Storage Versus Data: The Role of Storage in Managing the Larger Data IT Ecosystem (Video)

As a storage guy, this is going to be little difficult for me to say, or write, but the enterprise storage technology is only one part of the data management ecosystem. It is not the be-all and end-all of data management.

This realization may be tough for some storage vendors to admit, while I assume the majority of CIOs just intuitively understand it. For a business, what matters is the data, not the storage. In this ever-increasing digitally enabled world of business, data management is not a necessary evil, it is a business enabler, and can in the right circumstances become a differentiator for your company and drive revenue growth.

Topics: Storage Data Management

Looking towards Data Management and Enablement in 2018 (Video)

Copy Data Management (CDM) and all the permutations of “Copy” “Data” “Management” with or without additional terms like “Active” “Enterprise” “Virtualization,” etc., seems to be the rage these days. A few years ago, there was only one visionary company talking about CDM. Today, we see a range of vendors now claiming to be CDM, even when they don’t deliver the breadth of capabilities that the industry may have presumed CDM to be; and others who have been quietly delivering those capabilities and more, without the terminology (a.k.a. they were CDM before CDM was cool).

Topics: Data Protection Data Management Copy Data Management CDM-DME

Think Economics -- Not Features -- When Evaluating Big Data Value

Traditional enterprise data warehouse solutions helped to open the eyes of many organizations to the value of their data. Although these are significant systems, organizations quickly learned to monetize the actionable insight extracted from these systems, which led the rampant growth of the industry. Big data did not get big just from data growth. It got big because of its potential value, opportunities, and savings.

The more cost-efficiently you can capture a lot of data, plus the number of ways you can analyze it, equals the more worthwhile all that data could become. Value is results divided by costs. These (pseudo-)equations of big data value now extend not only to the disruptive power of transformative technologies like Hadoop, but also to increasingly popular cloud services for databases and data warehouses.

Topics: Big Data Data Management google data warehouse

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: Informatica Data Management platform-as-a-service

Citrix Synergy 2017: Anticipating a Focal Point on Cloud and Security

ESG annually checks in with IT professionals to capture the top items on the CIO’s whiteboard, and potential areas of increased spending. ESG’s research findings place security at the top of the list (no surprise here) but what if you are an IT vendor (like Citrix), have been in business for 20+ years and, while you help with security, you don’t possess any security products as they are defined by the market. Now let’s layer in the fact that businesses are racing towards cloud consumption models and you have 20+ years’ experience producing on-premises solutions. This means that it’s time to make some product family changes, marketing tweaks, and sales execution adjustments—and this is exactly what I expect we will see at Citrix Synergy 2017.

Topics: Data Management Citrix Citrix Synergy

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: Cloud Computing Microsoft Data Management artificial intelligence

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: Microsoft Data Management artificial intelligence

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: IoT Data Management

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: cloud Data Management Big Data Analytics

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