I’ve been working with data for a long time, and I love it. I’ve always found it helpful in my work and my day-to-day life. But lately, I’ve noticed that more and more people are trying to use data to make decisions — and that’s not always such a good thing.
Big data is great, but it’s not perfect
Big data is great, but it’s not perfect. The term “big data” refers to the collection and analysis of large amounts of data. It can be used for many things, including predictive analytics and machine learning.
Big data is important because it allows us to make better decisions by providing us with more information than we would have had otherwise. For example: imagine you want to know if your company should hire more employees now or wait until next year when demand increases due to an increase in sales (and thus profits). You could look at historical data on past hiring cycles or rely on expert opinion from someone who has been working at this company for many years–but neither option would give you very precise results because there are too many factors involved in making such an important decision! But if instead of using these two options alone we combine them together then suddenly our predictions become much more accurate; indeed this is exactly what happens when businesses use both traditional methods along with newer ones like big data analysis techniques like regression analysis where they’re able
We don’t need more data
We don’t need more data. We already have a lot of it, and most of it is not useful.
Data is data, right? Well not exactly. The term “data” means different things to different people: If you’re talking about your Facebook friends list or how many times you’ve been tagged in photos on Instagram, then yes–that’s all technically “data.” But if you’re talking about the information that’s collected from sensors in your car or fitness tracker, then no–that’s not really considered “data” at all (yet).
When we talk about Big Data and Analytics here at Cloudera HQ in Silicon Valley–and trust us when we say that we do talk about them a lot–we mean something specific: We mean big sets of structured information stored on computer systems so that they can be analyzed by software tools like Hadoop (which powers our products).
Data tends to be noisy, so you need to filter it out
What’s the point of collecting all that data if you can’t make sense of it? To do so, you’ll need to clean up your data.
Cleaning your data means filtering out the noise and outliers so that only good information remains. For example:
- You might want to filter out bad data points (i.e., ones that are missing or clearly wrong) before analyzing your results in order to get a more accurate picture of what’s really happening with your business or organization at any given moment in time;
- Or perhaps there are certain types of information that aren’t relevant for whatever reason–if someone has entered their age as “12” instead of “21,” then this would be considered an outlier because most people don’t turn 12 until later on in life!
It’s easy to fall in love with your own data — and your own ideas
You might be thinking, “I have the data to back up my claims.” But that’s not enough. Data can be misleading, especially when you’re looking at it through your own lens.
What if you’re wrong? What if there are other ways of viewing the same set of facts? What if other people have better ideas than yours? These are all questions worth asking yourself as you make decisions based on your own analysis and interpretation of data.
Be careful when you’re making decisions based on data.
Don’t be fooled by data. It can be misleading, biased, wrong and incomplete. Even if the data is accurate, there are many ways it can be misinterpreted. Data can also be manipulated to tell a story that isn’t true at all–or even worse–to support an agenda you don’t agree with in the first place!
The bottom line? When making decisions based on Big Data and Analytics (BDA), keep these five things in mind:
We can’t rely on big data to make decisions for us. It’s just too complicated. But we can use it to help us make better ones. We need to be careful not to fall in love with our own ideas and data, but by filtering out the noise and evaluating each piece of information carefully, we can make sure that what we do know is as useful as possible for making decisions about your business or personal life