Less is more (From data hoarding to data minimalism)

Data Hoarding

As we watch organizations struggling to collect as much data as possible, we can also see the infrastructure, storage, processing, and analysis costs increasing at the same time as the quality of analysis and insights is decreasing.

With more and more data being accumulated across data warehouses, data lakes, always with the perception that more data can be collected, overlooking the fact that the more data is collected, the more redundant and obsolete data is gathered and the harder it is to analyze it and derive useful insights to feed business decision processes.

The practice of data hoarding impairs the capability to extract value from data and reduce the possibility of successfully use data in the decisions processes – Too much data is an obstacle for a data-driven transformation.

It’s essential that all this data collection is consistently planned before it happens, creating strategies to make sure that that the data being collected is being used, making sure data is clean and well managed, maximizing the value of the information, but also the value of data as a strategic asset.

Data Minimization

Data minimization is an essential principle of data protection, and it refers to organizations restricting the personal data they collect from individuals and processing only information that is necessary to accomplish business purposes.

Data minimization involves restricting not only the collection of data but also deleting data no longer useful and setting limits for data retention.

This principle is critical in the light of the increasing regulations, and the increasing data privacy and security concerns among customers.

This context is creating the need for organizations to collect only the necessary data to enable them to provide their products and services and being fully transparent about it to its customers.

Customer trust around data is becoming mission critical for most businesses, and they must design their products for transparency, trust, and responsible usage of data, so that customers can trust they’re only collecting the data that will help them improve products or services.

This new level of transparency will rebuild trust. And trust is being increasingly perceived as a key differentiator for customers when deciding on their relationships with organizations.

But it should be taken even further, it must be taken outside the boundaries of data protection and extend to all data within the organization. A central point in the organization’s data strategy.

Data Minimalism

In a time where increasing capabilities in big data, cloud computing, data processing and analytical tools are being disclosed daily, when organizations are trying to generate and store all possible data - whether they need them or not – making the case for data minimalism may seem out of place.

Data minimalism is a reminder of the final purpose of the information organizations collect: to enable good decision-making.

To be able to maximize the return from their analytical investments, and avoiding data becoming a liability, organizations need to move to collect only the data they need.

Implementing data strategies closely aligned with the business objectives, collecting, and working on the data that is effectively necessary.

Data governance plays a critical role in this change in strategy, assuring that:

• All the data being collected and processed in the organization within a specific context, either operational, regulatory, etc.

• That it collected and analyzed with an end in mind, sustained by a business case and aligned with the business objectives.

Embracing data minimalism, allows a better transition to being data-driven, enhances the decision processes, reduces security risks, reduces costs on storage and on managing data, and increases the customer trust in the organization.