integrate, disseminate and manage data for all enterprise applications, processes and entities requiring timely and accurate data delivery. 8
- 9. Data management strategy • Is the process of planning or creating strategies/plans for handling the data created, stored, managed and processed by an organization. • It is an IT governance process that aims to create and implement a well-planned approach in managing an organization’s data assets. 9
- 10. The key objective behind data management strategy is to develop a business strategy that ensures that data is: • Stored, consumed and processed in a manner required by the organization • Controlled, monitored, assured and protected using data governance and security processes and policies • Stored, categorized and standardized using defined and known data classification and quality frameworks 10
- 11. Business data analysis • Aims to evaluate whether business data are aligned with an organization’s goals or not. • Business data analysis includes the activities to help managers make strategic decisions, achieve major goals and solve complex problems, by collecting, analyzing and reporting the most useful information relevant to managers' needs. Information could be about the causes of the current situation, the most likely trends to occur, and what should be done as a result. 11
- 12. Business data alignment • More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. • So how do you get the perfect fit where data strategy is driven by and underpins business strategy? 12
- 13. The main ideas are: • How to align data strategy with business motivation and drivers • Why business & data strategies often become misaligned & the impact • Defining the core building blocks of a successful data strategy • The role of business and IT • Success stories in implementing global data strategies 13
- 14. Data strategy • Data Strategy describes a “set of choices and decisions that together, chart a high-level course of action to achieve high-level goals.” This includes business plans to use information to a competitive advantage and support enterprise goals. • A Data Strategy requires an understanding of the data needs inherent in the Business Strategy: • “It’s the opportunity to take your existing product line and market it better, develop it better, use it to improve customer service, or to get a 360-degree understanding of your customer. Data Strategy is driven by your organization’s overall Business Strategy and business model. 14
- 15. OR • A data strategy is a common reference of methods, services, architectures, usage patterns and procedures for acquiring, integrating, storing, securing, managing, monitoring, analyzing, consuming and operationalizing data. • It is, in effect, a checklist for developing a roadmap toward the digital transformation journey that companies are actively pursuing as part of their modernization efforts. • This includes clarifying the target vision and practical guidance for achieving that vision, with clearly articulated success criteria and key performance indicators that can be used to evaluate and rationalize all subsequent data initiatives. 15
- 16. A Well-Developed Data Strategy has: • A strong Data Management vision; • A strong business case/reason; • “Guiding principles, values, and management perspectives.”; • Well-considered goals for the data assets under management; • Metrics and measurements of success; • Short-term and long-term program objectives; • Suitably designed and understood roles and responsibilities; 16
- 17. Businesses Develop a Data Strategy to: • Manage torrents of data that are critical to a company’s success; • Think of the future and trends and how to best manage them; • Drive innovation and establish a data culture; • Support the re-imaging of decision making in an organization – at all levels; • To develop a sustainable competitive advantage given the volume, depth and accessibility of digital data. 17
- 18. Four common drivers • Though the impetus for creating a data strategy can vary from one organization to the next, there are four common drivers: • Unification of business and IT perspectives. – In this way companies can adopt a “business- led/technology-enabled” approach for not only internal operations but also vendor and partner collaborations. 18
- 19. • Enterprise-wide alignment of vision and guidance on leveraging data as an asset; • Definition of key metrics and success criteria across the enterprise: – The data strategy defines “success” and “quality,” thus reinforcing consistency for how initiatives are measured, evaluated and tracked across all levels of interacting organizations; 19
- 20. • Reduction of technology debt. A data strategy takes the current state of the enterprise data environments and operations into account and provides guidance for applying innovation with minimal disruption to ongoing business operations. 20
- 21. Organizations should include eight components in their data strategy: 1. Semantics 2. Goals/vision and rationalization 3. Strategic principles 4. Current-state documentation 5. Governance model 6. Data management guidance 7. Reference architecture 8. Sample and starter solution library 21
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