5 Revolutionary Big Data Trends in Data Science


It has increasingly become commonplace for companies of every size to hold massive quantities of data. Further, the resulting datasets often exceed the memory of a single machine. This change in the value placed on even the most seemingly trivial data forces data scientists and data engineers to study it in a different paradigm and with a new set of tools.

What Is Big Data?

In an attempt to encapsulate what “big data” is in data science, we can best define it as the three Vs: volume, velocity and variety. 

Volume is perhaps the most well-known of these. Big data, by its very name, is often large and cumbersome. Social media platforms such as Facebook and Twitter are constantly taking in millions of data points on individuals' likes and dislikes, trying to target the right friendships and right advertisements to keep their attention. Big data often arrives as high volume, unstructured and pre-processed information. 

Velocity is the speed in which data is being received. For many online platforms from healthcare to banking to eCommerce, this stream of data is often collected in real-time and at a rapid clip. 

Finally, the third V of big data refers to the sheer variety of data that is being constantly collected. As an example, text, audio, video and image data are all something we come across on a daily basis. This data doesn’t exactly play well together and does not fit into a neat database like traditional data might have. 

Against all three factors at once, analyzing big data can quickly become a daunting task for a data scientist. Luckily, there are new trends popping up that have made big data processing much more manageable and efficient. 

5 Big Data Trends

Against all three factors aforementioned, analyzing big data can quickly become a daunting task for a data scientist. Luckily, there are new trends popping up that have made big data processing much more manageable and efficient.

1. Edge Computing

Data is collected when users have an experience. Whether that is when your watch records a jog, paying a bill from your phone, or simply tracking the amount of hours you spend scrolling social media, most data comes in messy and unstructured because we as a species are pretty messy and unstructured. Edge computing shifts the storage and processing load of that data from the large network to the individual device. This may sound like it would slow your phone down, but it actually reduces latency because it means that there is no main entity trying to process everyone’s jogs/bills/scrolls at the same time. This makes edge computing a win-win for both users and companies.

2. Hybrid Cloud Computing

Highly-regulated industries such as government, finance and healthcare are becoming more cost-effective by making use of a hybrid cloud, keeping sensitive information safe while enjoying the benefits of third-party cloud computing. A hybrid cloud seeks to optimize on the three Vs of big data, despite being distributed between public and private servers. A study by RightScale found that 94 percent of technical professionals surveyed used cloud computing in some capacity, with 69 percent opting for a hybrid cloud.

3. Data Lakes

In order to manage the exponential increase in data collection worldwide, companies are meeting the demand with data lakes. Data lakes, an alternative to data warehouses, are rising in popularity due to the fact that they can store large quantities of disjointed, raw data. Whereas data warehouses hold structured data that might need processing in order to “fit,” a data lake can hold both unstructured and structured data simultaneously without issue. This can be a solid alternative when a company has exponentially increased data collection but doesn’t need to process everything that is collected. 

4. Machine Learning

Companies and researchers are constantly working to upgrade current machine learning and AI technologies in order to meet the needs of big data. Natural language processing, for example, can be used to improve the online conversation experience with potential customers in real time. There are many open source libraries (such as Spark and Hadoop) that are specifically designed to efficiently process and model big data with machine learning. These tools are allowing companies to detect anomalies, predict future sales and graph trends using data in the petabytes.

Predictive analytics and machine learning are key components of Instnt’s technology to prevent fraud. See how it works.

5. Artificial Intelligence (AI) Technologies

More than just a buzzword, AI is reshaping the way we look for answers. AI is what can make a chat bot speak intelligently, recommend products you didn’t realize you needed and make predictions that better the best machine learning models. While one drawback could be that AI-driven results can be harder to decipher than a machine learning model, the continued adoption of AI in business has done well to increase trust among a non-technical audience. AI does best when analyzed at a large scale due to its need to learn from the past, which combined with the volume and velocity of big data makes the two a match made in heaven. 

Instnt leverages AI to monitor fraud risk and ensure compliance with geographic location, velocity checking and user behavior.

Future of Big Data

So, is big data just a fad or here to stay? There are a number of reasons to believe in the latter because: 

Companies Are Becoming More Data-Driven

Thanks to the effectiveness of machine learning and AI for prediction, it’s likely that these tools will continue to be used by companies for years to come. Similarly, the best way to train a prediction algorithm is with data, and lots of it — for neural networks to be effective at all, they require significant data input. As tools advance, so do we — and data to machine learning is like gasoline to a car. 

Customers Are Becoming More Diverse

As countries around the globe become richer, companies are seeking to reach new customers on a global scale. In order to understand how and who to reach, data is again a crucial component. It can be very costly to enter a new market (from shipping or securing distributors to navigating land ownership rights and marketing to a new audience and culture). The more data gathered, the more pertinent information distilled — thereby advancing the quality of decision-making. This trend towards the global has been happening for decades and is unlikely to wane  anytime soon.

Cloud Services Provide (Essentially) Limitless Computational Power

It is one thing to own big data, but it can take great computational power to truly benefit from it. With the influx of cloud services — AWS, Microsoft Azure, Google Cloud, IBM and Oracle, to name a few —   businesses have access to more servers and faster GPUs than ever before. With essentially unlimited computational power, businesses of all shapes and sizes can mobilize big data to train predictive models at reasonable rates. 

Big Data Trends in Data Science

Making predictions with big data, open source applications and what we can expect of big data going forward. If you are looking to ensure against fraud and incorporate compliance checks in customer data, big and small, Instnt technology is a simple copy and paste to include advanced neural networks that protect you and your new customers. In fact, we trust our system so much that we’ll even indemnify you for up to $100M in aggregate annual fraud losses. There’s truly nothing to lose by considering Instnt.


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Instnt's fraud loss insurance platform offers comprehensive protection for businesses for the entire customer lifecycle, from account initiation, and onboarding to subsequent logins, transaction processing, and the broadened accessibility of additional products and services.