"Data" means many different things to different people. For some, it's the quantitative result of work done in laboratories, which gives it an almost tactile, physical feel. For others, it's more like "information" - in a term paper, for example, or reporting on the numbers of people who attend a concert. In the supply chain, data is used to tell the “life story” of different products and services.
These different scenarios call for vastly different management strategies. In all cases, you need to collect, organize, share and report on the associated data, but the tools you use to do so vary significantly by the type of data you’re after. The lab worker, for example, is probably not going to be using a giant database to track his results, or even to share it with supervisors, colleagues, or other academics. He may even still be using a paper notebook, which works fine for this type of application.
On the opposite extreme, we might have a large corporation collecting Environmental Health and Safety (EHS) data. This kind of project certainly requires something more robust than a paper notebook and, if you were to go with a technical solution, you would do well to use specialized programs rather than Excel questionnaires or their equivalent. Some people like to go with giant enterprise solutions like SAP, Oracle, or Salesforce – all of which have either built-in modules and/or the capacity to be customized for these purposes.
And of course in the middle are the numerous “specialized” software packages, most of which are SaaS/cloud-based and address a key segment of the markets they serve. So you have compliance-focused solutions, supply chain programs, portfolio management programs (e.g. for business incubators that need to keep track of the quarterly financials of their member companies); and many, many more. There are new companies with fresh solutions to these problems popping up on a daily basis.
So how is one to choose which solution to use? Well, the answer to that varies by the person’s or organization’s particular needs. But here is an attempt to categorize your options quickly and painlessly so you at least know where to start looking. Try plotting yourself on the graph below - there are many other factors to consider, and we'll explore all of this in more detail in future posts but in the meantime, what do you see see as the strongest needs? What are you data challenges? And which solutions have you tried?