Develop A Winning Data Strategy. What are the Essential Components?

Develop A Winning Data Strategy. What are the Essential Components?

The present era is volatile, uncertain, complex, and ambiguous when it comes to data-driven businesses. Despite long-term investments in data management, the data problem that remains there is that it has been treated as one aspect of the technology project and not a corporate asset. To sustain, and survive in such an environment it is very important to have a winning data strategy where data can be managed and used effectively and efficiently. 

A successful data strategy ensures that an organization can use, share and move data to perform effectively over time. We have seen a clear shift in the analytics paradigms when enterprises shifted to data companies globally. 

There are few essential components of developing a winning data strategy. 


Identifying and understanding the data is vital for creating a data strategy, regardless of the structure, origin, and location. One of the most basic constructs for the best use and sharing of the data in an organization is finding the means to identify and represent the content. Whether it’s structured or unstructured, processing and manipulating the data is not easy unless the data has not given a definite name, format, and value representation. 


The second essential component in data strategy is to store the data in a structure and location that supports easy, shared access and processing. With time organizations evolve and data assets grow, many face issues regarding the size and distributed nature of the data landscape. The purpose is to store data in one place and give access to people. The best data strategy ensures that there are no multiple copies of data and it is available for future access. 


Package data for reuse and distribution while providing guidelines for access. Normally the data is stored and organized for the convenience of the application, but the problem is that the application is not designed to share data. The rules required to decode data are rarely documented. Hence, now the companies manage dozens of systems that rely on data from multiple sources via IT teams to support individual business processes. Packaging, sharing, and democratizing the data has become necessary to support businesses’ effective processes. 


Data generated from applications is a valuable source of knowledge, but still, it is important to prepare, transform it before using it for business use and analytics. Without IT involvements, data users now need self-service tools to process data easily deploy models adhering to data standards and governance.


Establishing, managing, and communicating information policies and mechanisms is vital for effective data use. A governance process ensures all data constituents understand and respect the data-sharing rules. Good data governance enables easier access, use, and sharing of data. It establishes the trust in data you use for analytics and decision-making. 

The strength of the data strategy components is that they help you identify focused and tangible goals. The purpose of components is not to identify every potential activity within a data strategy but the components offer visibility into the different disciplines that contribute to a data strategy. 


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