The Smart Way to Deal With Messy Data

The Smart Way to Deal With Messy Data

The Smart Way to Deal With Messy Data

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.

Read Also:
Is Data Management Broken? Can It Be Fixed? -

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.

Read Also:
IoT Analytics – Living on the Edge is the way forward for now

If this processing were unavoidable, then it would just be a cost of data sharing within or between organizations.

 



Read Also:
Internet of Things Transforms Business for Supply Chain Industry
Read Also:
4 Best Practices for Data Lakes
Read Also:
What Does the Cloud Mean for Big Data Analytics?
Read Also:
Is Data Management Broken? Can It Be Fixed? -

Leave a Reply

Your email address will not be published. Required fields are marked *