Master Big Data by Taking These Little Steps

Big data is all the rage among businesses these days.

But how can you even begin understanding big data? Not to mention even using it!

That’s why we’re here, to help you get a hold on and master big data! We’ll take you through all the steps needed to get started with this essential business asset in no time!

From what data you need to how to organize and structure it all, we’re here to help.

So, without further ado, let’s dive in.

What is Big Data?

Big data is quite self-explanatory. Simply put, big data is a large volume of varying data a business uses to improve its services and day-to-day productivity.

Big data can come from any source you get normal data from, whether that’s an online survey you conduct with customers, or from manual reports that you have to write up for your business’ taxes.

Big data is everywhere. That’s why it’s so big!

Why Master Big Data?

But why should you master and use big data?

Like we said before, big data can help you increase your ventures productivity.

You should at least take advantage of big data in order to:

• Reduce needless costs spent by your business.
• Compare your business to other industry professionals.
• Track the improvement or decline of business throughout the year.

To do this though, you need to understand your company, it’s data needs, and types of data input.

Understand Your Use Case

Whatever the type of business you may be running, there’s a reason you should master big data. This is your use case,

The exact ins-and-outs of your use case will be fairly specific to your marketing goals and target audience. But never fear, there are a few questions you can ask yourself and your customers to see how to use big data well.

Your use case for big data may be large and complex, or it might just be simple improvements on already decent systems.

Whatever the case may be, though, you need to figure out what it is.

Finding Your Use Case

To identify your use case, ask yourself these types of questions:

1. Who is your most loyal segment of customers?
2. What parts of your business fall behind your competition?
3. Where do customers think you could improve in comparison to other businesses?
4. Why does your company waste certain resources?
5. When do customers start losing a clear concept of your business’ goals and service?

Of course, there are many more questions you can ask but this is just to give you a general idea.

Any questions you can’t answer, however, are questions that make up your use case for Big Data.

To master big data effectively, you need to know how you plan to the data you’ll gather. So take note of any unanswered questions, and see how big data can help you find the answers.

Define Your Big Data

Now that you know how you need to use your big data, you need to define what data you’re going to be using.

Making sure you and your staff clearly know what data to take record of, and what not to. It may seem obvious to you, but there may still be some confusion among employees.

So communicate clearly what data you’re looking for. Otherwise, you may get tangled in a “data hairball” which can be hard to get out of!

Data hairballs also make sorting through data more difficult, as you have an abundance of data you’ll probably never use.

Clearly communicating what data you want to keep a hold of makes organizing the data far easier in the long run.

Let’s look at an example of how you might figure out what data you need to gather.

Diagnosing Data

Imagine for a second that you’re a local medical practice you. You have patients come in and out, regularly interacting with staff and services in-person for their healthcare.

In this case, you don’t want to only be collecting data from your website and other digital sources!

You’d want more traditional data too, like customer interaction between staff and patients etc. So make sure your staff have a means of gathering and recording feedback from customers etc.

Figuring out how to do this without breaking legal confidentiality may be tricky. But tools like surveys, suggestion boxes, and other ways for patients to give feedback would be a good way to diagnose different in-person data.

A system like this is easily sorted through because it’s designed to only take the data that’s needed.

Whilst the implementation may be different for your use case, you should follow a similar pattern for gathering your data.

Big Data Structuring

Finally, to master big data you need a knowledge of the structure of the big data you receive.

There are many different data structuring systems out there for big data, but there are two types of data structures you should be aware of:

Unstructured Data:

Unstructured data is big data that usually comes from person-to-person interaction, like the medical example we looked at before.

It can usually consist of large amounts of text or written information, that takes a bit longer to sort.

Unstructured data is data that fits a general idea, but is hard to specifically categorize and has a lot of uniqueness to each piece.

A large collection of tweets under a hashtag, for example, is a collection of unstructured but organized data.

Multi-Structured Data:

Multi-Structured data is far easier to sort through. It is usually found via human-technology interaction.

The number of visitors to your facebook page and comment on your posts is a good analytic that represents multi-structured data.

Since this data is easily categorized into fine-segments, you can keep a track of it on a far more modular-based system. Thus the multi-prefix.

Knowing these two types of data structures is a good way to start organising and finding the data you need. This, in turn, will help you notice the patterns to improve on, as we mentioned earlier.

Storing This Data Away

So there you go, now you know how to take advantage of big data!

We hope this post has helped you. Our services are geared towards helping you in data-heavy and analytical areas. You can find out more here.

For now, though, we hope this data has been neatly sorted into your memory! Good luck data-diving.