What is the future of analytics?

6 trends shaping the future of data analytics

Organizations are demanding more from their data analytics efforts, wanting immediate insights that will help drive business decisions. In response, many are adopting new technologies such as machine learning, deep learning and natural language processing. Eric Mizell, vice president of global solution engineering at Kinetica, discusses what role these technologies will plan in shaping the future of data analytics.

The algorithmic economy comes of age

“Organizations are dealing with a tsunami of data,” Mizell stresses. “The speed, size, shape of data generated by newer sources such as sensors, mobile apps, social media, machine logs, and connected devices far outpaces the ability of current systems and humans to comprehend, draw insights, and act on data. Organizations should look at algorithmic approaches such as machine learning, deep learning, and natural language processing (NLP) to automate insight discovery at scale.” 

Data and analytics architectures evolve

“Data and analytics architectures must evolve for the hybrid world,” Mizell says. “Cloud and on-premise, data in motion and at rest, transactional and analytic databases, in-memory and spinning disk, real-time and batch, AI and BI – all need to co-exist and interoperate. Organizations must look to bring together workload-specific, complementary analytic solutions to analyze all data, gain insights, and act. They must look at open, standard-based solutions that use APIs, micro-services, programming languages, and connectivity to seamlessly integrate with existing infrastructure and deliver business value while preserving existing investments.”

Need for speed

“From high-speed Internet to 5G networks to high-speed trading, ‘speed’ is a critical element for business success,” Mizell confirms. “Customers demand instant gratification and enterprises need fresh data and real-time insights to deliver business value. Gone are the days of nightly batch processing and waiting for hours or days to get answers to critical business questions. Technology executives need to build real-time analytic pipelines to simultaneously ingest, analyze, visualize, and act on data in motion and at rest and deliver fresh, timely insights to capitalize on fast-moving business opportunities.”

GPUs vs. CPUs

“CPUs have been the workhorse of business applications for decades,” Mizell says. “However, big data’s volume, variety, and velocity–coupled with shrinking insight shelf life–require organizations to investigate other technologies to address the compute bottleneck. GPUs, with thousands of processing cores per chip vs. 16 to 32 for CPUs, have emerged as the “go to” alternative to process complex data at scale. Organizations must investigate GPU-based analytic technologies that deliver performance, flexibility, and ease-of-use to modernize the analytic infrastructure.”

From control to collaboration

“Data and analytics must be pervasively available across the organization for maximum business value,” Mizell stresses. “Everyone in an organization– data scientists, business analysts, and business users regardless of their technical skills–must have fast, easy, and self-service access to data and analytics for data-insight-driven decision making. Organizations need to adopt analytic technologies that democratize analytics, data science, and machine learning to establish a data-insight-driven culture. Analytic technologies must be flexible to balance analytic innovation with guard rails of security, scalability, and availability.”

The impact of the Internet of Things

“The Internet of Things (IoT) will fundamentally transform how organizations do data and analytics,” Mizell says. “With the nexus of people, devices, and data, IoT will have a profound impact on every industry and every line of business. Organizations will have to figure out how to sense, interpret, and respond to data in motion and rest, in real-time and at scale. Organizations must evolve their data and analytics architectures to seamlessly ingest the tsunami of IoT data, combine it with data at rest for contextual insights, and act in real-time and at scale to maximize business value. They must look at analytic solutions that deliver exponential scale and flexibility to manage the IoT data cost-effectively.”

This article first appeared on “Information-Management.com

9 Useful R Data Visualization Libraries for Any Discipline

By Asha Hill — Customer Success Analyst at Mode

If you’ve visited the CRAN repository of R packages lately, you might have noticed that the number of available packages has now topped a dizzying 12,550. This means there are packages for practically any data visualization task you can imagine, from visualizing cancer genomes to graphing the action of a book.

For new R coders, or anyone looking to hone their R data viz chops, CRAN’s repository may seem like an embarrassment of riches—there are so many data viz packages out there, it’s hard to know where to start.

To provide one path through the labyrinth, today we’re giving an overview of 9 useful interdisciplinary R data visualization packages. We’ve noted the ones you can take for a spin without the hassle of running R locally, using Mode R Notebooks.

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Python for Big Data Analytics and the Role of R

Two Popular Open-Source Programming Languages to Consider for Your Data Science Toolkit
R and Python are two very popular open-source programming languages for data analysis. Frequently, users debate as to which tool is more valuable, however both languages offer key features and can be used to complement one another. A common perception is that R offers more depth when it comes to data analysis, data modeling and machine learning, but Python is easier to learn and tends to present graphs in a slightly more polished way.1,2 Using the interface Python offers for calling R allows users to reap the benefits of both of these powerful, popular tools for data science. Even if you choose not to combine the two, the different ways in which these two languages are valuable make them both important parts of a data science toolkit.

Why Python?
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