What data standards should you apply to your data dictionary for each project? How does data integrity and liability play into your decisions for accessibility?
“Data is the new gold,” those are the first five words that appear on nearly every About Me section on social media. This is because data, when utilized correcty, becomes information; and information becomes valuable when paired with the right action. Thus, data is what influences how the future will unfold, how people think, and ultimately how people behave. One caveat to data is that it needs to be processed into something meaningful in order for us to extract utility from it. This sounds like an easy task, but because there is so much data being collected, stored, and processed every single second, the task of processing data into something useful is far more complex that how it sounds. Data, for the context of this article, can be best defined as the factual information that describes a thing. It be a quantitative description, like a measurement or statistic, or it can be an non-quantifible description, like the type of data along with the charactisitics of that type. As you can see, the process of making data meaningful can be quite complex. This is why data is typically organized in meaningful ways within tables constructed by rows and columns. However, due to the abundance of data, the tables used to organize the data now become complicated as they too grow in number and it type. Luckliy, data dictionaries were created to help mitigate the complexty of organzing tables that organize data.
A data dictionary is a tool that helps with explaining all of the attribute names and characteristics used for each piece of data, used within the tables of a system or collection of data. Think of data dictionaries as the metadata about the the data. The metadata of data helps describe what it is and helps in explaining how it how it can be used (Cornel et al, 2020). But before a data dictionary can be created, and even before data can be collected, organizational members need to have a shared understanding for the rules of engagement of data, also known as the data standards.
Data standards are the detailed and specific set of instructions that describe the minimum requirements for handling data within an organizaton. This is to ensure that organizational members have a shared understanding of how data is collected, processed, and used. This shared understanding helps maintain the integrity of data, while providing a system of accountability, as data standards should also define who has access to the data, along with the scope of their responsibilities (Geng, 2015).
In regards to project management, specifically in a software development project, in each phase and in different teams within a project, data will be handled differently. Therefore, as the the project manager, it’s in the project best interest to establish data standards the ensures the quality and integrity of data. Once these data standards are communicated and put in place, the project manager must enforce them. From these data standards, the data dictionary can be created relevant to the project and team. This further helps with communicating much more reliable and accurate information about the project and its progress.
References:
Cornel, C., Morris, S., Corcket, K., Blewett, G. (2020). Database principles: Foundations of design, implementation, and managment. Cengage Learning. Apple Books.
Geng, H. (2021). Data Center handbook: Plan, design, and build operations of a smart data center (2nd Edition). Hoboken, NJ. John Wiley & Sons Inc.
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