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Get Ready to Take on 2017's Analytics Infrastructure Challenges

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Get Ready to Take on 2017's Analytics Infrastructure Challenges

Data analytics infrastructures need to become faster, smarter, and more robust than they have ever been before as AI, Machine Learning, and NLP continue to gain traction.

· Big Data Zone
Free Resource

Read about the story and evolution of the data warehouse, as well as the major challenges that these systems are currenly facing.  Brought to you in partnership with Panopoly.

For many organizations, their ability to collect data has long surpassed their ability to organize it quickly enough for analysis. As companies of all sizes are increasingly pressured to leverage Big Data to power business intelligence, data engineers are going to be pushed — and pushed hard — to make sure that business intelligence is built on the strong foundation. Data analytics infrastructures need to become faster, smarter, and more robust than ever before.

Building an analytics infrastructure capable of quickly incorporating large amounts of data from a wide range of sources in multiple and frequently incompatible formats is not an easy task. Fortunately, we’re going to have exciting new tools to help us out when it comes to analytics infrastructure challenges.

AI, Machine Learning, and Natural Language Processing

One of the most prominent trends that have recently emerged in order to deal with this almost limitless volume of data is the growing importance of AI, Machine Learning, and NLP for analytics.

NLP especially has high disruptive potential. As developed as Big Data analytics have become, our analytics infrastructures are still almost entirely geared towards managing structured information. Being able to accurately analyze increasing amounts of unstructured data is one of the significant benefits of merging NLP with data analysis.

These new technologies have significant implications for those responsible for designing, building, and managing analytics infrastructure when it comes to taking on new challenges.

Embracing Data Agility

John Schroeder, executive chairman and founder of MapR Technologies, predicts that data agility will become a key differentiating factor between successful and unsuccessful enterprises. He projects that over the course of 2017, both processing and analytical models will evolve and become adopted as “organizations realize data agility, the ability to understand data in context and take business action, is the source of competitive advantage not simply having a large data lake.”

In 2017, it is all about how fast you can extract value from the bottomless ocean of data as well as how quickly you can act based on the insights you derive. Machine Learning and NLP-powered analytics infrastructure tools such as Panoply.io are capable of connecting to all sorts of structured and semi-structured data sources — absorbing billions of writes daily without a line of code and allowing you to capture and process your data at lightning speed.

Growth of IoT

Pressure to integrate multiple streams of data coming in various formats from mobile, cloud, IoT, etc. into a useable manner is definitely going to keep us up at night.

The growth of the cloud and IoT means that there are exponentially more data sources that are incompatible with one another in terms of the data that they generate. Some projections place the number of IoT devices at 28.4 billion by the end of 2017, with each of those devices streaming live data that needs to be captured, analyzed, and readily available. Not an easy task.

Here, NLP with its ability to analyze unstructured information will come in handy as it will enable us to extract the important “bits” from multiple data formats and to seamlessly integrate this disparate data into the analytics infrastructure.

Automation of Data Prep

Most of the highly coveted Big Data is utterly useless. As Alex Woodie points out:

“Sure, platforms like Hadoop and Spark have removed technological tethers and turbo-charged our capability to store and process massive amounts of data, in an affordable and reliable manner… But without the capability to cleanse, transform, and standardize all that data at the scale required, all that power could be for naught.”

At present, we are witnessing the ongoing growth of the self-service data prep software market that is projected to reach $1 billion by 2019 with an adoption rate going up from the current rate of 5% to 10% by 2019. This trend clearly reflects the central interest of data professionals in automating mundane tasks related to data prep, which, reportedly, currently take up to 70% of their time.

The implications of AI and Machine Learning technologies to cleanse, transform, and standardize data in real-time are far reaching. Advances in Machine Learning and Deep Learning will enable organizations of various sizes to actually monetize their data and to use the insights to improve their business.

Essentially, organizations will be able to easily connect their data streams and get to work, while self-optimizing analytics infrastructures work the magic. For example, Panoply utilizes Machine Learning and Natural Language Processing (NLP) to learn, model and automate the standard data management activities, saving the data engineers, server developers, and data scientists countless hours of debugging and research.

As the rapid surge in data volumes is gaining momentum, the growing importance of AI, Machine Learning, and NLP for analytics and analytics infrastructures will continue to gain traction. Expect this extraordinary, disruptive growth to continue throughout 2017.

Read about the story and evolution of the data warehouse, as well as the major challenges that these systems are currenly facing.  Brought to you in partnership with Panopoly.

Topics:
big data ,artificial intelligence ,macine learning ,natural language processing

Published at DZone with permission of Yaniv Leven. See the original article here.

Opinions expressed by DZone contributors are their own.

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