How mature is your organization’s approach to managing data? Today, all projects use a lot of data and generate a lot of data – and how that is looked after is subject to legislation, best practice, policy and ethics.
AI doesn’t make it easier either: AI engines trawl through our data while also creating new data of their own. With all the analytics on offer today, how we manage our data, interact with it and understand it is essential.
Therefore, a data management strategy on your project is important. OK, if ‘strategy’ is too formal a word, perhaps you want a data management approach, or plan. What that looks like is going to depend on your project and the kind of work you do overall. Perhaps you already have an IT team that focuses on data management. Surely you’ll have an Information Governance team or Information Security who will have something to say on the topic.
The risks of not having a joined up approach to managing data on your project are clear to me, and I’m sure you’ll recognise the ones I’ve highlighted below in your own organization.
Data leakage
Or, to put it more bluntly: poor data security makes it easy for people to take your data and use it in ways that you were not expecting.
That doesn’t just mean hackers. If your systems are not locked down internally, you could find members of staff accessing customer records when they shouldn’t have permission to see the data.
Data leakage also encompasses being able to extract the data from one system and use it in another. For example, copy/pasting from a data presentation tool and then manipulating the data in a spreadsheet. When data is at risk of ending up where it shouldn’t be, it can’t be controlled and that can leave the organisation open to financial and legal penalties.
Efficiency losses
Data makes us faster… or at least, being able to find the data we need without spending ages searching for it does.
A project data management plan could lay out where data is being stored, what it is for, how to access it, who has access, what reports are produced and when, and lots more. Hopefully that should offset some of the frustration of trying to access something that isn’t available (to you, at least) and also save time and duplication of effort.
You can schedule meetings around when data is available so the team is looking at hours-old results instead of something that was produce a month ago.
Poor decision-making
If you don’t have the data, you can’t make data-driven decisions. The risk to your project of not having the correct, reliable data, can be huge. On one project, I had to create metrics and report on them regularly, but the underlying assumptions for how those metrics were calculated changed, and then I wasn’t comparing like with like. That messes up your ability to track and report reliably, and also made it look like I didn’t have a clue what I was doing!
Work out what data the project needs to inform decision making at every step and then factor in ways to get it. The more this can be automated, the better, because then it is more likely that the calculations will be based on the same formulae and assumptions each time.
Do you have data risks on your risk log? If not, why not? Or are there more you can think of? Let us know in the comments so we can all benefit from taking a smart approach. Thanks!
