Data is data. It can be quantitative or it can be qualitative. It’s largely neutral (up to the point of how people use it).
Quantitative data is information that can be measured (quantified) or defined with a specific numerical value. It typically answers what actions are happening & what the results are from these actions.
E.g. Visits to our homemage for May = 10,500 visits (sessions).
Qualitative data is information that describes in terms of language, behavior, & perspective. It’s subjective & gets at the why. “Why did you perform that action?” It often explains why someone is engaging in a behavior or taking action.
E.g. “I visited the homepage because I wanted to purchase a new pair of black dress shoes, that would look good with my suit, for a wedding. This is a very important occasion for my friends so I want to look my best. One of my friends recommended the website to me."
Both types of data can be extremely important.
Let's be honest. Data can be fabricated, but then that's not really data that's just fabricated baloney or outright lies.
Analytics is finding relevant insights from data. Sometimes analytics is also just peeling back a layer & learning what one doesn’t know or what needs to be investigated further.
"Analytics is about distributing knowledge, not data." - Gary Angel
We constantly need to be aware of the context such as:
- Is data from an accurate source?
- Is it reliable?
- Is it relevant?
- Is it precise?
- How confident are we in this data?
- What conditions are being applied? Or how are we defining it? What measurements are we using?
- What does it tell us? What does it not tell us? What are the limitations?
- Has it been manipulated?
Data can be manipulated or be deceptive based on intent & the conditions/criteria one applies to it. Think bad accounting practices such as Enron, which can = lying with fancy numbers. (Captain Obvious & our law firm advises us not to be like Enron. So don't be like Enron).
Data can also be corrupted if proper protocols are not followed or they're exploited. In turn, this can spillover & create additional issues for an organization.
Thus, the context for data matters...all of the time.
(Two of my favorite events about context that are bit less dry):
1. When a world class musician goes from "...$1,000 a minute" to "...$32 and change" in this Washington Post experiment.
2. The Guardian captured how an iconic street artist needed 3 hours to make their first sale.
Especially, when you're not at an enterprise level, it's unlikely we have a 100% confidence level in the data, 100% of the time. There are just too many variables, plus we're often relying on third-party tools for collection, storage, & analysis.
Thus, we seek what offers a high confidence level, is precise, & what can provide relevant insights. Google Analytics, (which has plenty of limitations), in terms of collection, storage, reliability, relevance, & context is the best out-of-the-box (when configured properly) analytics solution. It offers a high-confidence level, acknowledging we're very unlikely to reach a 100% confidence level or 100% accuracy all of the time, in such contexts.
In a lot of instances, Google Analytics offers the best/most imperfect information we have, when tasked with making imperfect decisions.
3 Limitations of Data/Business Realities
While I think data & analytics can be enormously beneficial to most organizations, in the context of business, I believe there are at least 3 noteworthy limitations of data. Or if you’d prefer, there are 3 significant business realities that need to be acknowledged.
1. Human beings (including your customers) are irrational & can/do behave based on emotion.
If you're familiar with emotional intelligence, behavioral economics, & research such as the work by Dan Ariely, you understand the impact emotion can have on behavior. Sometimes, logic & rationality do not matter. (I guess this provides some job security for #3, right)?
2. Innovative products & design strategy cannot always rely on data.
Going against the grain, taking big risks, ignoring some data, these sometimes can be necessary for innovation. Simply put, in some instances you & your team might have to make it happen with little to no data. (Is this extremely risky? Yes, indeed. Do products & businesses fail from this approach? Absolutely)!
I’m reminded of how Henry Ford explained this.
"If I had asked people what they wanted, they would have said faster horses."
The reality is, it's not always about faster horses. Henry Ford understood there was a need for faster & more convenient transportation beyond what was available at the time. (One could also reason there are needs for speed, exploration, freedom, & escape).
Innovators, at times, know better than others. Sometimes, this means ignoring the data & all the "Ultimate Guide/Best Practices" guru chatter out there. Instead, it's about execution of the vision when bringing a new product to market (which certainly is not the only approach to follow for every business).
3. Leadership will need to outweigh data at times.
As CEO, I'm certain you have numerous challenging decisions to make. To compound matters, you've likely had to make decisions with very limited information, poor intelligence, or even with nothing at all, while facing numerous challenges. Yet, despite all of these factors & adversity, you've still had to take imperfect action. Typically, it will be the best course of action when all options are terrible. (Yeah, it sucks & it's painful).
Hopefully, you're able to fill in the picture as much as possible, but there’s always some aspect that’s unaccounted for + you can be certain there's always something needing an answer from you...at the worst time (hello Murphy's Law).
These are at least 3 of the realities/challenges with running a business. We both know these aren't going away anytime soon.
Keep pushing forward.
What’s the most challenging decision you’ve had to make as CEO or business owner? (You can change their names). Let me know.
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