The definition of big data is quite simple – it’s huge amounts of information or data that are growing over time and can be analyzed and processed in a particular structured way. An example of this would be trade data from the New York Stock exchange, which generates a terabyte per day. You can’t expect this kind of information to be processed quickly by a human or even a team of humans. But this data can be used to trace purchases and sales, the growth and decline trends, make forecasts for the future. So special teams of data scientists, engineers, business analysts, and system architects are brought together to compile algorithms that go through data, visualize it, and deliver the result, which is then studied by business analysts who use it to make decisions that eliminate or minimize risks and guarantee future growth.

This kind of data, one that can be analyzed and mined for information is what’s called Big Data and it’s studied by data science. It can also be separated into three main types: Structured Data, Semi-Structured, and Unstructured.

Structured means that it’s all formatted properly and ready to be processed. Unstructured data is data of many different types and with no apparent structure.

To better understand the difference imagine that the first type is a document with a table while the second one is like a drawer full of documents, some relevant and some not. Semi-structured is like that same drawer except it has both documents laying around with no system and a few stacks of documents that have been structured properly.

These differences influence not only the tools and approaches for data processing, they also influence the way you store data. For example, the unstructured type requires NoSQL databases while structured data will be just fine in a more traditional SQL database.

While you can work with all three types, structured data makes it easier for analysts to draw conclusions while unstructured data is flexible and can be used for a variety of analyses. Basically, if you have a hundred receipts from one customer, that’s structured data and you can use it to determine their purchasing patterns. But if you have their receipts, their check-in information, their photos from social media, you’ll need to structure the data first, separate it into relevant categories second, and only then draw conclusions. However, with this extra information, you can also see which other brands your customers favor, which locations they visit and where they purchase your products.

Aside from this, Big Data also has several defining characteristics that set it apart from other types of data, so let’s take a look at those.

Main Characteristics of Big Data

Big data is all about three core v-parameters: volume, variety, velocity. You can guess what the first one means: big data implies that there are huge volumes of data available, so much that a human couldn’t possibly effectively process it all. Imagine a car factory, for example. It has hundreds of machines creating parts, putting them together, moving them from station to station. While an employee can, of course, see when something goes wrong, they can’t immediately spot what’s causing it or the numerous factors that lead to the error. Big Data analysis, however, traces the cause and effect, providing information that not even the most experienced worker can offer.

The variety part is pretty clear as well – data comes in many shapes and forms with varying sources, types, and purposes. This variety is what makes Big Data valuable – it delivers information that’s multi-purpose and can be useful to several aspects of your business development strategy. From gathering information on when people visit a store to what they purchase to which ads lure them in best, this is all things that Big Data does and does more efficiently than a human being.

Now, velocity is the last parameter and it’s a tricky one. Well, the explanation is clear – velocity means that Big Data comes in fast and nearly constantly. It’s harvested automatically and compiled at insane speed, piling up for you to analyze. This velocity is the core reason why processing Big Data isn’t an easy task. While you’d work one cluster, two more would appear.

There’s a fourth – hidden – v-parameter to this: value. All data is valuable, increasingly so. It’s the most important product in today’s world because it’s one that you can get anywhere and getting it is cheap. But the result of processing Big Data (any data, really) is invaluable to a business, more on that later.

What Industries and Companies use Big Data?

Let’s list a few examples: a seller could analyze their target audience’s buying habits and find out when it’s best to engage with them and through which channels. This makes it easier to do targeted marketing through emails and social media as well as offer discounts and special deals on goods.

For healthcare, Big Data analysis can literally save lives by providing information on the spread of epidemics, health data on patients (through biometric tracking in wearable gadgets), and responses in clinical trials. By analyzing medical Big Data, healthcare companies can help doctors prescribe drugs more effectively and tackle deadly diseases to prevent unnecessary loss of life. Big Data can help eliminate medical errors as well as trace patterns that lead to preventable diseases, all because it sees things that a human can’t.

In banking, Big Data analysis is essential for fraud prevention as it’s one of the few methods that can cheaply and efficiently go through the millions of operations that are done in banking systems daily. Automated protocols and simple interfaces make catching criminals easier and more reliable.

For particular company examples, look no further than the marketplace giant – Amazon. The company uses Big Data to make its advertising algorithms better and target customers more effectively. Big Data is being used by all the brands that you face everyday: Netflix, Spotify, McDonalds, all analyzing your preferences.

One of the more interesting cases is Starbucks, which is all over the United States, sometimes making it feel like the company has put locations on every corner. This bid to control the coffee market has paid off but how do they know whether it’s okay to open locations so close to one another? Yep, it’s all thanks to data science and high load applications processing that data. They determine the number of people visiting each location, how busy it gets, how much revenue it generates, etc. In the end, these data engineering techniques help decide whether it’s worth it to open another store nearby.

Why Do Business Choose Big Data?

Because you can use Big Data for anything, from better understanding your customers to predicting their preferences. You could be in healthcare, education, banking, or marketing – doesn’t matter. There’s a use for Big Data that will make your business soar.

Basically, using Big Data will guarantee you outpace the competitors that don’t use it because it’s a higher quality way of improving your services and it’s more comprehensive than anything else currently available. It’s the modern method of research and it’s the most effective one. Not even the smartest business guru can predict market trends and customer preferences as well as a data scientist and business analyst working on your Big Data. Implementation isn’t going to be cheap but the money you save in the long run by eliminating risks, anticipating the market changes, and protecting your business from human error.

You’ll need a team that can work on this data and it doesn’t even have to be an in-house one. You can outsource this task and another company will create a cloud computing algorithm to process your data and analyze it. This is exactly what SysGears does, helping companies in healthcare, financial, and education sectors getting the insights from their data. We provide data engineers and systems architects to process and analyze your data.

The Big Data Skills Most in Demand

For the past couple of years, one thing has been setting the Big Data circles abuzz — Scala. This relatively fresh programming language is the perfect match for data engineering because it allows making scalable and diverse applications, which fits the data science ethos perfectly. Scala is undoubtedly the future thanks to its immutability and focus on functional programming.

The reigning champion is Python, thanks to its use in text analytics and machine learning and artificial intelligence. Scala also has quite a bit to offer, such as good support for big data through the creation of high load systems. The language also gave us the Spark framework. Spark is used pretty widely in data science and it’s written in Scala. Plus, Scala Big Data and AI go hand in hand as they’re both used to improve algorithms and control predictive systems. They are essential for marketing in particular, which is one of the industries that uses Big Data most.

It’s also important to be familiar with data mining kits, which help extract data for later processing. You can either rely on premade industry staples like Apache Mahout or create your own. Either option is fine although the latter is, of course, more impressive if you succeed.

Any Big Data that’s going to be analyzed also needs to be stored somewhere, which is why SQL and NoSQL databases are vital in this field. While some are still using Oracle and other SQL databases, the newer generation of data engineers has been shifting toward distributed databases like MongoDB and Cassandra. The reason is their compatibility with newer and better instruments like Hadoop. Hadoop, by the way, also works with Spark, which leads you right back to Scala.

Last but not least, data visualization is important for presenting the results of your work. No algorithm is going to be good enough if what it puts out is only seen as a mess of information. Results need to be structured and visualized so having someone on the team who takes care of that is particularly important in business.

To give you an idea of how broad the skills and tools used in Big Data are, here’s a quick list:

• Programming-wise, Scala, Python, R, Java and C++ are the top dogs
• Data visualization requires D3, deck.gl, Tableau and other sleek and modern tools
• Distributed data storage utilizes NoSQL databases (Cassandra, MongoDB), Hadoop (HDFS), Cloud Providers’ services solutions (AWS, GCP) and more.
• Data scientists often use Apache Spark, Apache Storm, Apache Hadoop, etc

Conclusion

SysGears has experience with full-cycle development, from idea to finished project, in a multitude of industries, including Big Data projects in ecommerce and healthcare. We won’t reveal all the details in order to protect our clients’ business, but let’s just say that if you want an ecommerce aggregator with custom widgets and algorithms, you’ve come to the right place. Our team works with Scala, Play Framework, Java, and Spark every day with nearly a decade of experience behind our belts.

We’ve presented several examples of Big Data being a turning point toward success for different industries but it’s your success that matters most. If you’re still wondering how you can use Big Data, get in touch with us and we’ll provide a consultation. Or, if you have a project in mind and want to join the tens of businesses that we’ve helped with data engineering, let’s schedule a call.