What Are the Characteristics of Big Data?

You’ve almost certainly heard of big data. People often use specialized big data platforms to draw conclusions from huge amounts of information and make smarter decisions. There are some characteristics of big data you should know to expand your understanding of it.

The 5 V’s Offer Some Foundational Characteristics of Big Data

People often bring up the five V’s when they talk about how to define big data. They are:

• Volume:Big data involves handling a substantial amount of information, so the overall volume is sizeable.
• Variety: Data can be well-structured or have no structure at all. It also comes from numerous sources ranging from social media feeds to databases containing purchase information. Even if the information comes from dozens of sources, a big data platform can handle it.
• Velocity: This V describes the high speed accumulation of data. The information comes in quickly, and big data platforms must process it speedily so that users can get the most up-to-date insights.
• Veracity: This entry on the list relates to inconsistencies in data, all of which can cause some uncertainty in the results. People who use big data well know how crucial it is to minimize errors and other issues. They usually develop principles for handling the data before processing it.
• Value: This V is for the benefits that a company can enjoy if they have well-developed plans for how to harness the power of big data to their advantage.  For example, the right applications of it could make the world betterby addressing matters like homelessness or pollution.

The five V’s are commonly brought up in big data discussions. However, some people go even further and add a sixth V: Validity. It assesses whether the data is correct and accurate based on its intended use.

Data Structure Can Impact the Characteristics of Big Data

When explaining Variety on the list above, we mentioned that data has varying degrees of structure. More specifically, big data practitioners generally classify it into three groups: Structured, semi-structured and unstructured.

Structured data can follow a predefined format that someone creates. It usually enables putting the data into rows and columns or inputting it into fields. For example, if you use Excel to make a customer list that includes a person’s name, address, phone number and sales region, you’re making the data have structure. Structured data also lets you group related data together. You might combine people who are females and have purchased more than once.

Unstructured data is far more common in today’s world. It doesn’t fall into a defined format that supports storage in a relational database and may be much harder to search through and organize as a result. Emails, photographs and audio files are some examples of unstructured data.

Finally, there is semi-structured data. It has some identifying elements that allow organizing it. But, those aspects are not as well-defined as what a relational database requires.

The amount of structure that information has can affect some characteristics of big data. For example, unstructured data may not provide as much value as structured data would.

Also, Igneous published a 2018 study concluding that volume and velocity can be problematic, too. That’s mainly when organizations have too much unstructured data. It found that 81% of respondents manage at least one billion files and objects. It’s understandable, then, why they may struggle to keep up with the sheer amount of data they have and the speed at which new information arrives.

Some Industries Deal With Big Data Challenges

Big data offers enormous potential for giving organizations insights they may not have otherwise had and delivering them rapidly. However, obstacles exist when some industries try to work with big data platforms. For example, in health care, the difficulties range from eroded privacy to system incompatibility.

Similarly, in marketing, the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) define how marketers must treat consumer data. So, marketing professionals must familiarize themselves with any applicable laws and how those could dictate how they use big data platforms. Some analysts think privacy laws could slow down big data projects or at least change how people work with information.

When companies have information collected over years or decades, it could present ample opportunities for decision-makers to use that data and feel more confident about the actions they take. However, large amounts of data can also tempt hackers, especially if it contains personal or financial details. Banks and retail stores are two entities that use big data but must do so carefully.

Any industry that uses big data to assess the information of value to cybercriminals must make cybersecurity a constant priority. So, another characteristic of big data is that it must stay protected. If the public hears about companies misusing data, they likely won’t trust them and may take their business elsewhere.

Big Data Will Shape the Future

Now that you know the characteristics of big data, it should be easier to ponder why people might use the technology and what benefits they could get.

Keep in mind that big data will undoubtedly play a role in future success for industries and individuals, but only if people understand how to use big data thoughtfully and while prioritizing security.