The processing required to prepare unstructured data for analysis can be cumbersome and prone to error. That’s why companies should do more to organize their data before it is ever collected.
Unstructured data — data that is not organized in a predefined way, such as text — is now widely available. But structure must be added to the data to make it useable for analysis, which means significant processing. That processing can be a problem.
In a form of modern alchemy, modern analytics processes now transmute “base” unstructured data into “noble” business value. Systems everywhere greedily salt away every imaginable kind of data. Technologies such as Hadoop and NoSQL store this hoard easily in its native unstructured form. Natural language processing, feature extraction (distilling nonredundant measures from larger data), and speech recognition now routinely alchemize vast quantities of unstructured text, images, audio, and video, preparing it for analysis. These processes are nothing short of amazing, working against entropy to create order from disorder.
Unfortunately, while these processing steps are impressive, they are far from free or free from error. I can’t help but think that a better alternative in many cases would be to avoid the need for processing altogether.
We all know how each step in a process mangles information. In the telephone game, as each person whispers to the next player what they think was said to them, words can morph into an unexpected or misleading final message. In a supply chain, layers exacerbate distortion as small mistakes and uncertainty quickly compound.
By analogy, organizations are playing a giant game of telephone with data, and unstructured data makes the game far more difficult. In a context where data janitorial activities consume 50% to 80% of scarce data scientist resources, each round of data telephone costs organizations in accuracy, effort, and time — and few organizations have a surplus of any of these three.
Within organizations, each processing step can be expensive to develop and maintain. But the growth in importance of data sharing between organizations magnifies these concerns. Our recently published report, “Analytics Drives Success with IoT,” associates business value with sharing data between organizations in the context of the internet of things. And, to foreshadow our report to be released in January, we observe similar results in the broader analytics context. But with every transfer of data, more processes need to be developed and maintained.
If this processing were unavoidable, then it would just be a cost of data sharing within or between organizations. A disconcerting point, however, is that there is (or could be) structure in the ancestry of much of the data that is currently unstructured. For example, for every organization that generates a web page based on data in a database, there are likely multiple organizations scraping that data (either sanctioned or unsanctioned) and then processing it to try to regain that structure. In the best case, that’s a lot of thrashing just to end up with data in its original form. In the worst case, it’s a lot of effort to put toward obtaining data with many errors.