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Digital project management is not possible without a thorough data analysis. The amount of data can be so large that our human brain is unable to process it. Fortunately, there is the necessary project management software to help us with this. However, this software can only work properly if the necessary data are prepared and entered into the system. Many project managers do not spend enough time on this. However, in order to achieve a significant improvement in the multi-project organisation, this preparation is essential. Professor Paul Boudreau describes the importance of data for digital project management in his book “Applying artificial intelligence to project management”.<\/p>\r\n
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Clear and structured data-input <\/p>\r\n Types of data<\/strong> <\/p>\r\n Processing and structuring data<\/strong>
<\/strong>AI-based software is and remains a machine, and the results displayed after implementing AI in project management software depend on how data is entered. Unambigous\u00a0 and structured data entry is an absolute necessity. This raises several questions such as: what data do I need, what documents should I prepare for my database, which can affect my results, what can help my software to process my data efficiently.<\/p>\r\n
Every decision you make in projects \u2013 whether or not using data analysis \u2013 is based on data from the past, present and future.
Past data should be collected per project : project bottlenecks report, resources capacity versus output, output versus hours spent, budget versus hours actually spent. Registering all project-related information in a single report is a very important step in preparing your data for AI tools. This can be done in phases (every time when project information is available) or at once after the project ends.
Current data on project planning, resource availability, change of orders, policies and appointments within the organisation should be collected. Each project should be classified as a dataset that can be easily processed by a software. Storing documents in different formats sometimes makes processing difficult, so the result doesn’t always show the correct prediction. Various internal and external factors can influence the results of your multi-project management in the future and have an effect on your output. The system should take this into account to predict your results.<\/p>\r\n
Data correctness<\/strong>
When processing large amounts of data, quality is a challenge and sometimes even a problem. Known problems include : typos, capitalization usage, numbers combined with letters, incorrect characters, different forms of data fields, unfilled fields, and erroneous spelling. Although machines are smart enough to perform different functions, they cannot display the correct output based on incorrect data.<\/p>\r\n
In a multi-project organisation, it is often better to have an employee responsible for collecting, processing and structuring data so that the AI tools can easily use this information.\u00a0Make sure that the data input for all projects is done according to certain standards by, for example, working with templates. When compiling a particular type of document, the data\u00a0must be entered in such a way that the AI tool can recognize and distinguish the document of other types of documents. For example, when reporting problems, you can best create\u00a0categories of the type of problem.<\/p>\r\n
Prediction for the future<\/strong>
Algorithms \u2013 regardless of the complexity and scope of the project and the number of projects \u2013 will analyse data to find patterns, create templates, and create a model that can be\u00a0used in the future to avoid errors and achieve better results. At present, however, instruments are not yet able to to identify the link between a risk and a particular task if there is no\u00a0manually defined connection between them.<\/p>\r\n
Reference<\/strong>
Boudreau, Paul (2019). Applying Artificial Intelligence to Project Management. Amazon Fulfillment.<\/p>\r\n\r\n