(Part 1 of a 3-part series)
Whether you are a project manager, a PMO director or an executive, most of us want better information. Why? Information is the fuel for decision-making. Whether a decision is about what to have for lunch or about making a billion dollar investment, a decision-maker is reliant on information which is then used to evaluate options and ultimately choose a course of action. But just having information isn’t enough – it needs to be good information.
What information is collected, how and when we gather it and what we do with it when we have it are all factors that contribute to information quality. Individuals and organizations that are mindful of these tend to be more effective in collecting better information and managing the flow of information across the enterprise than those who take a haphazard approach. So what do the effective organizations know that the others don’t?
First and foremost, effective information managers recognize that data and information are not the same thing. Data is a set of facts while information is data that has been processed to create meaning. Data is the words, information is the story. Furthermore these managers recognize that good information cannot exist without good data. Good data does not just appear; it is collected through well-defined, well-supported processes. Processes for collecting data clearly define what is being collected and establish clear roles and responsibilities for both providing and collecting it. Attention is paid to timeliness and completeness and includes feedback to data providers so that data quality can be improved over time.
Effective data collection processes also focus on quality not quantity – they don’t collect data for the sake of data. While its nifty to be able to say you’re collecting data on 1,000 different ‘key indicators,’ trying to collect data on everything is time consuming and contributes little to the objective of having information for decision making. Indeed having too much data can feed ‘analysis paralysis’ where the amount of data cannot be synthesized into useable and relevant information. Asking for a few key data elements rather than all the data available not only reduces the time and overhead associated with data collection, it emphasizes the importance of the data that we do collect.
Consider the example of the manager who, in trying to understand how time is being spent by his staff, defines eight different categories of administrative work. Everything, from reading emails regarding benefits to attending team morale events with co-workers, appear as separate line items in the timesheet. Over time, and after thorough analysis, the manager notices: a) The aggregate amount of administrative time reported has increased, and b) The greatest increase is in the item labeled ‘timekeeping.’
But how can we be sure we’re collecting the right data? First, we need to work backwards from the decision. Not from what the decision will be, but what is the decision about. In other words, not what is the answer, but what are the questions. While we can’t predict all the questions that could arise at any time, there are certain questions that are part of an individual or organization’s decision-making DNA. If your answer can’t be supported with facts, you need to look at your data.
In the timekeeping example above, the real question was not ‘how much time is spent onteam morale events with coworkers’, but rather ‘how much time is available for production or project work (non-administrative)’. With that in mind, a single line item for miscellaneous administrative effort would have provided plenty of data to determine the burden associated with administrative work.
Collecting data is the beginning of the story for creating better information but it is by no means the whole story. In my next post, we’ll look at how we create information from the data and how that information is communicated all support better decision-making.