Working with accurate data is one of the core concepts of continuous improvement, and you must always know that you’re relying on a fresh set of information that accurately reflects the current situation. However, there is a small detail about this which tends to elude some people data is valuable shortly after being collected. In fact, depending on the kind of work your company does, some of the data you’re collecting may actually appreciate over time, becoming a more and more valuable asset in developing your workflow.
This is not uncommon in fields like sales and manufacturing, for example. It boils down to the fact that your last batch of data is aligned with everything that’s happened up until now, but it has no relation to the upcoming future developments in your company.
What to Do with Old Data
What this means is that you may suddenly run into a major event tomorrow that completely invalidates one or more recent batches of research results. So should you discard that data entirely?
Not if you’re smart about it! The right thing to do is to keep long-running backups of all your critical data, arranged in a way that allows you to easily sort through them and filter the relevant bits. So in the above example, all you’ll have to do is re-run your simulations (or whatever type of research you’re doing) taking the new developments into account.
This might sound simple and straightforward, but you’d be surprised how many companies go about it in a completely wrong way. It’s not rare to see an organization decide to throw out weeks of research and decide to repeat the process all over again, instead of using the valuable old data that they’ve gathered in order to gain a new insight.
Delving Deeper
You can also take this one step further, depending on the complexity of your operations. If you’re able to collect all your old data sets and keep them easily accessible, you may benefit from running a long-term comparison of all that data, taking each old set into consideration. In other words, every time you run a new test and gather some new information from it, you can compare that data against all your previous data sets to figure out exactly what variables are changing over time, and at what rate.
Another benefit of keeping your old data around is that you can more easily spot patterns that repeat over longer periods of time. Sometimes, a certain anomaly will only present itself every other month, or even more rarely than that. If you don’t have an easy way to access your old data, you can draw some wrong conclusions from this, and start reacting to problems that aren’t even there.
Of course, this is all easier said than done. Maintaining an adequate, intuitive access to all of your past data is something that takes planning and preparation, not to mention the time needed to actually implement it. You may also need to change some aspects of your data collection and retention systems, depending on what exactly you would want to evaluate on a continuous basis.
Continuous Improvement for Your Data Collection Systems
Once you’ve got this set up and running though, it won’t take too much effort to keep the system in a good condition and adjust it for any new variables in the future. If you’re particularly clever about this, you should even be able to use that old data to drive the development of your collection system forward. After all, you’ll have quick and easy access to the exact kind of information you need on that, such as which variables are being evaluated more often, which parts of the company are in the dark with regards to getting enough data on them, and so on.
Conclusion
Constantly evaluating your old results is one of the core aspects of any true lean company, and the sooner you start building up your experience with that, the better results you’re going to observe in the long run. Don’t forget another core value of lean leadership while you’re on this adventure, either keep making new contacts and exchanging information with other specialists, as this is the best boost you could possibly get to your creativity and productivity.
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