Analyse data quality and completeness

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Having collected all available IT room discovery data and information this activity is to ensure that such data is consumable, useful and complete.

The process of data quality analysis

Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses or disprove theories.

There are several phases that can be distinguished, described below. The phases are iterative, in that feedback from later phases may result in additional work in earlier phases.

Data requirements

The data necessary as inputs to the IT room analysis is specified based upon the requirements of those directing the analysis.

Data collection

Data is collected from a variety of sources (e.g. SME’s, IT room data repository, Configuration Management Database). The requirements may be communicated by analysts to custodians of the data. The data will also be collected from scan tooling in the environment (e.g. Microsoft System Center Configuration Manager, end-point clients, anti-virus tooling, network diagrams). It will also be obtained through interviews, downloads from online sources, or reading documentation.

Data processing

Data initially obtained must be processed or organized for analysis. For instance, it involves placing data into rows and columns in a table format (i.e., structured data) for further analysis, such as within a spreadsheet or statistical software.

Data cleaning

Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that data is entered and stored. Data cleaning is the process of preventing and correcting errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques.

Exploratory data analysis

Once the data is cleaned, it can be analyzed. SME’s may apply a variety of techniques referred to as exploratory data analysis to begin understanding the messages contained in the data. The process of exploration may result in additional data cleaning or additional requests for data, so these activities may be iterative in nature. Data visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data.


Once the data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative.

When determining how to communicate the results, the SME may consider data visualization techniques to help clearly and efficiently communicate the message to the audience. Data visualization uses information displays such as tables and charts to help communicate key messages contained in the data. Tables are helpful to a user who might lookup specific numbers, while charts (e.g. bar charts or line charts) may help explain the quantitative messages contained in the data.


  1. Conduct discovery review workshop to ensure that the breadth and depth of the data collected aligns with data model, meets reporting requirements and remains sufficient.
  2. Initiate a quality assurance process to check data currency, consistency and accuracy.
  3. Update IT room data repository.

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Activity output

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