Chuck's Blog

Wardens Of The New Data Lake

The cool stuff: enterprise IT groups are now moving beyond the 1990s model of structured data warehouses, and now are starting to think in terms of more accessible and cost-effective "data lakes": vast repositories that are landing zones for any and all enterprise data that might potentially be interesting in the future.

The motivations are clear: information is the new asset, and its value can only increase when new uses are found.  Yes, applications still "own" their information, but it's also an asset that's owned by the organization at large.  Data lakes hold the promise of making it far easier to span organizational and temporal boundaries and harvest all that latent analytical goodness.

But, as in any valuable shared asset, it's going to need to be managed: what goes in, what comes out -- and who can play.

And when new important roles emerge, technology innovation is not far behind.

Digital Literacy For Business Leaders

It's happened more times than I care to count. 

I'm meeting with relatively senior business leader, not an IT person.  We're talking about what's going on in their world. 

I ask an open-ended question around, say, social or mobile or cloud, or maybe big data analytics, and I don't get the response I was expecting.

It's obvious I've committed some sort of faux pas.  There's a stiffening, a deer-in-the-headlights sort of look, followed by a quick referral to "someone in IT who's working on it". 

An awkward pause usually follows.

I bet if I asked a more familiar question around competition, or business strategy, or budgets or people or globalization -- I'd probably get a knowledgeable and engaged response.

I used to think it was my fault for bringing up the topic.  Not anymore.

VMware's Project Serengeti And What It Means For Enterprise Hadoop

Virtualize something -- anything -- and you make it easier for everyone to consume: IT vendors, enterprise IT organizations -- and, most importantly, business users.  The vending machine analogy is a powerful and useful one.

At a macro level, cloud is transforming IT, and virtualization is playing a starring role.

Enterprise-enhanced flavors of Hadoop are starting to earn prized roles in an ever-growing variety of enterprise applications.  At a macro level, big data is transforming business, and Hadoop is playing an important role.

The two megatrends intersect nicely in VMware's recently announced Project Serengeti: an encapsulation of popular Hadoop distros that make big data analytics tools far easier to deploy and consume in enterprise -- or service provider -- settings.

And if you're interested in big data, virtualization, cloud et. al. -- you'll want to take a moment to get more familiar with what's going on here.

Rewiring The Human Brain In The Era Of Big Data Analytics

Our poor, struggling brains.

Evolved for survival on the serengeti, we're now attempting to thrive in a digital world that we weren't programmed for.

Sure, we can adapt -- over time.  Through thoughtful reinforcement, we can learn to overcome many aspects of our evolutionary programming.

But being aware of the problem is half the challenge.

I've noticed that there's more and more conclusive evidence that we -- as a species -- weren't wired to work effectively with data.  It's something we learn to do, often despite ourselves.

And in the coming era of big data analytics, we're going to have to collectively get better at it.

Much, much better indeed.

When Business Leaders Own Their Information

The subject of this post came to me in a rather convoluted manner.

As I began to work more with organizations who are trying to achieve broad analytical proficiency, I started to notice something that at first appeared both unusual and unsettling.

One or more business leaders in the organization would begin to lay strong claims to "owning" their information.  

They were prepared to invest their cycles and resources towards acquiring more and improving the value of what they already had available.  They could be persuaded to share -- depending on circumstances.

While these leaders were moderately polite to others in the organization who were doing the same, they didn't want to be dependent on other groups (including, to a certain extent IT) as they went forward with their information ownership goals.

As I started to see more of this -- including in my own company -- I had to stop and think: is this a good thing, or a bad thing?

And I've come to the conclusion: it's a very good thing indeed.  It's precisely the right behavior, and should be encouraged.

VMware To Acquire Cetas: Making Analytics Easier To Consume

Today VMware made an acquisition that's extremely interesting from several perspectives: cloud, virtualization stacks, big data analytics and more.  

While certainly no Instagram, I think we'll all look back in a few years and agree -- yes, this was an important one.

Cetas describes themselves as ".. an early stage startup that delivers Analytics-as-a-Service to customers and is driving the democratization of analytics and business intelligence by dramatically lowering the barriers to entry for all companies, from SMB to large enterprises."

Yes, quite true when they were a standalone entity.  

But as part of the VMware (and, by extension, the EMC) family, it gets much more intriguing indeed ...

Tales From The Trenches: Changing Your Business Model With Big Data Analytics

The premise -- at a high level -- is easy to understand: the new-found power of predictive analytics has the potential to change the way you do business.

But the devil (as always) is in the details.

Clearly there's a transformation at hand.  

The starting point: a familiar world of historical reporting on events in the past.  The goal state: the ability to better predict (and capitalize on) likely future outcomes.

And getting from Point A to Point B is rarely as seamless and pleasant as you read about :)

To that end, I'm actively studying transformational stories about how organizations are learning to change the way they do business around big data analytics.  

And, as it turns out, EMC Corporation is an excellent example of just such an organization.

Analytically Enabling The Enterprise via BI As A Service

Many amazing things being done in our EMC IT group these days.

The original premise was that -- once we had made enough progress on transitioning to an ITaaS model -- the value-added strategic IT initiatives would start to come hard and fast.  Well, they are.

Why?  

Almost everything IT does now is based on variable, re-usable (and competitive!) services: IaaS, PaaS, SaaS etc.   IT friction is dramatically reduced as a result.

Business users are now marketed to, and not simply told what they can and can't have.  

A financial governance model based on price signals and an internal marketplace is now starting to optimize IT decisions.  

It's a very different IT game here at EMC than just a few short quarters ago.  Perceptions have changed; enthusiasm is everywhere.  And I get the privilege to share it with all of you ...

In a previous post (The Journey To Big Data Analytics), I outlined the process by which most companies we thought would likely acquire and leverage new analytical capabilities.  At a high level, we now feel prepared to start our own internal journey in earnest, much like we're simultaneously tackling the challenge of mobilizing our extended enterprise.

In this post, I'd like to begin to share with you how we're organizing for success around this particular topic.  Yes, we're using plenty of EMC technology (naturally!), but the real key to anything transformative is the organizational approach.

The Journey To Big Data Analytics

Our story so far ...

Many now realize that big data analytics is the next big thing in creating value from information. 

As such, more than a few people are wondering how they might think about their journey: how they get from where they are today to where they want to be in a sequence of logical, digestible and justifiable steps.

We spent a great deal of time in 2011 learning from organizations who had visibly achieved this proficiency.  What kinds of skills were required.  How they were organized.  And, more importantly, how they got to where they are today.

We've boiled down our learning into a relatively simple three-phase model that describes that pathway that many organizations seem to follow.

And, if you're wondering how to get started on your own journey, perhaps you'll find this useful.

Greenplum UAP And Enabling Your Data Science Team

It must be Data Science Week here at EMC.  

I've already written about our latest findings from the EMC Data Science Survey, and shared how EMC Education is offering their first Data Science Associate coursework to support the increased demand for knowledgeable individuals.

It's almost inevitable that all of this would be leading up to an EMC product announcement, and -- yes -- that's the case.  

The new Greenplum UAP (unified analytics platform) is especially exciting to me because it appears to be the first integrated software suite that is precisely targeted at the newer style of data science teams.

Here's the pitch: in the new world of big data science analytics, all the value comes from the data science team.  Since it's pretty obvious that (a) knowledgeable data science professionals will be very scarce resources for the forseeable future, and (b) their time is extremely valuable, then ostensibly makes sense to build tailored software environments that maximize their productivity.

That, in a nutshell, is what makes Greenplum UAP so unique and compelling.