Nik Rouda

Nik Rouda

Nik is a senior analyst covering big data, business intelligence, analytics, visualization, and other technologies that help people understand what is happening in their business. He enjoys working with early stage investors in validating both products and business models. He has a broad IT background spanning both infrastructure and applications, a customer-focused perspective, and extensive experience in marketing and technical sales worldwide.

Recent Posts by Nik Rouda:

Delta-V Awards - 2016 Edition - 20 to 16

It's that time again. Time to reflect on the year that was. And what a year it was for big data and analytics, with many massive advances in the technologies that power our understanding of the world around us. I don't know about you, but I certainly need a better understanding of the world. Thankfully, the industry continues to deliver innovations and gives me plenty of choices for recognizing those companies whose efforts are changing the velocity of insights. An introduction to the Delta-V awards and past winners can be found here. 

Topics: Data Platforms, Analytics, & AI

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: Data Platforms, Analytics, & AI

How the US Election Polls Failed Us...

...and what it means for big data and analytics.

Topics: Data Platforms, Analytics, & AI

IBM Jumps the Cognitive Shark


Generally, IT events are pretty subdued affairs. Some good meetings, some good dinners, some boring breakout sessions, and lots of overly ethusiastic marketing claims. IBM's World of Watson was exceptional. The videos felt like days of Superbowl commercials on Watson. Every buzzword was covered: cloud, mobile, social, IoT, open source, analytics. Bingo! Brand name customers dutifully trotted out on stage. IBM said the company's own brand value statement should be "humble genius" but I didn't see much evidence of either attribute. This was the most over-the-top, over-produced brag fest I've seen in years of IT conferences.

Topics: Data Platforms, Analytics, & AI

AI & Machine Learning Take Center Stage


Live from New York, if just a bit Off Broadway, were the O'Reilly AI conference and Strata & Hadoop World shows last week. These events were fascinating as character studies of overlapping technology stars. AI is now the fresh faced celebrity, if a bit immature, raring to get a big break, while Hadoop has become recognized as always delivering a solid performance, but may be fading a bit in glamour.

Starting with the AI conference then. This was a stunningly cool event. The stuff being done with machine learning, deep learning, neural networks, etc., is incredible. A well-trained AI can accurately process rough inputs, analyze to build complex models, and predict all manner of outcomes. In an instant. Except it's all still rather hard to use and could benefit from some better packaging and integration with other IT technologies. If there is a shortage of general data scientists, I wonder how many people can really master machine learning as it's presented today. Can we say "user interface?" That aside, we are on the cusp of some truly remarkable things. Though AI may still struggle to do some tasks my 10-year old tackles effortlessly, at the same time it can process phenomenally more info and find hidden patterns. And it's fascinating how an AI thinks, perceiving the world in fundamentally different ways than us meat. This space is ripe for massive growth, if we can get the tech accessible to more than a handful of companies, universities, and government labs.

Topics: Data Platforms, Analytics, & AI ESG on Location

ESG Video Capsule: Upcoming Research on Database Market Trends

Preview the topics of ESG's upcoming research project examining who the buyers and influencers of database decisions are and what drives their evaluation by watching this ESG Video Capsule:

Topics: Data Platforms, Analytics, & AI

IT Operations Analytics from the Source

 

One of the top uses of big data today is IT operations analytics. This makes sense. By nature, IT components are designed to log all of their many status messages, and this information is generated with debug, tracking, and audit purposes in mind. The aggregate output, however, can be a logistical problem in itself. For each device, some poor sysadmin has to decide what level of logging is desired, and then live with the consequences of that decision. Set the logging threshold to "errors only" and important context will be missing when it's time to diagnose an issue. Set the logging criteria to "everything" and staggering amounts of data will be generated, often too much to process, and certainly much of little or no value. Limit the time period to an hour or a day, and the key information may have been overwritten by the time it's needed, and then the problem will have to be recreated.

Topics: Data Platforms, Analytics, & AI

The End of the All Together

And then there was one. I've written before about the end of "end-to-end" IT solutions, but yesterday was a defining moment in the market. HPE sold off its software division, including all big data and analytics assets like Vertica and IDOL. Dell and EMC closed their merger, creating a new hardware powerhouse, but only after selling its own Dell Software group which encapsulated Statistica and TOAD. Intel carved out McAfee security. In the not so distant past, IBM also shed its server division to focus more on analytics and Watson offerings. What all these actions signal is a seismic shift in the market, a rift between the hardware and software and the idea that a single vendor can win everywhere. The sole remaining exception may be Oracle, and if the movie Highlander taught us nothing else, it is that there can be only one.

Topics: Data Platforms, Analytics, & AI Cloud Services & Orchestration

Understanding Poetry at the HPE Big Data Conference

HPE's Big Data Conference was given the tag line #SeizeTheData which immediately made me think of the wonderful film "Dead Poets Society." One of my favorite scenes is when the students learn how to measure and analyze poetry. You can refresh your memory of the dialogue by watching this clip  or reading here. Of course, the whole point is that using analytics doesn't work in poetry appreciation. Which I thought made #SeizeTheData rather ironic as a hashtag. 

Topics: Data Platforms, Analytics, & AI

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: Internet of Things Data Platforms, Analytics, & AI