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DATA CLEANSING FOR INTEGRATED PM

Once you have gathered your data impacting projects from around the enterprise, now what? Well, the data needs to be cleaned. This is the process of detecting and correcting (or removing) corrupt or inaccurate data points from a dataset coming from your data collecting activities, and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.

The objective of data cleansing, is to produce a data set consistent with other similar data sets in the Integrated PM System. The clean data definition is defined by the workflow consuming the data. Data enhancement may be required due to source data incompleteness, the data is made more complete using Knowledge Curation processes.

Leverage the artificial intelligence capabilities of Cortana™ and Watson™ to complete Text analysis, Linguistic analysis, Concept analysis, Key Word Analysis, Semantic Role analysis, and Taxonomy analysis to assist in the cleaning and categorization processes. This is used to package the data into standard data sets regardless of the data source. This is critical for combining information flows with workflows in the Integrated PM System.

Before the data cleaning process can be completed, there is a mapping of source data points to standard business process inputs. This mapping requires dimension analysis to align apples to apples, dates to dates, hours to hours, and converting yen into dollars, or visa versa.

Package the data into standard data sets regardless of the data source. This is critical for combining information flows with workflows in the Integrated PM System.


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Most data cleansing tools have limitations in usability:

  • Project costs: costs typically in the hundreds of thousands of dollars
  • Time: mastering large-scale data-cleansing software is time-consuming
  • Security: cross-validation requires sharing information, giving an application access across systems, including sensitive legacy systems

But now, you can have the power of large organization data cleansing, but at a fraction of the cost and complexity.