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Process data management & continuous improvement

Data handling automation in Excel to facilitate routine analysis and continuous data improvement.
by

datasetnotebook .com

on 26 May 2015

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Transcript of Process data management & continuous improvement

Process data integration and management in Excel
Dataset notebook makes routine analysis possible by bringing structure to your data and automating access to historical data.
So routine analysis is often skipped preventing better process understanding and continuous improvement.
As a result, up to 80% of the time of analysis projects can be lost to sourcing, preparing and cleaning the data*.
www.datasetnotebook.com
1. Your data is organized in a unified
dataset structure
considering data validation, -visualization, -analysis.
Data is often scattered over multiple locations and not properly structured and cleaned for analysis.
* http://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html
2. All variables of a process are defined in a dataset enabling automation of data import and merging.
3. Process datasets are gathered in a shareable data store for fast access and concurrency control. Excel data in the store is cleaned and ready for analysis.
Collect data directly in Excel.
Collect off-line data under controlled conditions.
Access historical data.
Easily combine process (batch) datasets into one data matrix for analysis.
Perform routine analysis.
With historical data at your fingertips you are empowered to perform data analysis frequently to ...
Employ a TQM approach to process data management.
Characterize your process, reconsider the relevance of collected information, and identify new important information.
Continuously improve your process data.
Add new important variables or remove irrelevant data. Datasets within a process will remain compatible.
Automate import of data from external sources.
(data validation on entry, user access control)
(filter data on import, no need for scripting)
gain new insights.
investigate into data quality and the relevance of collected information.
Full transcript