Big data is big business.
And for good reason.
As Harvard Business Review recently reported, an exhaustive study of 330 North American companies led by the MIT Center for Digital Business in conjunction with McKinsey’s Business Technology Office revealed that the use of data in business decisions like product development, hiring and firing, as well as marketing and sales has huge, bottom-line implications:
The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results.
In particular, companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.
Why?
Simply put … because data “outperform[s] human intuition in a wide variety of circumstances.”
The trouble with big data is that the vast majority of it doesn’t come in easy-to-analyze, numerically-based, gift-wrapped form.
In fact, according to IBM, of the 2.5 exabytes of data generated every day as of 2012 “about 75% … is unstructured, coming from sources such as text, voice and video.”
That’s why to extract data from websites you must develop a machine-based method of transforming unstructured data into business insights.
To help you with that, we have outlined five steps you can use to ensure that the process of collecting and leveraging unstructured big data is not only fast but also saves your organization money.
Before you dive in, it is important to set your objectives and get priorities straight. Decide what you are looking for and what’s really important for your organization. This will help you stick on a defined course so you save on resources by collecting the most relevant data for your company.
So here is how you do it:
1. Define the Source
You can only imagine the volume of data out there.
The good news is … you don’t have to go after all of it. Instead, you only need a specific type of content. Therefore, the first step into leveraging big data is defining the type of online content you want to extract data from.
For instance, you might decide to collect data from news articles, blog posts, customer reviews, forums, case studies, guides, or whitepapers, videos, or infographics.
The point is to pick the combination of these sources that best suits your data requirements.
Naturally, the source you choose will depend on your specific objectives and the topic you chose earlier on to guide you. Case in point, if you’re interested in learning about a competitor’s product in order to improve your own, then product’s specific review sites and relevant forums should be your go-to resources before you consider others.
The more sources you identify the more it will cost you time, money, and possibly get your crawler blocked. This post explains in depth how you can tell which resources to crawl and which ones not to.
2. Define the Data Type
The second step is to define the type of data you want to extract and give structure to out of all the unstructured data available.
Are you looking for comments on a blog post? Do you want the contents of that blog post too? Do you want customer reviews and ratings? Are you interested in price and feature comparisons? Or do you want to extract popular keywords and social sentiment behind the numbers in a host of news articles?
How does the type of data you choose align with your topic and set objectives? Avoid any data type that does not match with your objectives at the time.
Remember, choosing a source of data is one thing, but choosing the type of data you want to extract from that source is another. Be sure not to skip this step by assuming it’s covered in defining the source. It’s not. Getting detailed about the type of data ensures relevance and it’s a step towards attaining the most detailed results.
3. Unify & Aggregate the Data
The data you acquire from the different relevant sources – though similar – will not be entirely the same. Now that you’ve begun collecting it it’s under one digital roof, you’ll want to organize it in a specific order. Therefore, you will need to set specific standards and organize this data accordingly.
For example, different time formats from different pieces of data should be set to a specific time format – one familiar with your organization. Similarly, check all the review scores from different pieces of data and normalize them to a fixed scale.
Making unstructured data uniform and organizing with a specific format according to set scales and standards not only shows off your organizational skills … but also improves the data accessibility in order to make use of it.
4. Define the Frequency, Depth and Versatility
The web is a dynamic place. Everything changes fast, data included.
However, as much as you need to be on the look-out, it’s not necessary to react every time a letter or the wording on a post changes.
It is equally important to keep track of major industry changes and shifts.
This means you’ll need to set the frequency at which you want the acquired data to be updated.
Depending on your company’s needs, striking a balance for the optimal update frequency ensures that (1) you don’t miss out on any crucial information and that (2) data collection isn’t all you do.
Alongside of frequency, it is important to define the depth and versatility of the data. For simplicity’s sake — especially if you’re just getting started — you may want to make it shallow but from many different sources or deep but from just a few sources.
In the end, the deeper the data you collect the better, but depending on your business type and the industry, your set objectives will help you decide the best mix.
5. Choose How You Want to Consume the Data
This final step could just as well been the first.
To ensure you don’t end up on the list of those who think big data is just a fad, miss out on great opportunities, or even worse go through all the previous steps for nothing, you must have a solid plan for consumption.
The most common use of big data is to understand and target customers, meaning their preferences, emotional states, and buying behaviors. These facts help you engage with and sell to them more easily.
The other key use of big data optimizing your internal business processes. Unstructured big data helps you understand the process other organization are using and then guides you into how you can apply those lessons to yourself.
There are many more general uses of big data which you can apply to your own organization. The most important thing, however, is that you put in place a disciplined and structured approach not just to collecting data but putting it to work.
Always ask yourself: “What do I want to achieve? How will I make regular use of the data I collect?”
In Conclusion
Extracting insights from unstructured data can be daunting, but it doesn’t have to be impossible or even difficult.
By having the correct goals and objectives firmly in place as well as a well-defined process to guide you, you can not only extract but also make maximum use of big data.
If you need help, Webz.io offers both custom web crawlers along with plug-and-play options to help you scrape the Internet and obtain insights from unstructured data. Download this PDF to see how our solution compares to you building your own in terms of cost: time and money.
