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 be on 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 have no need for the data and it is not producing 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.
Achieving Expertise
Analyzing big data, configuring machine learning algorithms, evaluating outcomes, and implementing the underlying analytical systems for big data requires a high level of expertise in a variety of disciplines. Some organizations do not have the expertise or they are in the process of developing that expertise.
Those that are new to analyzing big data might seek the help of a trusted partner to get them up to speed or they may outsource the role entirely. Given the value of organizational data and the risk of exposure, however, outsourcing should be treated with a due diligence assessment of the outsourced company’s capabilities and reinforced with a strong contract.
Building Better Models and Tools
Those who are using big data probably see room for improvement, especially in the models they are using to interpret the data, and in lesser cases, the software and infrastructure they utilize.
Cloud computing can offer great advantages in expanding to meet big data needs and in providing the raw computing power to analyze large data sets. Other companies are deploying private or hybrid clouds so that they can offer more customized analytics to decision makers.
Performing Better Housecleaning
Lastly, some only use 1% of their data because they simply do not have a policy and procedure for removing useless data. A large component of this is the data retention policy which spells out how long different types of data will be stored by the company and when that data will be destroyed.
Additionally, some data that fits certain criteria may be removed immediately. This might include spam or other junk emails, draft files, temporary files, Internet history, cookies, or encryption keys. Removing this data makes it easier to manage the remaining data and it can prevent malicious outsiders from obtaining data that could be used to launch attacks or otherwise harm the company or its customers.
Humans only use a small portion of our minds, and we use an even smaller portion of the data we collect. The good news is that there are viable strategies companies can employ to begin utilizing more of that data. So what is holding you back?
