Let’s get right to it: just because everyone’s talking about data visualization and data analysis and big data doesn’t mean that any of it’s easy.
The problem with using data to make decisions or to tell stories is that you have to have data in the first place.
And that’s where the idea of “garbage in, garbage out” in data comes from: if you have bad data, you’re going to end up with bad analysis.
The best part of having data is using it to make decisions. But if you have bad data, the truth is that you’re only going to make bad--or at the very least, uninformed--decisions.
Most business intelligence and data visualization platforms are BYOD (bring your own data).
BYOD can be a tough system when you’re trying to extract value out of data though because the process of cleaning data and making it usable for analysis (called ETL, which is short for “extract, transform, and load”) is a long and winding road.
Getting good data (or trying to turn bad data into good data) isn’t easy--or fun. That’s coming from the two types of people who often get saddled with this job: the data scientist and the business intelligence professional or analyst.
And they also consider those two parts of the process to be the least fun parts of the job.
In a perfect world, you’d have perfect data that’s always clean--no blanks, no special characters, no accidentally double-counted assets. Everything would add up and your assets subtracted by your liabilities would be one beautiful zero.
But in an imperfect world, we find workarounds.
If the data that you want to analyze doesn’t have to be something you generate or own, find a platform that aggregates all of that data already and has it cleaned and organized, just ripe for your analyzing pleasure. If it has visualization and analysis tools built-in? Even better.
Let someone else do the data collection and upkeep for you, so all you have to do is explore and find insights--the fun parts.
When you’re looking at data analysis and business intelligence tools, it might feel like BYOD is your only option. But depending on the type of analysis you want to do, looking for a platform that comes with its own extensive dataset could be your best bet.
Pharma companies spend a lot of money in R&D--more than almost any other industry. And the R&D ratio (R&D compared to revenue) is interesting because it can tell us how invested in new product development one company is compared to another.