We may not be using more of our brains, but we can probably use more of our data. Did you know that organizations typically use only 1% of the data they collect? Why is this, and how can we change it? Do organizations need more motivation, utility, expertise, tools, or just better data retention policies?
The Problem of Motivation
Motivation is the driving force behind activity, but businesses, and the people who run them, are often juggling many priorities. Big data may just not just be at the top of their list.
It is easy to push analyzing organizational data because it seems easy to keep it around. After all, storage media continues to grow and is available at lower cost. There are, however, additional costs, especially associated with the loss of data in a breach, that organizations do not often factor in when considering the cost of storing data that may or may not be utilized in the future.
At the same time, some organizations struggle with motivation because they are always waiting to collect a bit more data before analyzing it. Statistical analysis is typically more reliable as data sets grow.
Machine learning, however, can be used to fill in the some of the gaps once an analytical program has been sufficiently trained. Still, no matter what method is used, a minimum amount of data will be needed for a relatively accurate analysis, and some companies are afraid of acting on potentially incorrect data. The downside of this is that they are also waiting to capitalize on the benefits of the data they hold.
Motivation needs to come from the top down if you want the use of data to be both successful and consistent. Organizational leaders must decide what they want to achieve from their data and then empower those best suited to analyze it the task of putting it all together to obtain meaningful and valuable results.
Finding the Utility
Lack of data ROI could also be due to a lack of value or utility. Some organizations collect data just because it is there or because it was provided. In reality, they do not need for the data, and it is not providing them any value.
In this case, the best course of action is to make an informed decision as to whether the data is valuable. If it is not valuable, the organization should delete the data so that they do not have to expend resources managing and protecting it.