Data, in whatever form it takes, is on the forefront of most business plans today. If you are the one responsible of making business decisions based on data, you are going to want to make sure you know how to analyze the information as quickly as possible. Take an e-commerce company for example. Your employees aren’t the only ones contributing to your data. Your customers submit data of their own every time they sign up for your service or purchase a product from you. Overall, the numbers from both sides add up quickly and it often takes a significant amount of effort to analyze. The progression of data creation is exponential, with colossal amounts of data generated every day.

Simply put, Big Data is comprised of extremely large amount of information. This definition is important for business owners to understand, but understanding the scope of Big Data isn’t the be-all or end-all. Business owners need to learn about how Big Data can be analyzed for insights that lead to more strategic business decisions and moves.

Below, you’ll find information that will help you make sense of Big Data. After you finish this article, you’ll understand how data is collected, the types of Big Data and what your business can learn from this this data.

The Collection of Big Data

Data collection methods often differ from organization to organization. Some industries and organizations’ Big Data encompasses information on transactions, while others is comprised of enterprise content. What is more standardized across industries, however, are the steps of data collection.

data collectionThe first step of Big Data collection is gathering information. Some companies use web scraping tools to gather their data, and others rely on their customer resource management tools to capture information. Next, companies need to store the data they collect. Many companies build internal automated processes that allow them to store their data in spreadsheets. Others might take advantage of a storing service that saves the information for them.

The third step is data organization. Even if an organization collects data efficiently, they’re likely to collect extraneous information they don’t need, too. So, every organization needs to sort and clean the information they collect and save. A company will likely also have to reorganize their data after it’s clean, so it’s optimized for further use. Last—but not least—companies need to verify their data. Until companies validate the authenticity of their data, they cannot trust any insights the information produces.

The Types of Big Data

Big Data is made up of a mix of unstructured, structured and multi-structured data. Unstructured data is information that’s not organized or easily interpreted by traditional techniques. A great example of unstructured data is a social media post. In general, standard databases and data models are unable to organize and understand this type of metadata.

structured and unstructured dataStructured data almost always has a defined length and format. Numbers, dates and strings of words are a few examples of structured data. Chances are your company already uses structured data that’s stored in a database to inform your business decisions.

Multi-structured data is derived from interactions between people and machines. One of the best ways to remember multi-structured data is to think of a web browser. As a user works on the browser, a combination of text and visual data is chronicled; the browser will also log structured data, like transactional information, about the user.

Understanding Data Improves Business

The amount of insight businesses can gain from Big Data is somewhat overwhelming. Due to this fact, experts suggest that companies focus on what they want to learn from Big Data, not what they can learn. To take advantage of all that Big Data has to offer, you need to establish a clear plan.

Several prominent companies use Big Data to decrease their expenses, and others use Big Data to improve their internal processes. One of the most popular processes right now is to use Big Data to reduce customer churn.

The Four V’s of Big Data

Industry leaders often use “The Four V’s of Big Data” to frame the Big Data discussion. If you need a quick way to remember what Big Data is and how its massive amounts of data are used, think of the following words—volume, velocity, variety and veracity.

The most obvious characteristic of Big Data is its volume. The amount information taken into consideration for business decisions also grows every year, making volume an essential component of Big Data. With an exponential growth model, Big Data’s velocity must also be addressed. Remember, everything from a text message to a credit card swipe can (and most often is) considered part of the Big Data collection process. As more technological advances become established, Big Data’s velocity will only continue to increase.

Variety is another important characteristic of Big Data. When you think of Big Data’s variety, remember that Big Data is comprised of unstructured, structured and multi-structured data. As discussed in “The Collection of Big Data” section, veracity is another part of understanding Big Data. Without prior data verification, you can’t draw valid insights.

ibm-big-dataThe Bottom Line

To use Big Data as effectively as possible, companies need to understand the ultimate value that Big Data offers their operations. More specifically, businesses leaders need to understand how seemingly countless attributes influence their data collection objects.

Screen-Shot-2015-09-28-at-2.05.01-PMThe high elevation view of Big Data can be overwhelming, but it’s pivotal to business success. Currently, companies that aren’t using Big Data to their advantage are stuck in the past. They’re scrolling through countless spreadsheets and data sources trying to make sense of everything. Then, they’re compiling analysis reports that take either months or years to create. By the time these groups are able to make business decisions, their data is often outdated and irrelevant.

On the other hand, companies that use Big Data are ditching the troubles and limitations of traditional business insight creation. These industry leaders use Big Data’s real-time cycle of analysis to make the most informed business decisions possible when they matter most.

If you want to run your company as efficiently as possible and improve your bottom line accordingly, you need to understand your data.