Unleashing the Power of Big Data

A massive transformation is taking place in business, as companies across all industries are leveraging technologies like big data analytics, artificial intelligence (AI), machine learning (ML), and the internet of things (IoT) to streamline operations, boost productivity, and drive growth.

Yet, despite the rising demand for data and automation, many organizations are falling behind in their digital efforts and risk being displaced by competitors. In one surprising study, 72 percent of leading corporations said they have yet to forge a data culture, while 69 percent said they haven’t created a data-driven organization. And 52 percent reported they are not competing on data analytics.

As such, there is a clear and present opportunity at hand for agents to act as advisors for customers, helping them navigate the changing technology landscape and form strategic roadmaps for collecting, processing, and using data analytics.

To capitalize on this opportunity, agents must have a clear understanding of how technologies like big data, AI and ML, and the IoT can come together and benefit customers.

Let’s take a closer look

First Things First: What is Big Data?

Big data is a term used to describe large volumes of information sourced from transactions, machines, social media, etc. Big data can uncover useful patterns and trends to guide product development and to understand changing market conditions and consumer needs.

The thing to keep in mind about big data is that it’s massive — almost beyond comprehension. After all, data is being generated practically everywhere you look these days as more connected devices are coming to market. As IDC SVP David Reinsel explained, the global datasphere will grow by 61 percent by 2025, reaching 175 zettabytes. To put this in perspective, if you were to put 175 zettabytes on a stack of Blu-ray disks, you’d reach the moon 23 times.

Of course, data collection is not a new concept, as companies have been doing it for decades. However, organizations cannot still process extremely large data sets. In fact, most data that’s collected goes to waste. This is because data is often unstructured, in the form of images and videos, and therefore very hard to convert into useful information. In fact, 95 percent of businesses cite the need to manage unstructured data as a problem.

What’s more, most data has a shelf life and needs to be processed rapidly, which is easier said than done.

How Businesses Are Managing Big Data

Recent advancements in AI and ML make it easier to process large data sets, enabling companies to pull data from a variety of different disparate sources, so that they can be used to make informed recommendations in near real-time. For example, rideshare apps like Uber and Lyft rely on AI and ML models to process data and deliver instantaneous updates when matching drivers with passengers and optimizing routes. In short, these tools enable companies to take raw data, analyze it, and maximize its full value.

The main challenges that businesses are still facing when deploying AI and ML are that they are costly, complicated, and time-consuming to deploy at scale. They also require the expertise of dedicated data scientists, which can be difficult to obtain. For this reason, true AI and ML development are usually exclusive to well-funded enterprises and impractical for smaller organizations.

To overcome these challenges, many small to medium-sized enterprises choose to leverage platforms that contain third party ML and AI functions. This approach is much easier and more cost-effective than building in-house data science teams. We’re also seeing more platforms coming to market that merge AI and ML with useful technologies like robotic process automation (RPA) and optical character recognition (OCR), which enable businesses to process data and automate more efficiently.

Big Data and the IoT

At this point, you’re probably wondering how the IoT fits into the picture.

The internet of things (IoT) refers to the growing ecosystem of connected objects that can communicate with one another over a network. The IoT ranges from smart household devices like lightbulbs and environmental systems to manufacturing sensors and security cameras.

The IoT has been growing for the better part of the last decade. At the end of 2019, there were about 7.6 billion active IoT devices. By 2030, the IoT is on track to exceed 24.1 billion connected devices and generate $1.5 trillion in annual revenue.

For most businesses, the true value of the IoT is its ability to generate raw data for analysis in AI, ML, data mining, and discovery-oriented analytics projects. For example, manufacturing facilities can use sensors to study productivity and output over time or make inventory predictions. In another example, IoT sensors can collect highly granular weather data for more detailed and accurate forecasting.

It should be noted that many companies are now using IoT devices in conjunction with data processing tools like the open-source Apache Flink platform to enable data streaming, with low latency and high fault tolerance. Data streaming, or event stream processing, involves having a continuous flow of data for real-time processing. For example, AWS uses Apache Flink to power its Java application. And Capital One uses Flink for real-time activity monitoring and alerting.

Telarus has a wealth of experience, support, and partners, making it easy to help customers get the most out of their data and analytics initiatives.

To learn more, contact Telarus today.