In part one of this two-part installment, we will be talking about the pervasiveness of data in our everyday lives and the implications and consequences. In the second part, we will talk about how data can have unintended consequences in work and life. We will share strategies to overcome these potentially detrimental effects while embracing the positive side of data.

As we kick off the first month of the year and many individuals and organizations are establishing targets in their personal and professional lives, I thought it would be a good time to talk about the metrics we use to set those targets – otherwise known as data.

We live in a data-driven world. From iPhone watches measuring our health parameters to large data centers computing terabytes of data per second, the use of data in our world has become inevitable. The International Data Corporation estimates that in 2010, the world contributed about two zettabytes (ZB) of digital information. That is nothing compared to the amount of data collected in 2020, calculated at 44 ZB by the World Economic Forum. For those of you who may be wondering, 1 zettabyte is a trillion gigabytes. That is 1,000,000,000,000,000,000,000 bytes of data!

Why Should You Care?

As the world continues to evolve, and data exponentially with it, “knowledge-workers” are on the rise. Individuals and organizations that will thrive are no longer task-centric, like centuries ago (think about assembling a certain number of widgets a day), but rather prioritize knowledge as their main capital. The ability to think for a living has become increasingly indispensable, and with it, the ability to read, assess, sort, and critically evaluate data.

Garbage In, Garbage Out – Data Without Context Results In Misinterpretations

As a pre-med student, I was fascinated with the world of computers and data, and knew it was going to forever change the way we lived. So, I veered from the typical pre-Medical undergraduate degree path of biology, and instead obtained my pre-med degree in Computer Science and Mathematics.

As a Computer Science major, I learned a valuable rule very early regarding data in programming; garbage in means garbage out. One thing became evident, whether the code I entered was accurate or not, the result was always reflected in the data. The output was based on the input – if I did my programming code with good data, the software executed what I wanted it to. If I fed garbage in, it meant I got garbage out. The important lesson I learned was that if the variables used for your input are inaccurate, the output – upon which decision-making is hinged – will be inaccurate as well.

Healthcare, like most other industries, has become dependent on data. Now, almost two decades in the healthcare field, I have found the same principles apply. Using the wrong input – misinterpreted data – can lead to setting inaccurate targets and wrong decision-making. So, it is important that when we are presented with data, targets, and setting metrics, we are constantly asking the story behind the data.

The ability to sort through data, ask the right questions about the data you are presented, and understand the story behind the metrics is an indispensable tool in today’s leaders and knowledge-workers. Your ability to successfully optimize your data-driven decision-making depends on this ability.

Data Talks! Are You Listening? – Questions To Ask Of Your Data

Data alone can only take us so far. Increased data tracking was originally intended to empower us. Grant us insight through patterns and trends. Unfortunately, cases where data tracking has led to negative effects are growing.

As a leader, it is important that when you are presented with data, you automatically flex your thinking and ask yourself a few questions – How was this data captured? What is it supposed to measure? Are we asking the right questions to capture this information? If not, why not? Always ask – what is the story behind this data?

This is my practice when relying on data and I have found it indispensable. In the next part, we will talk about lessons from Amazon, and how to create objective practices for reviewing and critically analyzing data.