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:

Informatica Origami

Choosing an orange origami swan as your logo is an interesting signal, ripe with metaphors. Was Informatica unfairly labeled as an ugly duckling before? Is the company now folding in on itself? Does it take a lot of skill and dexterity to create something beautiful from a blank square of paper? Is this latest re-invention a swan song? Let's hope not, as there is actually quite a bit to like about the new directions.

Anil Chakravarthy, Informatica's CEO, has outlined the three iterations of the company. First, focused on ETL and ingestion. Second, expansion into MDM, archiving, and data quality. And today, re-launching as an "enterprise cloud data management" platform-as-a-service (PaaS) vendor, which is again functional, if not sexy.

Topics: Data Management Systems Management

Microsoft Build: a Smarter Future Today

Microsoft Build is aimed at developers. From the presenters' dress code of jeans and tight polo shirts or T-shirts, this much is clear. The expectation is that attendees are here to learn to use a broad portfolio of Microsoft software and cloud resources to create new applications. The keynotes were a mix of genuinely inspiring, even life-changing possibilities and the lines of code that were used to make it happen.

A top-level theme was" cloud’s new edge," or rather the combination of an intelligent cloud with an intelligent edge. Since I've been writing for a while about how the battlefield for cloud isn't resource capacity per unit cost, but rather the tooling for integrated and assisted analytics, this idea wasn't a big surprise to me. See a few example blogs here and here and here

Topics: Artificial Intelligence Data Management Cloud Platforms & Services

Microsoft's Broad AI Strategy in 10 Tweets

This morning Microsoft hosted an analyst-oriented overview of select elements of the company's artificial intelligence strategy. Since they said it wasn't under a NDA, I'm happy to share that with you here. This really just scratches the surface (no pun intended) of their offerings around AI, machine learning, deep learning, and associated applications, enhancements, APIs, and development platforms. As noted in several blogs before, this will be the major battleground for the cloud service providers. It was high time Microsoft took the field and strutted their might. I'll have more to say on the topic after Microsoft Build next week.
Topics: Artificial Intelligence Data Management

The Real Dangers of Assisted and Augmented Reality

 

Anyone who knows me knows that I am a big fan of using science, technology, and analysis to drive continuous improvement and efficiency. I can't help but calculate the time, cost, and relative value of different approaches. For example, "What if we switched to using just one kind of LED light bulb throughout the house and ordered dozens at once when they are on sale? Does that make it easier to replace them? Is the NPV of investing the capital in surplus discounted light bulb stocks now greater than the future cost and effort of making subsequent acquisitions?" This is a real thought process that I have had, debated, and defended recently. Aren't I fun to live with?

I also value self-reliance and simplicity and the ability to take apart mechanical things and repair them. In the last week I've disassembled and fixed our leaking Subzero refrigerator (blocked drain on the condenser drip pan) and our also leaking Frigidaire clothes washing machine (loose connection on the internal filler hose). I love my 40+ year old pickup truck and my 50+ year old Mustang all the more for their repairability. In general, parts are accessible, understandable, and often easily cajoled back into operation (like the corroded connection that occasionally reduces the alternator's ability to charge things). Bonus: Should I need them, replacement parts are often 10x cheaper than their modern equivalents, like $27 for a refurbished alternator for my old Mustang, versus $270 for the one on a modern BMW.  Kids love to ride in the old vehicles, too. "Wow, look I can turn this crank to raise and lower the windows, so cool!" To me, the understandability is a key part of the pleasure. With little effort you can see what each thing is doing and infer how it is doing it. My friends and I may not be able to design a carburetor, but we can take one apart and rebuild it.

So, I'm struggling a bit with the idea of applying machine learning and artificial intelligence technologies to everything around me. Google recently gave me a Google Home device; I'm not quite sure why. Maybe they wanted to give it more real world voice recognition training opportunities. Maybe they wanted me to write about it, as good social marketing. Maybe they wanted to hear how I'm advising their competitors, naughty, naughty. I don't like the idea of it listening to my conversations. My wife really doesn't like it listening to her. The kids adore it. It's something that I feel a natural affinity for anyway, having spent some time at a "smart home" startup a decade ago, which we'd now label "IoT" technology. Controls for our system were amazing stuff at the time, "I can text my home to check on the alarm system or control lights!" They were also frighteningly rudimentary: I had to TEXT my home to turn on my alarm system or control lights because "smartphones" and their app developer tools didn't exist yet. Now I can speak aloud and call up any song I want and have it play in any room, and read the actual lyrics, no matter how garbled the singer. Mondegreens are a relic of the past. I can ask "Hey, Google, why isn't my car charging?" and it suggests possible problems with the alternator. Then it gives me a link to a website for more information or YouTube videos showing how to do it. This is amazing stuff, available commercially today. So much more rewarding than scheduling an appointment with an "expert" mechanic at a dealership who may or may not get to the same fix, but will certainly charge me $150 per hour while he Googles it himself and then marks up parts prices by 50% or more. The point here (yes, there was a point here) is that technology has made it far, far easier and cheaper for anyone to access knowledge and learn how to do things. We can readily disintermediate supply chains and services. Anyone can now be an expert on any topic, on demand. This assisted or augmented reality for everyone is practically magic.
Topics: Data Management Internet of Things

Picking a Winner for the Best Cloud

Stop me if you've heard this one: five major cloud service providers walk into a bar. They make a bet that the one with the best enterprise cloud will get free drinks for life. The bartender tells them he already knows who will win. They are astonished as none of them has yet had a chance to boast about their server specs, cost per gigabyte, network quality, security features, uptime record, support teams, number of data centers, or any other common claim or metric. How can he possibly have predicted the outcome?

Topics: Data Management Cloud Platforms & Services

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: Data Management

Database Forecast: Cloudy with Increasing Chances

ESG has recently published an overview on IT market adoption of cloud-based databases. Shall we just call them cloudbases? Perhaps not. A major trend is emerging. While relatively few are choosing cloud as their primary mode of deployment, majorities are currently running at least some of their production workload in the public cloud. Attitudes and adoption vary considerably by age of company (and age of respondent!), reflecting how deeply entrenched traditional on-premises offerings and processes may be for different businesses. How many, how many, and how much, you ask? ESG research subscribers can read the full report here.

Topics: Data Management Cloud Platforms & Services

Cloudera Builds Strength and Agility

Haters gonna hate on Hadoop, but they've confused 'tween growing pains with weakness. The broader Hadoop ecosystem continues to mature at a very healthy pace. Even if the players are starting to outgrow the labels of "Hadoop" and "big data," leading companies in this sector will continue to build on what is now established to be a strong core. Perhaps most prominent among these young varsity athletes is Cloudera.

Cloudera has long enjoyed the popular attention of the market. More than 1,000 customers use Cloudera in more than 60 countries today. Technology vendors and channel partners have associated themselves with the cool kid. At last count, Cloudera has over 2800 partners, and that number includes 450 ISVs, of which 388 are certified, bringing 184 partner-developed solutions, of which 120 have been verified in production, and 44 are available in a ready-to-roll solutions gallery. Meanwhile big names like Intel and Michael Dell have provided significant scholarships to fund the company's development.

Topics: Data Management

Strata Data Conference Gets Good

Last week we saw the rebranding of Strata + Hadoop World as the new Strata Data Conference. This name change reflects the nature of the content, which is decreasingly about specific Hadoop projects and increasingly about how to get analytics value from any data anywhere. Beyond that, the show reinforced several key themes I've been predicting for some time.

Topics: Data Management ESG on Location

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