The concepts of standards, collaboration and reuse are well understood in organizations within most companies. Most teams are well educated about system architecture, development methods, requirements gathering, testing and even code reusability. Most business teams can read the concepts of business requirements, business process definition and outcome measurement. Unfortunately, the assumption of applying these concepts to data to support improvement, accuracy, sharing, and reuse is still foreign to most organizations.
The idea behind developing a data strategy is to ensure that all data resources are posted in such a way that they can be used, shared and transferred easily and efficiently. Data is no longer a by-product of business processing - it is an important asset that enables processing and decision making. The data strategy helps by ensuring that the data is managed and used like an asset. It provides a common set of goals and objectives A data strategy establishes common methods, practices and procedures for managing, manipulating and performing share data across the enterprise in a reproducible manner.
While most companies have several data management initiatives underway (Metadata, master data management, data governance, data migration, modernization, data integration, data quality, etc.), most efforts are focused on point solutions. Address specific project or organizational needs. A data strategy establishes a road map to align these activities in each data management discipline in this way. They complement and build each other to provide greater benefits.
Historically, IT organizations have defined a data strategy with a focus on storage. They created comprehensive plans to shape and manage their platforms and have developed sophisticated methods to deal with data retention. While this is certainly important, it does indeed address the strategic aspects of content storage - there are no plans for how to improve all methods of receiving, storing, managing, sharing and using data. The data strategy must contain the data storage address, but it must also be taken into consideration data is identified, accessed, shared, understood and used. To be successful, a data strategy involves each of the various disciplines within data management. It will then address all issues related to making data accessible and useful so that it can support a multitude of processing and decision-making activities today. There are five main components of a data strategy that work together as building blocks to support large-scale data management in an organization: identity, store, provision, procedure and governance.
Identify data and understand its meaning regardless of structure, origin or location
Store Persist data in a structure and location that supports easy, shared access and processing
Provision Package data so it can be reused and shared, and provide rules and access guidelines for the data
Process Move and combine data residing in disparate systems, and provide a unified, consistent data view
Govern Establish, manage and communicate information policies and mechanisms for effective data usage