For any Project Manager playing with various data is indispensable part of his routine job . Independent of the fact whether this data is directly related to the project or indirectly affecting the project deliverables, the project manager has to deals with it. It could appear in any form like resources required or available, money required or available, days ahead or behind the schedule, material quantities available or required, sampling data , research data and so on .. . This data handling may be required at various stages of the project. Further, it is done repetitively and progressively i.e. addition of relevant data and deletion of irrelevant data happens at various tollgates or milestones of the project. So if a Project Manager has a good understanding of various aspects of data handling he or she can easily convert the data into information and further use that to his knowledge and wisdom.
Data Sources
The first step is to identify the data sources i.e from where the data will be coming from. Data sources can be Internal or External to the organization. Internal data is the data which is generated within the organization and it generally comes through PMO (Project Management office ) which utilizes the OPA ( Organizational Process Assets ) and provides the data to the Project team. This data could be in ready to use format or may requires further processing as per the need of the current project the team is working on. External data can comes through Published Material ( e.g. research reports , literature available ) , Computerized database ( e.g. users history , market trends ) or any other kind of syndicate services ( e.g. market survey , opinion polls ). The selection of Data source is based on –
- Quantum of data required.
- Accuracy of data required.
- Budget available for data collection.
The Quantum and Accuracy level required for data can be identified by answering 4 W’s – WHO – Who is the user of data WHY – Why you need the data. WHAT - What all data needed. WHEN – By when you need the data.
Scaling the data
Once the data is received from the source it is scaled as per the need, in one of the below form using one or other techniques –
1. Range Partition – In this format data is divided into various ranges of values, with lower and upper limit specified. These ranges are usually, but not necessarily, are equally partitioned.
2. Ranking – Here the entire data is listed from top to down either in ascending or descending order and than ranked accordingly.
3. Statistical Scaling- Here the data obtained is classified in terms of statistical characteristics like Percentage, Mode , Median, Geometric Mean, Harmonic Mean, Standard deviation etc.
4. Grouping - Here the data values are grouped based on at least one common characteristic. This characteristic could be any specification which helps to correlate the data values for further analysis.
Testing the data –
Once the data is scaled it is tested for a sample of scenarios to validate and verify the desired result. This step is not always necessary and in some cases data is directly put into final use, based on the confidence level of the project manager and the source of data. Testing the data for few scenarios however improves the probability of success and failures with the rest of the data. To select test data one of the sampling techniques can be used, which itself is a detailed topic.
Using the data -
Once the data is scaled and sample tested it is available to the Project Manager or Project Team for use. Team can use one of the following approaches to use the data and are free to decide the best approach for them
1) Step by Step Approach – In this approach data is segregated for use at different stages of the project. Selective data is analyzed , used and decisions are made for that particular stage.
2) Need based Approach – As per the need, any section of data can be referred to drive the information. Entire data is referenced at any point of the project lifecycle to make decisions using cross references .
Maintaining the data –
It is the essential part of data handling process. The data is maintained through either conventional documentation or using advance software systems The advantage of using software systems is that, apart from being readily available, it helps in making decisions at other levels of the organizations too , starting from project level to middle management to top management. It is obvious to understand that until the data collected disseminates the information, of the specific purpose for which it has been collected for, the goal is not accomplished.
A good data handling process not only helps the Project Managers to take right decisions at right time but also enrich the organization by providing competitive advantage, better responses to uncertainties, better ground work for similar future projects , better communications and better SWOT analysis.
