By Leezan Omerbell
In my last life as a foreign affair professional, I worked as a field volunteer for Un Ponte Per, a non-governmental organization under the United Nations, at the Domiz Refugee Camp in the Kurdish Province of Dohuk, Iraq. As a volunteer, I used my language skills to collect data to help manage and allocate resources. A few days after arriving, I was in one of the trailers looking over my notes when a man entered the trailer. The man was about my age and wanted to know if his grandmother could come inside and sit on one of the empty chairs while waiting to be processed. Without hesitation, I agreed. A few minutes later a young woman entered with him and sat on one of the chairs. When I questioned him about the whereabouts of his grandmother, he simply pointed to the young woman.
As it turned out, the woman was not his grandmother, but his wife. You see, I had failed to take into consideration that even though I spoke the regional languages, there can be variances in vocabulary depending on location and dialect. Although the man thought he was communicating effectively, and I thought I was receiving the information correctly, there was still a disconnect. To me the word he had used meant someone old, such as a grandmother. But in his dialect, it meant “wife.”
The Merriam-Webster dictionary defines communication as “a process by which information is exchanged.” This process is the foundation of all relationships, personal and professional. But what we often forget, is that machines too need to communicate and exchange information with one another as an integral part of modern life and business. Broken down to their simplest level, machines such as database systems communicate with one another continuously and need to so do to remain relevant.
But how do we prevent miscommunication between these machines? If humans can have such misunderstandings, like the one that took place between that young man and myself, then machines can most certainly experience miscommunication too. As a solution, data dictionaries for database systems were created to enable clear and correct exchanges of information. For your own systems, before accurate exchanges can take place, you should do your due diligence, and do some database dictionary “house cleaning.”
- Update your data dictionary: Update your data dictionary to reflect your database as it changes. Databases change…a lot. Columns and fields become irrelevant; some are taken out while new ones are added. So, before you begin exchanging data with another database, make sure your own data dictionary is up to date.
- Make your data dictionary readable: This isn’t corporate law where you must write policy in a language no one can read. The point of your data dictionary is so that others can clearly understand what your database is about. If others can’t read it or understand it, then you have failed to create a working data dictionary. Make your data dictionary simple and easily readable.
- Answer questions: This might sound like common sense, but if an individual who is working with your data dictionary has a question, answer it. And set up time to provide clarifications. Learn from these instances and update your dictionary accordingly to prevent similar questions in future.
Again, the whole purpose of your data dictionary is so your database can communicate with another system. This allows everyone involved, machines included, to get on the same page. If it fails to accomplish this, your data dictionary needs work. Small improvements to your data dictionary can yield huge benefits for your database.
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