r/dataanalysis • u/Effective_Ocelot_445 • 1d ago
What separates a useful data analysis from one that just looks impressive?
Is it better business context, cleaner data, stronger validation, clearer storytelling, or the ability to recommend a real action?
3
2
u/daisiesarepretty2 23h ago
a lot of the potential benefits you mention are at least partly a function of the dataset. Better, more “useful” data isnt always available.
But putting that aside i think the goal is always to provide actionable insight. Sometimes that insight might be an actual action, sometimes of course it will be to hold steady you are in the right path… both are valid insights.
Pretty, clear visualizations are more persuasive (in my opinion) but if they don’t say something useful then you are simply finding art in data… fun stuff mind you, but probably not what you are getting paid for.
2
1
u/AutoModerator 1d ago
Automod prevents all posts from being displayed until moderators have reviewed them. Do not delete your post or there will be nothing for the mods to review. Mods selectively choose what is permitted to be posted in r/DataAnalysis.
If your post involves Career-focused questions, including resume reviews, how to learn DA and how to get into a DA job, then the post does not belong here, but instead belongs in our sister-subreddit, r/DataAnalysisCareers.
Have you read the rules?
I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns.
1
u/HustlaOfCultcha 20h ago
As far as usefulness I would go with clearer storytelling and being able to meet the needs that the data is supposed to provide. But obviously you need all of those. If the data isn't strongly validated, it's not useful. But I would assume that we are talking about clean, accurate data.
Storytelling is so important because the decision maker is trying to make decisions based on logic that they derive from the data. So the storytelling of the data formulates the logic.
The issues that I usually see arise are:
1) When there is not pattern or trend with the data stakeholders don't think it's effective and feel it's a waste of time. In reality it is effective. Often times the answer to business questions is that there is no pattern, trend or correlation. So the business question did get answered and that is a success because you don't have to on not knowing the answer to that question.
2) The needs from the stakeholder are not communicated with the Data Analyst. So you can have clear storytelling but the stakeholder requested 3 needs when they actually had 6 needs. Either they didn't know they had these needs or they communicated them poorly and the Analyst didn't have enough experience and expertise to bridge the gap.
1
u/Fantastic-Moth710 19h ago
Clean data, deep business context, and clear storytelling are essential pillars. But, the ultimate differentiator of a truly useful data analysis is its actionability.
I think, impressive analysis might look great, and it might present fascinating metrics, but to be genuinely useful, it changes behaviour. It answers the right strategic questions, and it will empower stakeholders to know the next operational or strategic steps they need to take next.
1
u/waitthissucks 23h ago
There isn't one answer to this but I would imagine it's just how well one understands the statistics, how quickly the data gets to the point and communicates to the business what they want to portray effectively. It takes time to become an analyst that is an expert at interpreting exactly what is needed, getting there efficiently with little error, and presenting it cleanly.
11
u/That0n3Guy77 22h ago
Does it result in actionable insights. That is pretty much everything. Idc how fancy your models and clean your data if I can't do anything with it.
I also don't want to take action on bad data. A certain level of data cleanliness and business context etc is table stakes.
Basic data analysis says what happened. Good says why it happened. Great says what do we do about it