Pssst: Data isn’t tech.
Certain companies are self-identifying / branding with buzzwords like “big data” and “big tech” to portray themselves as future-forward behemoths with “data-driven” cultures.
Why?
Because. They. Use. Data.
And because of that, it totally makes them technologically advanced! Right?
RIGHT?!
Who knows? Maybe the company has its own super-duper-MAX2100-data-driven-culture campaign, and it ALMOST runs like a well-oiled robot. Or does it? Or perhaps maybe they’re working on repairing rusty ROI?
Nonetheless, this post will discuss why data isn’t tech, why it’s essential to know the difference, and where to go from there once you do.
Off to the future we go!
First, some clarification.
So, what makes me qualified to explain the difference between tech and data?
Professionals like myself are (sometimes) called Citizen Developers or Project Managers because we analyze data, processes, and more to solve business problems. Citizen development is a specialization in project management. Plus, my bachelor’s degree in Applied Management (not the same as Business Management) was statistics and data analytics heavy. I also get pinged for data science analytics roles (none have been 100% WFH — hint hint). Also, check out my portfolio!
Get data-informed first.
To understand why data isn’t tech, we need to understand what data is.
Data comes in different formats (like numbers and letters) and gets compiled in databases, spreadsheets, and data visualization tools.
To simplify, we analyze data with tools and technology. Then, once we can analyze data because the tools helped to make it digestible, we gain new insights.
Wild, right?
However, before we can analyze data, it has to go through different methods and processes to ensure that it’s accurate, usable, categorized (correctly), etc. And we do that through “tech” or, to be more transparent, a computer.
So, what’s up with everything being data-driven?
Simple misinterpretation, perhaps? (Nope!)
First, we need to keep in mind that people, in general, are change-averse.
Then, with that in mind, what frequently happens during project implementation (*ahem* going full-on data-driven) when an entire company (or a majority) isn’t on board?
The project fails.
But before a project fails, we must remember what happens before that…when there are goals/deadlines/benchmarks to hit. Perhaps some team members need to learn something NEW too. And it needs to be implemented quickly because of deadlines!
That’s a total disregard for data. It’s not data-driven.
And the kicker…
Projects fail more often than not, and becoming data-driven is worse!
Note: the quiz is a Google Form. You need a Google account to take it and see your results.
Can you guess the percentage of orgs in 2022 still struggling to become data-driven? You might be surprised!
Also, the Standish Group reports that about 31% of software projects are successful, and 46% of those return a high value. (I’ll stop this rant and get back to the point.)
In my humble opinion, I think people mistake data for technology because we usually need tools to compile, clean, and aggregate data into something measurable. Then, once it is, business analysts and project managers translate that information into meaningful insights, models, and more.
Here is where the confusion happens—the processes and methods used to transform raw data before it goes into visualization tools are technical, perhaps more so for people who aren’t software or data engineers.
Then, if those same people are tasked with creating a “data-driven culture” but don’t understand the difference between data and tech, it quickly becomes apparent.
That can negatively impact company culture, especially if most of your employees are privy to the difference between “tech” and “data.”
Disclaimer: an AI helped me write part of this post (and it still required A LOT of editing — nonetheless, thank you, You.com, for creating YouWrite! When published, I’ll write about the experience at the end of the series and place the link → here ←. In the meantime, read my 5-part Innovation Series, the first post in this AI series, or learn about different types of AI.)
What’s the AI tool’s perspective?
There is a big difference between tech and data; bosses, managers, change agents, executives, directors, and company leaders must understand this before launching a data-driven culture or digital transformation initiative.
Tech is about the tools we use to create or access information. Data is the information itself. So when we talk about “big data” or “data-driven cultures,” we are talking about using data as an input for decision-making.
Many organizations assume that because they have access to lots of data, they must be doing things in a “data-driven way.” However, having access to data does not mean they’re using it effectively. To be truly data-driven, you need three key components:
- Good quality data
- People who know how to analyze and use that data
- Set processes to gain insights faster
Without all three of these components, it is very easy for management teams to misinterpret the difference between tech and data, which could be potentially disastrous for their organization’s transformation efforts.
[Comment from the author (the human, me, Jarred): WOW! That’s a pretty impressive explanation!]
So why is it essential to know the difference between tech and data?
First, the business landscape is becoming increasingly complex and competitive. As professionals, we must continually improve our skills to keep pace and ensure our own “continuity.”
As we develop our skills in an increasingly digital business landscape, some might find ourselves helping with new digital transformation initiatives. Like helping our bosses make the company more “data-driven.”
And with that, it is crucial to note that data-driven doesn’t mean tech-driven.
(Personally, I think it should be renamed “people-driven” or “employee-driven” transformation because we input the data and transform it into something meaningful.)
So, to reiterate:
It’s not tech that will do that.
It. Is. People.
It’s not data that will do that.
It’s… (say it out loud with me) …PEOPLE!
Who leads those digital data-driven-everything transformations?
BOSSES! (well, perhaps in a project management sense, but that’s not the point!)
ROBOTS!
AI!
MACHINE LEARNING!
TECHNOLOGY!
DATA!
PEOPLE!
What do we need to help us get on the same page to make our companies embrace data-driven digital transformation initiatives?
TECHNOLOGY
DATA!
PEOPLE (who understand the difference between technology and data)!
What is the AI’s perspective on the above section?
Data-driven doesn’t mean tech-driven. We must use data in everything we do, from marketing and sales to product development and business operations. As professionals, we must understand how data can drive our business decisions.
Fortunately, there are plenty of resources to help us learn more about data analytics and other aspects of the digital world. For example, online courses like Coursera or edX can give you a good foundation in data analytics and related topics. Vendor-sponsored courses are available on subjects like big data management or cloud computing if you want something more specific.
Keep it simple
The ability to categorize differences like the ones above helps us gain a better perspective vs. overcomplicating things. Heck, it could even help us be more precise when explaining a change initiative. In addition, simply understanding the difference allows us to communicate complex scenarios more efficiently and deliver that information to less tech-savvy or change-resistant cohorts.
Plus, the more succinct you communicate your plan, the more your cohorts will be more apt to understand things better. Who knows? A more precise understanding may help them be more confident in sharing their input/feedback. After all, you made it easier for them to do so. (Gold star!)
In closing, now you know that data isn’t tech!
As you can see, there’s a colossal difference between data and tech, and having a better understanding of the differences allows us to take a step back, reflect, and better pitch our data-driven approach(es) while becoming more effective change agents!
Ultimately, it all comes down to understanding and communicating in a more human vs. technical way. This can also help us be more intentional about using technology for good, understanding customers, and more.
At the end of the day, we are just people trying to optimize our company’s decision-making capabilities to solve problems better. And be able to participate in the change process!
Leave your comments below on how your company has successfully transitioned toward being more data (vs. “tech”) driven!
