Week 3 Blog Post: Listing of Active Businesses

DataLA asserts that their objective of the content model is transparency in terms of professional accountability and removing the space between the city and its residents, the Angelenos. The dataset I chose to further examine pertains to the listing of active businesses. The list is composed of active businesses that are registered with the Office of Finance. The definition of “active” used by the dataset is registered businesses whose owners have not asked for a cease of business operations. The list is updated frequently on a monthly basis.

The content models that this dataset consists of : location account #, business name, DBA (Doing Business As) name, street address, city, zip code, location description, mailing address, mailing city, mailing zip code, NAICS (North American Industry Classification System) code, primary NAICS description, council district, location start date, location end date, and location.

The dataset informs us of all the various active businesses and asserts that it is able to detail all the active businesses within a single list. However an issue arises in understanding the list; a few columns are difficult to understand at first glance. For instance, one would not instantly know that council district 1 is the Northeast Los Angeles and Northwest Los Angeles area. An individual would have had already possessed this sort of knowledge to truly understand the column; this would bring trouble for users that are not accustomed to this sort of background knowledge.      

I believe that the dataset performs well in describing active businesses in other, various ways. One of the easier ways that the dataset provides is using addresses, cities, and zip codes. The data set also uses latitude and longitude to determine the geographic locations of the businesses, which makes it easier for an individual to find them. This extensive list of active businesses would be of great use to any individuals in the neighborhood. The search bar assists in narrowing down results and helping people find exactly what they want faster and more easily.

If I were to restart the dataset, I would keep majority of the infrastructure the same. The columns such as the geographical locations and street, city, and zip code are very useful in searching and discovering businesses. The one thing I would add onto this would be columns that focus on descriptive details like what type and what services the businesses provide. I would also cross reference the businesses to reviews of their work and their contact information (like their home websites); this would provide the general public the information they would want and need to know all in a single spreadsheet.   

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