Data Management - Creating a Vision for Data Analytics
A data strategy is an overall, integrated holistic approach to information management and governance for an organization. It utilizes guidelines, processes, and models for gathering and managing data so that it ensures data integrity (i.e. data is accurate, clean, and usable). Data integrity is a key principle of data strategy because it prevents corruption or mistakes from taking place and it also provides managers with insights into how their data is being used and what changes may be needed to optimize the way data is being managed. Data integrity also helps managers make informed decisions about which strategies to use to achieve business goals.
A data strategy
is typically developed by an external group that is aligned with the organizational vision. This external group may be a special team hired by the company to assist in the development of a data strategy. In some cases, however, the company develops and implements its strategy based upon the guiding principles that align with the vision.
Developing a data strategy can be a time-consuming, complicated process. While many issues need to be addressed, such as business development, implementation, usage, reporting, usage, and more, managers often do not know where to start when it comes to creating an organization-wide vision of the future. The vision should include the mission and vision of the organization, the resources needed to implement its goals, the processes that will be required to implement the vision, and the procedures necessary for making data accessible and meaningful to all employees and stakeholders. All of these components must be included if managers are going to successfully carry out their responsibilities and create a data strategy that drives the organizational purpose and vision.
The creation of a data strategy requires the input of multiple departments including the executive management team, finance, human resources, and other key personnel. Once a vision is developed, the data management team can develop methods by which data will become more relevant and useful to the organization and its stakeholders. Next, the data management team should develop a methodology for managing the strategy, which may include using internal documents and information obtained through research and analysis, outsourcing decision making responsibilities, developing joint venture partnerships, and engaging the rest of the organization in the process of developing the vision and implementing the strategy. Finally, the data management team must determine the cost savings that will result from implementing its vision.
Data management is not simply a matter of creating a large database or a small collection of standardized information. At the core of information, architecture is the concept of domain-specific data models. Domain-specific data models are those used by an organization that effectively represents the diverse needs of its constituents. These data models are designed to efficiently represent each aspect of a business's data in a format that is unique to that entity. This is because no two organizations are identical, even though both may have the same business goal. The implementation of a data strategy that effectively coordinates domain-specific data models and Data Scalability
will go a long way in ensuring that a company's data structures are correctly aligned with its goals and objectives.
Without having clearly defined organizational goals, there is a risk that managers will become too lax in their monitoring and their application of analytics. On the other hand, if there is a clearly defined set of long-term and short-term goals, there is a much greater chance that these goals will be aligned with each other and with the data strategy that has been developed. Long-term goals need to be managed and goals need to be aligned with one another for the organization to achieve its long-term success. It's good to click on this site to learn more about the topic: https://en.wikipedia.org/wiki/Data_management