Data powers the modern world. The trouble is, sometimes not all of our data is useful for achieving our objectives in its current form. Other times, we may have to look to other departments or even other institutions to acquire the data we need to draw meaningful conclusions.
This is why data recycling and data restructuring come in handy. But what do these terms mean, and in which situations is one technique a better choice than the other?
What is data recycling?
This is a useful technique for wringing extra insights and value out of existing information. In a nutshell, recycling is where companies take data that's already proved useful elsewhere in solving a problem or drawing a conclusion, and then use it again to draw conclusions about something else.
As you can see, the term "recycling" is appropriate — it's a little like taking a pile of old aluminum cans and using it to make new window casings. In some cases, recycling your data is a good way to make it useful for another department or team within your organization. You've probably heard about "siloed" data. Data recycling can be a good way to get your data out of those silos and put it to wider use.
What is data restructuring?
Restructuring your company data can serve similar ends as recycling, but it differs in a key way. Instead of taking a set of data initially used for one purpose, and using it again for another purpose, restructuring involves giving your existing data a "second wind". In other words, it involves using a tool like IBM's Restructure Data Wizard to manipulate and reorganize applications and tables so they become more relevant for answering a new question.
For example, if you have a case group, it means you have multiple "rows" of related data that describe something which can be observed. With a data restructuring tool, you can identify variables within this case group with similar "identification values" or interesting correlations — and then use those variables to construct a new data table.
When does it make sense to recycle your data?
There are several situations in which data recycling might make sense:
- Real-time, "live" data is the gold standard for drawing meaningful and timely conclusions about a given topic. Sometimes, companies don't (yet) have the means to capture live data. By engaging in data recycling, they can begin enterprise planning using the information they do already possess, while they're developing their live data capture techniques and storage solutions.
- Recycling can be a great way to engage other businesses or organizations in meaningful partnerships. For instance, research colleges all over the world solicit data from the public about a variety of topics, often gathered through national surveys conducted by other groups, and use it to illuminate whichever topic they happen to be studying. It could be consumer behavior, environmental studies, political activism or something else entirely.
Data recycling can be a richly rewarding collaborative effort. It's a way to ensure existing data sees as much use as possible — by benefiting other teams within your company, a business partner or even a non-profit entity.
When does it make sense to restructure data?
Here's a common problem among organizations that depend on data and data systems: over time, your reporting requirements change or you develop a need for deeper analysis. That's where restructuring comes in. Here are some cases where data restructuring might make sense for your business:
- Perhaps you've accrued so much data that generating routine reports has become a time-consuming or resource-intensive endeavor. Restructuring your data helps lift the most meaningful information out of your databases and uses it to create a leaner data structure that's faster and easier to process and report on.
- Maybe your company's goals have changed. If so, your databases are likely still relevant on some level — but perhaps not as relevant as they could be. Restructuring variables into cases and vice versa, for example, means you can look at the same information through a different "lens."
Other times, restructuring your data makes it compatible with a wider variety of programs. You might even have teams within your organization that use different programs for interacting with company data. As an example, manually trading data between, say, an Excel data file and SPSS or Minitab introduces the potential for human error unless you use a smart data restructuring tool that takes the needs of each platform into account.
Ready to choose?
Recycling and repurposing data can solve a variety of short- and long-term needs for your company. If there's one caveat, it's that recycling data between companies or organizations comes with some legal and privacy issues. Before you engage in this kind of exchange, you need to be sure you have permission to share that data in the first place.
With that warning out of the way, it's fair to say both of these techniques could help you breathe new life into your data and fuel useful insights and business development.
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