Today, humans interact with many connected devices on a daily basis — from smartphones and laptops to TVs and even refrigerators. All of those devices generate an enormous amount of data, known as big data. Big data defies analysis through conventional statistical methods, and so a specialized field has emerged to address it.

Big data analysts, also known as data scientists, are experts who work with very large datasets — often consisting of millions of data entries. Working with big data is still a new profession that is continually being shaped and refined. One way to characterize big data is with the three “Vs” — veracity, velocity and volume.

Continue reading this big data career guide to learn all about veracity’s meaning in big data analytics.

Big data analysts are expected to have strong competencies in computer science and information technology, given that they work with complex databases and computer software. Some employers prefer that their big data analysts have computer programming knowledge. Because of this, it’s often preferable for students to earn a computer science degree, rather than a degree in statistics.

The most ideal degree is a computer science program that offers a specialization in big data analytics. This type of degree would give you foundational knowledge in both worlds—computer science and data science. You’ll learn the principles of computer software design, algorithm assessment and statistics.

You can begin working toward an exciting, in-demand career in big data analytics when you earn a computer science degree at Grand Canyon University. The Bachelor of Science in Computer Science with an Emphasis in Big Data Analytics degree program follows a 21st-century curriculum to teach crucial skills in statistical analysis, computer programming and system architecture, among other areas. 

Click on Request Info at the top of your screen to begin planning your future at GCU.

Organizations are increasingly relying on big data analytics to make important decisions. It’s critical that analyses are based on accurate, high-quality data. If a company makes a major decision based on inaccurate data, the results can be costly.

For example, consider a company that manufactures products for babies and young children. The company notices that there have been thousands of complaints on social media about its new crib. These complaints state that the crib is prone to sudden collapse.

The company’s leadership is horrified at the thought of its products potentially causing injuries to babies, and so it prepares to issue an urgent recall notice. Let's say the aforementioned Gretchen then takes a closer look at these thousands of social media complaints. She finds clues that tell her the complaint dataset actually stems from questionable sources.

In other words, the complaints were generated by “robot” social media accounts and are not accurate—no cribs have actually collapsed. Gretchen saves the company from issuing a costly and unnecessary recall notice, and allows them to instead launch a public relations campaign informing consumers of the mix-up.

Veracity in big data can also directly affect individuals—not just companies. For example, consider the software needed to operate an autonomous vehicle. Autonomous vehicles are equipped with high-tech sensors to detect obstacles and prevent potential collisions. However, the sensors can only receive data; the software is needed to interpret whether something is actually a collision threat.

There is a significant difference between an overturned big rig on the road ahead and a plastic bag being blown across the road by the wind. The more accurate the dataset interpreted by the software is, the better the machine will work.

If you ask a data scientist which of the Vs is most important, you’re likely to hear the answer, “Veracity, of course!” Veracity in big data refers to the accuracy of the data. Veracity’s meaning also encompasses the following three traits of data:

1. precision
2. trustworthiness
3. reliability of the data

For some data scientists, veracity’s meaning also extends to its relevance. A dataset may have millions of entries, but not all of them are necessarily relevant for the analyst’s particular objectives. Non-relevant data are often referred to as “noise” or “white noise” because they can be safely ignored without compromising the results of the analysis.

As a hypothetical example, let’s say that data scientist Gretchen is analyzing 10 million rows of travel-related data. Her goal is to develop a statistical model that predicts airfare prices to enable travelers to easily find the lowest rates for their trips.

However, about one million rows of data pertain to hotel prices. Since hotel prices aren’t germane to Gretchen’s project, she gets rid of them. She also notices some discrepancies that call into question the accuracy of two million additional rows of data, and so she eliminates those from her dataset as well.

Thanks to Gretchen’s savvy analysis, she’s able to build a statistical model with a high degree of precision and accuracy.

If you’re still in high school and you already know that you want to be a data scientist or big data analyst, you should meet with your school guidance counselor. Try to add more courses in mathematics, especially statistics. You should also take classes in computer science, if available.

After high school, you’ll need to earn at least a bachelor’s degree. A Bachelor of Science (BS) degree is typically sufficient to begin working in the field. However, some employers do prefer that their higher-level job candidates possess a master’s degree in data science, and so you may decide to return to school after gaining some experience in the field. 

Once you have a bachelor’s degree, you can begin looking for your first job in the industry. You might need to work for a few years in an entry-level position, such as statistical technician or assistant. After you’ve proven your capabilities, you may be eligible for a promotion to the position of big data analyst or data scientist.

Now that you understand veracity’s meaning and importance, you may be wondering exactly how data scientists determine veracity in big data. Data scientists look at several factors to establish whether or not to trust a particular dataset.

1. One of the first steps a data scientist will take is to check the source of the dataset. Data acquired from universities, scientific organizations and governmental agencies are generally considered trustworthy and accurate. For example, data acquired from the National Oceanic and Atmospheric Administration (NOAA) will be trustworthy, while data acquired from a website entitled “Crazy Jake’s Wacky Weather Predictions” will not be trustworthy because the source is questionable.
2. Data scientists can also review the metadata—the hidden information that typically accompanies datasets. A dataset’s metadata should include information such as date published, variables observed, temporal coverage, resolution and dataset creator. If the metadata is incomplete or missing entirely, it calls into question the veracity of the dataset.
3. A third way of establishing the veracity of big data is to do a comparison of the descriptive statistics, or the statistics used to summarize the dataset. A dataset’s mean, medium, maximum and minimum values by themselves won’t allow you to determine the veracity.

However, by comparing these descriptive statistics to others of similar datasets, you can determine whether any major discrepancies exist. If so, this calls into question the veracity of the data. The dataset may need to be discarded if the discrepancies cannot be explained.