Computer processing speeds affect big data

Increasing Processing Speeds with new Memory Storage

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One of the biggest pitfalls of our data collection and analysis is our storage capabilities and processing time. We’re limited by what our machines can handle. And though we’re working with more powerful machines than ever before, we still need to break barriers to access even faster processing and larger storage capacities.

Fortunately for us, Intel and Micron Technology have developed the 3D X-Point, a solid-state drive. This technology is four times denser than traditional RAM because it combines RAM and flash storage. This technology is almost 1,000 times faster at reading and writing information because it works even when it’s turned off. This is a revolution in data processing.

You’ll get to try out this new technology in the Intel Optane SSD DC P4800X Series, with a price tag at $1520. But what does this do for the future of data analysis?

What will this do for data analysis?

Essentially, this technology will increase processing speeds by 600%. This works alongside your hard drive, rather than replacing it, making it an ideal solution for data analytics. This kind of technology (which will only get better and more efficient over time) can be an affordable solution to gaining insights from large memory pools.

Why do processing speeds matter?

Big data isn’t going away. Let’s look at the amount of data that we generate every minute. You should view the Data Never Sleeps infographic put together by DOMO, but here’s a rundown:

  • Google receives over 2,000,000 search queries
  • Facebook users share 684,478 pieces of content
  • Consumers spend $272,070 on web shopping
  • Twitter users send over 100,000 tweets
  • Apple receives nearly 47,000 app downloads

And there’s even more. This doesn’t count the medical records generated, utilities usage information, articles published, students accepted to universities, etc. Our need for greater processing speeds for data analytics is only increasing by the minute. Literally.

Conclusion

We need better forms of memory storage and processing in order to handle the huge amounts of data that are being produced by the minute.

The technology that Intel and Micron Technology created is a good step in the right direction but is just the frontier of the technology we’ll see and use in the future.

We use state-of-the-art technology to process big data and give you useful visual analytics. Contact us to talk about your data needs and request a pilot.

Is Artificial Intelligence Dangerous?

Is Artificial Intelligence Dangerous? Our Thoughts on the Matter.

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If you’ve followed us for some time, you’re well aware that we like to discuss the future of technology, Artificial Intelligence, data, and analytics. And what is at the forefront of thought concerning AI more often than this one question: is artificial intelligence dangerous?

Many thought-leaders, like Stephen Hawking, think AI will be the death of us all. But we don’t quite agree with that doomsday attitude. We think, much like anything in this world, AI and it’s inherent danger or safety is in how we use it. So, really, it’s up to us.

We enjoyed this article on Futurism, titled, “Artificial Intelligence is Only Dangerous if Humans use it Foolishly”. We’ve got a couple of favorite parts of the article that we’ll share here, but you should definitely go read it for yourself and join us in discussing this important question.

There are a lot of concerns over the safety of AI (in most science fiction movies the AI outsmarts us too quickly), and there are very real concerns that AI will replace a large number of jobs (47 percent, according to this study).

There may be a big push to use AI to replace everything and everyone, but as Dom Galeon says, “Moreover, there’s the danger of looking at AI as the magical solution to everything, neglecting the fact that AI and machine learning algorithms are only as good as the data put into them.”

Artificial Intelligence is only as dangerous as we make it.

It’s not the AI we should fear. It’s the way humans will utilize that AI. And while we can sit and imagine numerous doomsday scenarios, the plain fact is that AI will likely replace many of the utilities and jobs we rely on now. Hopefully, this move will improve the human condition. But the AI can only do what we allow it to do. What algorithms are we using? What data are we feeding it? We should keep this in mind so that we don’t let the need get ahead of us.

As Galeon says, “Ultimately, the greatest threat to humanity isn’t AI. It’s how we handle AI.”

What are your thoughts on the future of AI? Should we fear it? Share your ideas in the comments.

EPA Case Study

EPA Case Study

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Working with various research scientists, ChalkLabs developed a novel solution to help EPA facilitate coordination among its various intramural and extramural research activities. For the first time, the scientists were able to visualize their programming laid out in an orthogonal map allowing them to compare and contrast projects from disparate programs and identify overlaps and gaps for future programming.

Greater Spokane Incorporated Case Study

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GSI Case Study

ChalkLabs has worked with regional chambers and municipal institutions to apply advanced analytics enabling Economic Development.  By integrating PushGraph® into individual regional selection criteria, ChalkLabs is able to design targeted recruitment strategies that support an organization’s efforts to identify key drivers for relocation or growth within a community or region.  The ChalkLabs Economic Development Solution allows an organization to effectively visualize their prospective targets, and identify key stakeholders required to engage in discussion on evaluating the value proposition of the region.

How can Big Data help the utilities industry?

How Does Big Data Improve Utilities?

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The utilities industry deals with a lot of data. From infrastructure to customer usage, and fgrids to weather monitoring systems. But how can the industry use that data to improve products and services? What started as manually reading meters has changed as technology has evolved. Fortunately, the collection of data has become much more sophisticated. But as more and more data is collected every day, utility companies still struggle to analyze it all.

Basically, there are two main ways to use data to improve the utilities industry. Use it to improve customer services and products, and to improve operational efficiency. This is where our platform comes in to play. Our Pushgraph® platform can handle huge collections of data points and offers real-time computing and visualizations. The utilities industry can use big data analytics to help study and identify patterns of correlated human behavior and energy usage.

Utility companies have been utilizing our Pushgraph® data fusion solutions for Smart Cities. To learn more about Smart Cities, read this recent article published by CNBC.

Contact us to learn more about how we help Utilities companies improve.

We’re constantly working with utility companies to improve their relationship with Big Data. Give us a call or visit our Solutions page for more info.

National Institutes of Health Case Study

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In 2009, ChalkLabs was hired to help the National Institutes of Health (NIH) analyze their $26 BN portfolio of grants. The NIH wanted to learn more about which institutes were performing what types of research and if there were gaps or overlaps in funding, research, and personnel. They wanted to do this in a way that captured the semantics across subject areas regardless of the title of a given grant.

In order to accomplish these goals, ChalkLabs created a novel portfolio analysis tool whereby administrators, analysts, or researchers can quickly search, navigate, cross-reference, visualize, and export their large-scale data analyses in a web-based, real-time, easy-to-use, intuitive fashion. Rather than simply searching for keyword matches, our portfolio analysis finds thematic and semantic similarities, allowing users to access a broader and more thorough view of their data.

The Beginner's Guide to Predictive AI

A Beginner’s Guide to Predictive Artificial Intelligence

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As you may know by now, many businesses have developed uses for AI (artificial intelligence). Therefore, the implications of AI for the future of healthcare, education, and learning, and robotics (not to mention space travel) are far-reaching and exciting, but it can still be a difficult topic to understand. That’s why we’ve put together a quick guide to Predictive Artificial Intelligence or, AI that can analyze large chunks of data and predict trends and events.  Because we’re big fans of data, analytics, artificial intelligence, and predictive analysis.

What is Predictive Artificial Intelligence?

Basically, Predictive AI is simply artificial intelligence algorithms including pattern recognition, predictive modeling, and advanced data analytics.

How can we use Predictive Artificial Intelligence?

Because there’s no limitation to the uses for predictive AI, we’re only going to list the big ones:

  1. Storm or Weather Predictors
  2. Behavior Recognition
  3. Healthcare, including mental health assessments
  4. Disease outbreaks
  5. Predicting life-span and likelihood of disease/illness

You can read more about these and others by viewing this infographic from Futurism.

AI and predictive analytics can be used to improve the quality of life for everyone in the future, but it’s being used right now in big data and business analytics. Give us a call (812-250-8649) and we can show you how we’re using predictive analytics in our Pushgraph® platform.

Pushgraph® Overview and Features

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At ChalkLabs we’re all about powering Knowledge Discovery. We know that data collection doesn’t matter unless businesses, educational institutions, disease research foundations, and government organizations can organize, visualize, and analyze that data. That’s why we focus on more than platforms; we focus on solutions to real-world problems. Here’s a quick Pushgraph® overview. 

At its core, Pushgraph® is a lightning-fast analytics and data visualization platform with an easy-to-use interface. We’ve created a flexible, sustainable, and cost-effective tool that develops long-lasting solutions and relationships.

Leading features include in-memory computing, predictive analysis, semantic search, visual analytics, and security protocols. Visit the Pushgraph section of our website to learn more, or call us to ask for a demo and Pushgraph® overview: 812-250-8649

USDA Case Study

USDA Case Study

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In 2013, ChalkLabs developed a novel research portfolio management solution for the National Institute of Food and Agriculture, a research arm of the US Department of Agriculture. This solution allows directors, national program leaders, national program staff, and communications specialists the ability to visually identify latent relationships, identify overlaps, and identify gaps within the grants and research programs that they fund. This solution immediately generated a cost efficiency of 96% on average for two common types of interagency and Congressional requests the agency receives. This new solution is saving the agency hundreds of thousands of dollars each year.

WSU Case Study

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In 2016, Washington State University (WSU) contracted with ChalkLabs to build a solution for higher-ed. This new solution will provide WSU with a competitive advantage in amongst their peers in many different areas including human resources, research programming, and competitive intelligence. Feedback from users has been excellent so far in regard to the research outputs produced and how much time and money this new solution will save the university compared to previous tools and processes used for such activities.