Big data has become a very popular buzzword in the business world. Tons of articles talk about how big data can influence your business decisions and lead to new insights, but how do you interpret your big data? Statistics are notorious for being manipulatable, and raw numbers can be hard to make heads or tails of.
Before data can be utilized to improve a business, healthcare, or science, it needs to be interpreted. As with any scientific experiment, after the data is collected, that information needs to be processed and analyzed. This allows mistakes to be identified, outliers to be examined and discredited, and decisions to be made about how useful the information is.
Where Is The Data Coming From?
Before you even start to interpret the data, you need to determine the sources of the data. Big data can be about any number of subjects, like consumer behaviors, the efficiency of machines, weather patterns, and crime statistics.
Interpreting that data starts with a strong understanding of its subjects and the tactics used to gather it. If there is a bunch of data about consumer online shopping patterns, but all of the information is from one website, then the data can’t be trusted as all encompassing, as consumers buy differently from different sites.
The data could still be valuable, but that understanding is essential to properly interpret it. Other necessary qualities for big data are impartial collectors of the data, using accurate tools to measure the data, and how much human error might influence the results.
Knowing How To Visualize The Data
Just having a long list of data isn’t helpful to anybody. The data needs to be compiled and put into a format that people can understand. That’s where the world of graphs and charts come in.
First off, there is no perfect graph to visualize data. Each has its own benefits and disadvantages. For example, a pie chart is great for showing percentiles of a group of data but terrible for displaying exact numbers. On the other hand, a bar graph is great for comparing data sets against each other but not good at showing trends. Knowing which charts fit which needs lets you better find the value in big data.
Having Experts Analyze The Data
So you’ve got a bunch of graphs and charts, but what does it all mean? People now need to find the value in the information being presented. Specific experts are great for different kinds of big data, so have the right people ready. For example, a psychologist could help decode big data sets of human patterns, while a mechanical engineer could help with efficiency data of machines in a factory.
It’s important to have a few different types of experts analyze data sets. There is a subjective nature to analyzing data, and analysts with different backgrounds might pull important insights from the same information. For example, a customer support specialist might look at consumer patterns on a site and see ways to improve a company’s reviews, while a marketer might see ways to improve sales.
Using The Data To Create A Test
In the scientific community, a single experiment isn’t considered fact. Other scientists perform the same test to corroborate the results. So if you perform a test to collect some data, don’t just blindly implement the results on a large scale.
Test your results on a smaller scale and see if you get positive results. Run potential changes by those it might impact to get better impressions of what will happen. If the change is to improve sales, run a focus group or collaborate with some current customers to make sure it’s on the right path. If a change will impact an employee’s workflow, make sure they believe they can implement it and it won’t interrupt something important. If people agree with what you plan to do, smaller tests can create data to measure how powerful your changes are.
Take Everything With A Grain Of Salt
Big data is an invaluable tool that should help guide businesses, science, healthcare, and more to greater heights. But remember to occasionally step back and look at the situation around the data. Get as much of the full story you can and see if the data truly can apply to your situation.
Do you have a question about how to better interpret big data? What are some flaws with the big data push that keep you wary of it? Let us know in the comments below!
