How Artificial Intelligence is Impacting Big Data

The term Big Data has been around since 2005, but 90 percent of the data currently available was created in just the last few years. It makes sense that as technology grows, and more data is about to be captured and stored, the more we have to compile and analyze. In fact, people generate 2.5 quintillion bytes of data on a daily basis. Wading through this significantly large amount of data, when you’re feeling the pressure to make key business decisions for your organization, means you need analytics fast.

The importance of data continues to grow

Data is more abundant now that it has ever been before. According to Forbes, more than 150 zettabytes, or 150 trillion gigabytes, of data will require analysis by 2025. To meet this need, organizations need applications and security that can help then arrive at business decisions at a competitive speed. All of this data helps drive those decisions, so new ways to process this high volume of information are necessary.

Organizations are constantly looking to their data for hard evidence to support decisions that lead to improved business performance. To aid in this, they need platforms which can help accelerate analytics, business processes, and data processing. They want applications with predictive capabilities that can yield instant results. They also need a lot of storage. Addressing all of these needs requires forward-thinking and a lot of technological know-how.

Artificial Intelligence as a new disruptor

One option growing in popularity is incorporating artificial intelligence (AI) into the analysis process. AI can learn quickly and interpret data to your organization’s specifications. This allows it to not only draw conclusions but to continue to learn from those conclusions to analyze new information. The key element is data. AI can’t learn without it. Through this data, AI can recognize patterns which help facilitate future predictions. Organizations looking to adopt AI solutions now for their data analysis needs will most likely be ahead of the curve as Big Data consulting services continues to get bigger.

Breakthroughs in AI

Realizing the significant benefits to adding AI into existing applications for data processing, many companies are offering products that combine the technology with functionality related to analyzing data. Here are just a few examples:

IBM and Cloudera

IBM and Cloudera have partnered to improve big data and AI solutions with a go-to-market initiative across the open Apache Hadoop ecosystem. Partnerships like this are taking place to address those current issues many companies face today. With a need to interpret a large volume of data quickly and efficiently, advances like these are specifically aimed at managing Big Data workloads. This symbiotic relationship works across multi-cloud, edge architectures and hybrid on-premise setups. Enterprise Data Hub and Cloudera DataFlow will become available to IBM customers, while Cloudera will deploy IBM’s Watson Studio and BigSQL to its customers. This alliance provides customizable analytical and decision-making applications that can be deployed in a variety of setups, including hybrid cloud environments. Users will be able to take advantage of hybrid data architecture for quicker decision-making.

Amazon Web Services

Using pre-trained AI services, in addition to customized models at scale, Amazon Web Services (AWS) incorporates AI for the cloud. Whether using Amazon’s pre-set models or building, training, and deploying your own, this application helps you build datasets quickly and easily as more and more data comes into your organization. AWS offers secure cloud storage and uses managed databases to store information.

Microsoft Azure

Microsoft Azure uses AI to help discover latent insights within your data. Once you establish your own models, Azure goes to work on your information, extracting the distinct pieces that sometimes go unseen with such a high volume of content flowing through the system. As a cloud-computing platform, you have the ability to store all your data where it can easily be accessed from anywhere. Machine Learning then lets you build and deploy data analytics solutions to quickly process data on demand.

Big data will keep getting bigger

The more technologically advanced we get, the more data we’re able to capture, consume, and interpret. This information is what drives organizations’ decisions, so the faster results can be accessed, the better. Pairing analytical tools with technology that can make assessments quickly is the best way to get through the volumes of data being collected today. As the level of data continues to grow, disruptive tech like AI will help maintain our ability to stay on top of the information.

Ethan Millar is a Technical Writer at Aegis Softtech

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