Data Minimalism: The case against data hoarding

For the next 5 years the size of data available is expected to grow at a rate of 40% per year.

The awareness of data as the most critical asset for an organization keeps growing but the results will not follow and despite large investments to manage this crescent entry of data, most organizations are still unable to retrieve the meaningful insights that will enable them to take advantage of the potential created by all this data.

Less is more

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.

More and more data is 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 analyse it and derive useful insights to feed business decision processes.

It is increasingly 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 Minimalism

Data minimalism sounds counter-intuitive, especially 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.

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, 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 analysed with an end in mind, sustained by a business case and aligned with the business objectives.

Data Privacy

This concept will also prove critical when addressing customer data, especially 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.