Non-technical barriers to data-sharing in organizations

Sharing data is hard.

In part, this is because storing data is so easy. Collecting and storing the volume, velocity, and variety of data today is easier and cheaper than ever before. This unintentionally creates huge data silos that organizations now realize impede their ability to get data from the people that have it to the people that need it.

We’re making great technical strides in building integrations and connections across our data repositories.  So why, despite the $4.5 trillion dollars spent in worldwide IT in 2022, do organizations still struggle to make data-driven decisions? 

Computers are easy.

It amazes me to think about the technology we have to successfully get billion-dollar computer systems to talk to each other, yet we struggle to get two people who work in the same organization to do the same. 

Machines collaborate better than humans.  Why? 

For starters, computers are easy, relatively speaking.  Enter the same input a dozen times and get the same output each time. People are harder and infinitely more complex.  Enter the same input a dozen times, and you’ll likely get very different outputs each time.  Human behaviors are dynamic, changing, and to an extent, unpredictable.

Non-Technical Barriers to Data-Sharing

The truth is, it’s not data that gives organizations a competitive edge. It’s the knowledge that is extracted from the data.  And the difference between data and knowledge is the human.   

When implementing your data and knowledge management strategies and systems, the tech you choose will no doubt be a huge factor in the success of the platform. But also think about the following non-technical considerations:

1. Social

Creating an innovative technical solution without considering the people that will use it is like building a house and letting it sit vacant. Who will use the platform and how?  What motivates users to participate? How can we increase adoption by increasing participation and collaboration towards a shared mission? Develop tools like personas to help research, identify, and understand what motivates users in order to create the most effective digital experiences possible. Build flexibility into your processes that can adapt as your personas change.

2. Cultural

Consider the team structures in your organization — the key interrelationships that influence behavior over time. How do team structures, norms, and needs impact data sharing, collaboration, and decision-making?  How does this manifest itself in lexicons, taxonomies, and ontologies?  How can we make allowances in our strategies for natural and necessary differences in team behaviors?

3. Organizational

How do we turn data into knowledge, and then share that knowledge with others that need it?  How do we measure the outcomes of our decisions?  How do we measure not only time to insight, but time to learning? Document the learning loops in your organization. How low does it take for data to be prepared and contextualized into information and knowledge? Once you know that, build specific features into your tools that employ machine and human factors to accelerate those learning loops.

Social, cultural, and organizational considerations enhance technical implementations. It’s important to remember that behind every data point is a person.  A person that has the data. A person that needs the data.  Any many people that deal with the consequences of decisions driven by that data.