Data Analysis, Data Science, Data Crunching, Freelancing?

Mm… Now this is an interesting topic. What do Data Analysis, Data Science, Data Crunching and Freelancers all have in common? Have you ever heard of the term “Big Data”? Are there freelancers out there

who understand what data is and how to analyse big data? What is data analysis in the first place? How about some definitions to start with. If we have an understanding of what Data Analysis is then we can decide whether there might just possibly be freelance data scientist out there. I’ll let you into a little secret…yes, there are freelance data scientists out there and they are very good at what they do.

Now for the definitions…

• Big Data is just a “big” word for large amounts of data that need to be processed to provide the owner of the data with information. Global corporations often have massive amounts of data that they need to process in such a way to return information which will assist management in decisions on the way forward etc.
• Data Science is the way data is processed. The end goal is to extract information from the large amounts of data. For those who don’t know, there is a difference between data and information. Simply put, data is what is inputted into a knowledge base or database and information is what comes out after the data has been processed.
• Data Crunching is the actual method of sorting through the data. The term “Data Crunching” sounds like a computer is going to chew the data into small chunks and spit the bits out. Well, in a way, that’s exactly what data crunching is. The data is sorted into categories and subcategories as requested by the data analyst.
• Data Analysis is the actual process of converting the data into meaningful information. The data analyst is a specialist who is experienced in providing the rules of how the data needs to be modeled or analysed.

Wow! There are some big words in these definitions. Let me simplify it all in a sentence or two.

We currently live in a day and age where everything has become digital and global corporations collect large amounts of potentially meaningless data. For example, a clothing retailer would like to know how long it takes for a cashier to process a customer transaction from start to end. How many items did the customer purchase? Were there any issues that caused the customer to stand at the cashier’s desk for longer than necessary? The easiest way to do this is to collect the number of times a cashier scans an item of clothing for each customer transaction from start to finish. Processes are put in place to collect all of this data. What now? Unless the data is interpreted in a meaningful way, the data is useless. If I were the clothing retailer, I would want to know how many customer transactions a cashier can process per hour. Time is money, right? If I can get my staff to process more customers in an hour then I make more money. A data analyst will create models from the data and turn it into meaningful information.

Where about freelancers? Do freelancers make good data analysts? Am I better off employing a full-time data analyst or rather contracting a freelancer on a project basis? In my honest opinion, it is better to hire a freelancer on a project basis than it is to employ a full-time data analyst. Why, you ask? Well, as we have mentioned before, freelancers are hardworking, independent professionals. They are guaranteed to provide a quality service as their reputation is on the line. They are also self-employed so there are no company benefits. A freelancer needs to get as much work done in as short a time as possible. It is the only way he can earn a living.

So, in conclusion….hire a freelancer for your next big data-crunching exercise. You won’t be sorry.