Skip to main content
Engineering LibreTexts

1.4: Data Accuracy

  • Page ID
    93629
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    The countless copies of this data could also have discrepancies, making it impossible to know which information is accurate. Whenever a new value needs to be entered into the database, every single file with this data has to be updated to prevent this. This would lead to tedious work that wasn’t 100% accurate in the end. For example, a company could have stored customer data, including name, address, and city. There could be a request in which the record of a customer who lives in a specific city is needed. In order to achieve this, a new program would need to be written and executed, and the file containing the customer's city had to be accessed. Every single customer who belonged to this city would need to be specifically selected and taken out into this new program in order to organize the data. This is neither convenient nor reliable. These copies also contributed to the difficulty involving the creation of new applications, as they may be unable to find the appropriate data. This also ensured atomicity didn’t work. Atomicity is a sequence of database processes such that either all occur, or nothing occurs. This could be used to prevent updates to a database occurring only partially; however, atomicity is unable to work unless it is able to read and write to every single file, which in this structure, is extremely difficult.

    There was also a difficulty in accessing data due to the “spaghetti code” structure of this system. If a specific set of information is needed to be organized in a new way, unless it was anticipated prior to the initially being created, it was nearly impossible to achieve this. The application needed to display the information in the requested way would not have existed. This system doesn’t allow data to be retrieved in a convenient manner, leading to different systems created down the line.

    Integrity problems were also created due to the data values in a database needing to satisfy certain types of consistency constraints. Since most of the code involving these files is in different languages, it is almost impossible to change them all to enforce new constraints. The file system also lacks concurrent access. In modern systems, multiple users can update the data simultaneously. This is to ensure a faster response time and to improve the overall performance of the system. The involvement of multiple users may result in inconsistent data, which is normally prevented using supervision. However, in a file processing system, this supervision is lackluster due to the several applications and various languages. It all leads to the same problems in the end.


    1.4: Data Accuracy is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.

    • Was this article helpful?