Moving Forward with Machine Learning for Cybersecurity

At Black Hat last week, you couldn’t pass a slot machine without some cybersecurity technology vendor crowing about machine learning or artificial intelligence. Yup, machine learning algorithms have great potential to help with security analytics and employee productivity, but this technology is in its infancy and not well understood.

Topics: Cybersecurity machine learning artificial intelligence

Data Makes the World Go Round...

...Or at least data can be used to model the Earth's rotational vectors and predict trajectory locations over time. A few things have got me thinking about the world of data and the data of the world. First, I watched "Hidden Figures" with my girls last night. Amazon's machine learning models accurately predicted that we would like this film and positioned it strategically on our suggested titles list. There was a lot to like, including worthy themes around:

  • Math, science, engineering, analytics, and computers.
  • Women earning respect for their ability to excel in these areas.
  • Minorities demonstrating that diversity in teams improves outcomes.
  • Healthy patriotism in the context of advancing human potential.
  • Strategic government funding of research and innovation.
  • and Kirsten Dunst (assuming that her inclusion here doesn't undermine all my previous points).
Topics: Big Data Data Management machine learning data set

The Google Machine Learns to Compete

Language can be frustratingly ambiguous. Or delightfully ambiguous. When you read the title of this blog, did you parse it as Google is a machine that is learning to compete? Or that machine learning will be how "the Google" competes? Both work, and both are true.

First meaning: there is clear evidence Google is making huge progress in cloud services to better compete against its rivals. Executives at the Google Next 17 conference cited a competitive win rate of 60% in the last quarter, with best results when the company gets a fair shot and customers dig deep into the technical differentiation. Sure, Microsoft is entrenched in most enterprises, and AWS has ridiculous momentum, but Google has invested $29 billion over the last three years to innovate in its own way. Many of the services' advantages are subtle but impactful, such as more granular billing for data warehouse consumption with BigQuery, custom configured compute instances, or the potential for API access to data services already within Google's domain. These have real benefits in reducing costs and increasing value of data.  Machine learning even helps Google be more efficient, like finding ways to reduce data center cooling costs by 50%. As ESG research shows the financial cost/benefit equation is still the top perceived advantage for cloud-based databases, then Google should win simply on price efficiency for compute and storage resources. See a past comparison of costs here. Assuming buyers take the time to understand this and don't default to their Microsoft sales teams or Amazon's DevOps audience dominancy.

Topics: Data Management & Analytics Data Management google machine learning

2017 Big Data & Analytics Prediction: Part 2: Machine Learning (Video)

It doesn't take a supra-genius AI to predict that machine learning will continue to get better this year. Yet, there is a disconnect between the public Hollywood view of technology and the current limitations. Check out the video below for my ideas on how the gap will begin to narrow:

 

 

 

 

 

Topics: Data Management machine learning big data and analytics 2017 predictions

Why Machine Learning is the Future of Big Data

Just as big data has emerged to heavily disrupt traditional databases and data warehouses, machine learning will be the next big wave of advancement in data management. Why, you ask? There is a simple, one word answer for you, "economics." They say any innovation has to be 10x better, faster, or cheaper to overcome the inertia of a traditional approach in IT. Apache Hadoop made it at least 10x less expensive to house data by distributing it across commodity hardware using open source software. Of course, there were (and still are) some rough edges and hidden costs, but this was compelling enough to get significant market traction versus legacy hardware and software. Weirdly, the core utility of Hadoop distributions has morphed towards being utilized mostly as a storage layer, with an ecosystem of other tools building analytics value above it.

Topics: Big Data Data Management Data Analytics machine learning

Election Data Models Lesson for Cybersecurity

If you are like me, you were pretty convinced that Secretary Clinton was poised to be the President elect. Confidence in this opinion was based on reviewing numerous big data analytics models from the fivethirtyeight.com, the New York Times, Princeton, etc.  The lowest percentage gave Mrs. Clinton roughly a 65% chance of winning on November 8. 

Topics: Cybersecurity security analytics machine learning

Second half predictions for big data & analytics

As summertime rolls on, we can enjoy a little sun, a little rest, and a big opportunity to reflect on the key trends to watch in the second half of 2016. Here are a few of my predictions of what comes next:

Topics: IoT Data Management IaaS Big Data Analytics machine learning

The Delta-V Awards: Machine Learning

The ESG Delta-V awards recognize the top 20 companies that made an impact in big data and analytics in 2015. Here are the ones to congratulate for their success in the category of Machine Learning, including cognitive, artificial intelligence, and variants.

Learn a bit about the Delta-V awards here.

Topics: IBM Big Data google machine learning

IBM, Databricks, and the Spark Movement

Many people consider "Hadoop" and "big data" to be synonyms, and certainly there is significant overlap between them. Yet in my mind, Hadoop is an ecosytem of data management tools, and big data is an approach to understanding complex situations. So the one can be used for the other, but surely isn't the only way to go.

Topics: IBM Analytics Big Data hadooop machine learning spark

Microsoft Invites You to Spark IT Up

Who builds the world’s most popular tool for analyzing data? Did you say Microsoft? Good. For most anyone in business, Microsoft Excel is where we get started around business intelligence, though we may not typically call it that. From this humble beginning, many graduate on to much more sophisticated solutions. What not everyone realizes is that Microsoft is capable of supporting you even as you go to much greater depth. At the recent Ignite event in Chicago, Microsoft made the case that day will improve worker productivity, especially around data insights and collaboration, with an extremely deep portfolio of complementary technologies.

Topics: Microsoft Analytics Big Data cloud database business intelligence machine learning