20 Oct


A data strategy is an approach to using data wisely to guide business decisions. The main function of a data strategy is first to help business managers establish which data are crucial to a company's operations. The data strategy then defines the processes, methods, and rules which define how to manage, track, and act on business data. Finally, Data Strategies take the data and use it to support strategic decisions. Therefore, data science and data analysis are two key concepts in data strategy.



Actionable Data management involves the process of collecting and managing information in a systematic and coordinated manner. Experts believe that information technology (IT) is an increasingly important element in the success or failure of any business. IT has developed tools and technologies to simplify business processes. One example of a data strategy tool is the Enterprise Data Management (EDM), a suite of software tools that help you effectively manage your data assets. Other examples of information technology tools used as part of data strategies are application service providers.



Several principles guide data strategy. One principle is the importance of data quality. Most businesses believe that they have a clear need for certain types of data, which they can easily provide, but if they do not have data quality, they will not be able to provide it. For example, if you cannot verify a customer's identity, there is no way that you can successfully gain access to all the customers in your database.


 Therefore, data quality is very important in data management.
Another principle of data strategy relates to the types of analytics that work best with a given type of data. Generally, analytics refers to the methods used by data scientists and computer programmers to extract value from large sets of unstructured or semi-structured data. In this case, there are two types of analytics: supervised and unsupervised. The former uses expert knowledge and data mining techniques; the latter does not.



Part of an effective data strategy includes making sure that your employees understand the process that you use to measure the success of your projects. Some managers think that they should just hire a data analyst, who would then provide them with reports detailing their project's success or failure. However, in the age of big data, "one more survey" is often not enough to satisfy analysts who have spent months collecting information from many different sources. As such, you should ensure that your analysts fully grasp how to build a data strategy so that they understand what kinds of analytics to use on any given project. This includes both understandings how to determine what kinds of information and data you need, and how to collect and evaluate it to provide your company with the information it needs to improve its bottom line.



Once your team understands how to build a data strategy, it is important to maintain it. Just as an effective data management strategy requires the right data catalog in order to be able to analyze and measure the success or failure of projects, a data strategy requires the right data catalogue in order for the manager to make informed decisions about what kinds of analytical data to use and when. Without a data catalog, the manager will likely make wrong conclusions regarding which data to pursue and which ones to discard. Also, keep in mind that the data management process itself is only part of the overall process of how to build a data strategy; the catalog is only one aspect of a comprehensive data management strategy. Finally, remember that it takes time to develop an effective data management strategy. Check out this post for more details related to this article: https://en.wikipedia.org/wiki/Strategic_planning.

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