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 doesn’t? 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?
Business Analysts like myself are (sometimes) called Data Analysts because we analyze data to solve business problems. Plus, my bachelor’s degree in Applied Management (not the same as Business Management) was statistics and data analytics heavy (compared to typical Business Management degrees). I get pinged for data science analytics roles too (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 then 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 the way we do that is through the use of “tech” or, to be more transparent, a computer.
So what’s up with data-driven everything?
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 a 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 need to remember what happens before that…when there are goals/deadlines/benchmarks to hit. Perhaps, some team members needs to learn something NEW too. And it needs to be implemented quickly, because 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. For security, and to take it/see results, you need to have a google account.
Can you guess the percentage of orgs in 2022 who still struggle 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 here and get back to the point.)
In my humble opinion, I think people mistake data with technology because we usually need to use tools to compile, clean, and aggregate data into something measurable. Then, once it is, business analysts and other professionals take that information and translate it into more 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 — IS technical and perhaps more so for people who aren’t software or data engineers.
Then, if those same people are tasked to create a data-driven culture, but don’t understand the difference between data and tech — it’s apparent.
Even if they can’t see it themselves.
And that can impact company culture for the worst. 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 — none the less, 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 or the first post in this AI series.)
What’s the AI tool’s perspective?
There is a big difference between tech and data, and it’s important for bosses, managers, change agents, executives, directors, and company leaders to 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 really 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, simply having access to data does not mean you are 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 – with potentially disastrous consequences 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 growing increasingly complex and more competitive by the day. And with that, as professionals, we need to continually skill up to keep pace to ensure our own “continuity” (see what I did there?)
As we develop our skills in an increasingly digital business landscape, some of us 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 get renamed to “people-driven” or “employee-driven” transformation because we input the data, and transform 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 all 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 need to use data in everything we do, from marketing and sales to product development and operations. As professionals, we must understand how data can be used to drive our business decisions.
And 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 offered by providers like Coursera or edX can give you a good foundation in data analytics and related topics. If you’re looking for something more specific, vendor-sponsored courses are available on subjects like big data management or cloud computing.
Keep it simple
The ability to compartmentalize 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 more easily communicate complex scenarios and deliver that information better to less tech-savvy or change-resistant cohorts.
Plus, the more succinct you are in communicating your plan, your cohorts will be more apt to understand things better. Who knows? Maybe having a more precise understanding will 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 is a colossal difference between data and tech and having a better understanding of the differences between the two 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.
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!
