Data Driven or Data Inspired?

Having data about our company may be helpful in a lot of aspects, but how exactly should we be using that data?

Firstly, we need to clarify the meaning of data driven and data inspired decision making in organizations.

Data driven means that we have the data to determine the outcome of an outstanding decision (Stewart, 2019). It implies that our decisions will be made based solely on the data we have available, as we place the available data-set at the center of the table and make business decisions around it (Kaushal, 2019). 

Data inspired implies to consider data as an important asset but never use it by itself, and instead combining data with context in order to have a deeper meaning of what we are analysing.

According to Kaushal (2019) if data says something, we have the right to challenge it, because the real growth lies when intuition, experience, and wisdom are put to practice.

With the growing importance of individuality, it no longer makes sense to make assumptions based only on data, because pure data doesn’t comprise context.

Context is what gives meaning to data. Pure data is only meant to answer a very specific question, and context will open up the meaning of data, since it will make data more than just “numbers”. 

Imagine that we want to analyse the health of an organization, should we just analyze, for example, data concerning the amount of sick days taken in a company in a specific period? Or should we look at health in a more profound and systemic way?

If we only rely on the data we have on how many sick days the workers in our organization have taken in the last year, to draw conclusions on the level of organizacional health of our company, this will mean we’ve chosen to be data-driven. But if our approach is based on a multiplicity of inputs and not only objective data, or if objective data is only being used in a complementary way, this means that we are data inspired. 

Let’s take an hypothetical scenario. Imagine that in your company the team appointed to work at the assembly line is the team that has taken the least amount of sick days last year, but when you go and watch them at their job, you learn that they rely greatly on each other’s attendance to keep the assembly line working and that specific working conditions are the cause of the injuries appointed as the reason of the sick days they actually took. When you talk to the workers, you learn that most of them have stress related issues because of how monotonous is their job, and that most of their injuries are caused by how difficult it is to maintain focus in a task that is so automatised in their brains. So, can this really be considered healthy working conditions? Could we have understood this only by looking at data?

What about engagement? Are pulse surveys enough? An engagement survey is about obtaining employee feedback and opinions. It’s not about finding a final number or score (Brown, 2020). What if our workers have really high levels of engagement but aren’t being able to find a healthy work-life balance/or integration, are the levels of engagement that positive as they looked initially? It is then crucial to always evaluate our quantitative results (data points) alongside our qualitative feedback (e.g., open-ended comments or even interviews). Using them together will help us determine next steps and have a more clear and adjusted action plan to promote organizational health.

To build a more efficient way to look at data, here are 6 steps, according to Khurana, Wery, and Peirce (2020), we can consider:

1. Business decisions and analytics: Prioritize analytics insights that fuel the business strategy, not those that just report what’s happening.

2. Data and information: Let the data tell a story through the flexible integration of multiple data types, rather than forcing the data into a predefined model.

3. Technology and infrastructure: Build tools that support the analytics ecosystem, including AI, and democratize insights through analytics-as-a-service (AaaS).

4. Organization and governance: Establish an operating model to empower the use of governed data and analytics.

5. Process and integration: Ensure insights are rapidly integrated into decisions through an aligned, agile process.

6. Culture and talent: Establish a data-inspired culture that blends business knowledge and analytics insights across all levels.

We can conclude that if we have a human factor, we shouldn’t just let data tell the story, we should always give insight and add context to our analysis, especially when it comes to organizational health and behavior.

Considering the specific case of organizational health (i.e., a concept that goes beyond the individual employee health status level to integrate organizational culture, processes and practices and its respective alignment with sustainability principles) it is even more important to include the insight of different teams like Management (Top and Middle), HR, Health & Safety, and others, in order to help give meaning to data and promote more integrated and reliable decisions concerning preventing measures.

References

Aishah, N. (2020) Know what you are talking about: The difference between Data-Informed, Data-Driven and Data-Inspired in a nutshell. Available at: https://medium.com/sapera/data-informed-data-driven-data-inspired-whats-the-difference-b02464a99641

Brown, A. (2020).  How to Measure Employee Engagement the Right Way. Available at: https://www.quantumworkplace.com/future-of-work/the-right-way-to-measure-employee-engagement

Kaushal, D. (2019). Data-Driven Vs. Data-Informed: Know the Difference. Available at: https://www.netsolutions.com/insights/data-driven-vs-data-informed/

Keller, S., & Price, C. (2011). Beyond Performance: How Great Organizations Build Ultimate Competitive Advantage.

Khurana, A., Wery, R., & Peirce, A. (2020). How companies can transform information into insight. Available at: https://www.strategy-business.com/article/How-companies-can-transform-information-into-insight

Stewart, S. (2019). Are You Data-driven, Data-informed or Data-inspired?. Available at: https://blog.amplitude.com/data-driven-data-informed-data-inspired

Walker, G. (2018). The Importance of Context in Data Collection. Available at: https://www.automation.com/en-us/articles/2018/the-importance-of-context-in-data-collection