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Archive for the ‘big data’ Category

What’s New in Dragline (7.1.0): Installing HP Vertica Pulse

Installing HP Vertica Pulse on Vimeo

HP Vertica 7.1.0 introduces the general availability of HP Vertica Pulse, our add-on sentiment analysis package for HP Vertica. Pulse provides a suite of functions that allow you to analyze and extract the sentiment from text, directly from your HP Vertica database. For example, you can use HP Vertica Pulse to analyze sentiment from Tweets or online product reviews to get a feel for how satisfied your customers are about your products or services.

HP Vertica Pulse automatically discovers attributes included in text and scores them using a built-in system dictionary. You can tune user-dictionaries to detect certain words or phrases, to determine how words are scored, and to filter out attributes that are of no interest to you. Because of this flexibility, you can tune HP Vertica Pulse to work for your specific business needs.

Currently, HP Vertica Pulse allows you to analyze English language text only. You can download HP Vertica Pulse as an add-on package for your Enterprise Edition or as a trial for your Community Edition, from my.vertica.com. Additionally, the Innovations section of the HP Vertica Marketplace offers a beta version of Pulse for Spanish only. Take a look at this video to learn how to install Pulse and stay tuned for our next video, ‘Using Pulse’.

HP Vertica Pulse documentation.

Is Big Data Giving You Grief? Part 5: Acceptance

“We can do this”

Over the last month or so, this series has discussed how organizations often deal with a missed big data opportunity in ways that closely resemble the grieving process, and how that process maps to the commonly understood five stages of grief: denial, anger, bargaining, depression, and acceptance. This is the last entry in the series; it focuses on how an organization can move forward effectively with a big data project.

While big data is big, complicated, fast, and so forth, it is also very vague to most businesses. I was at an event recently where a poll question was asked of a room full of technology professionals – “How important is big data to your business?” A surprisingly high number of respondents felt that big data wasn’t relevant to them. Afterwards, I spoke with one of the attendees over lunch. I asked him what the primary challenges were to his business. It turns out that their business costs rely primarily on commodity costs – if the price of an input such as oil goes up or the supply is disrupted, the entire business is affected. I asked him whether he thought social media was relevant to his business, and he didn’t believe so. I then talked about how hedge funds have found that Tweets can be a very effective way of predicting commodity prices and availability disruptions. Until that moment, he was unaware that this was possible. This was what I call a “light bulb” moment. Suddenly, the appeal of big data became clear.

This experience highlighted for me a fundamental issue I see daily in the big data space – that it’s just too big (and vague) for many organizations to grasp its tangible value – an important pre-requisite to moving forward. So even while they go through all the stages of grief and struggle with the fact that their competitors may be outperforming them due to big data, companies also struggle with how to turn that into a plan of action.

Once they’ve worked their way through the realization that something’s wrong, organizations are often ready to take action. Here are some of the most helpful techniques I’ve seen businesses take over the years to begin an effective big data program – to accept the reality of the situation, and move forward.

Execute tactically, think strategically
For the organization first tackling big data, this is probably the most important thing to keep in mind. Big data projects rarely start with a crystal clear vision of what the strategic outcome should be. Uncertainty and hype around the opportunity, unfamiliarity with how to handle big data, lack of a data science competence, and so forth all create challenges that make it tough to articulate an up-front strategic vision.

But don’t interpret that as a pass to ignore the potential impact of a big data project. Thus the advice. Execute the project tactically – be prepared to move fast with the aim to demonstrate value quickly. And when the project is complete, a debrief with the business leadership is essential. In this debrief, answer two questions: How did applying big data matter to the business? And given what we’ve learned, how can our next project impact the business in a bigger way?

The answers are inputs to the next project, and over time can serve as a powerful guide to articulating a big data strategy for the business.

Don’t boil the ocean
Very often, when a group of people from an organization attend a big data event, they all come back very enthused about big data projects. Vendors love to talk about big-picture, blue sky notions of transforming businesses or industries with big data. It’s exciting stuff, but doesn’t lend itself to immediate action – especially for a business new to big data.

So don’t start there.

A much better approach is to identify measurable goals that can be tied to actions that can be completed in the right timeframe. What’s “the right timeframe”? Good question! In part, it depends on how open the business is to a big data initiative – if the leadership team is bearish on the idea and needs powerful convincing, it’ll be important to demonstrate value quickly. Also, immediacy is a powerful guide to enthusiasm – so don’t tell the IT team to disappear for a year and come back with a big data architecture. There’s no immediacy, and as a result there likely won’t be much focus. So don’t boil the ocean and try to do everything at once, in a big hurry. Start with focus, and retain it as you progress.

One foot in front of the other (and sometimes…baby steps!)
When an organization wakes up and realizes that it’s at risk of being left behind or otherwise outperformed by others due to big data, the first response can be panic. The CEO or CMO may set a goal for the team – catch up. This can kick everyone into overdrive quickly, which is great. But it can also set everyone running in different directions with a vague charter to do something to change the business…now!

The tendency is to start chasing the Big Goal – maybe something dramatic like “reinvent the business”. For the organization new to big data, this is a recipe for trouble. Developing any new core competence takes time, and nobody starts as an expert. Learning to incorporate big data into your business is the same thing. It’s probably not realistic to expect a team accustomed to managing enterprise applications (which might all be running on a twenty-year-old technology stack) to learn massively parallel technologies, large scale data management and data science in a week. Or a month. Or a year.

So put one foot in front of the other. Don’t expect to master big data overnight, and instead take measured steps. Pick a project with a strong return on investment to get stakeholders on board and get the technology team’s feet wet in new technology. Then make the next project somewhat more ambitious. As the team learns more about delivering these projects, it’ll be much more natural to assess larger questions such as revising technology architecture.

It’s not too late
Marketing is marketing and reality is reality. Just because one of your competitors released a success story about their big data program last week doesn’t mean that there’s no benefit for your company. And when an article shows up online or in the printed media that declares that the big data war is over, and you lost if you’re not one of a handful of companies – take it with a huge grain of salt. There’s nothing wrong with a big data project that makes your business more profitable, or drives more top line revenue. And while it’s fun to contemplate reinventing your company, there are plenty of practical (and do-able) opportunities for improving revenue, customer experience, efficiency, etc. So don’t think for a moment that it’s too late.

Furthermore, by waiting a bit, organizations can take advantage of the learnings of others – things to do, things to avoid, and so forth. And the tools will usually improve. And successful use cases will become easier to spot. All these factors will reduce the risk to your big data project, and increase the likelihood of success. So it’s not too late.

To Accept or Not
Sadly, not all organizations make it to this stage. I’ve seen companies get stuck in finger pointing exercises, or trapped in endless cycles of ill-defined big data “science projects” that never seem to produce anything tangible and never end, or even put on blinders and avoid big data completely. But for companies who get to a place where they’re ready to accept the challenge, there are opportunities to meaningfully impact the business. And there are frequently increasing returns on well-crafted big data projects – which is to say that for every additional dollar spent over time, the value to the business actually increases. I’ve seen this cycle unfold time and time again, and in every single case of which I’m aware, the organization has reached the stage I’m referring to as “acceptance”, and is moving forward in a well-planned fashion with an effective big data program.

In fact, as I write this I’m listening to the HP Vertica Customer Advisory Board talk about their experiences to date with Vertica. And every one of them has approached their big data program in the ways described above. And every one of them has discovered increasing returns to their big data investment over time.

So put big data grief aside, accept that big data can help your business, and get started!

Is Big Data Giving You Grief? Part 4: Depression

“The problem is too big.  How can we possibly address it?”

Continuing the five part series which explores how organizations coping with big data often go through a process that closely resembles grief, this segment addresses the point at which the organization finally grasps the reality of big data and realizes the magnitude of the opportunity and challenge…and gets depressed about the reality of it.

Having seen this more than once, I’ve observed a few ways this shows in an organization.  Here are the most common reactions.

It’s too big

This reaction makes sense.  After all, as much as we in the industry say that “big data” is more than big and describe it with a laundry list of varying attributes, we all agree that it’s big.  It represents addressing data at a scale never before attempted by most organizations.  It represents analytic abilities perhaps never done before – and a capability pivot towards being an analytics-driven company.  And it may represent opportunities that are so big they appear to be nebulous: “If I capture ten thousand times as much data about my product, how does that translate into value?  Does that mean I’ll sell ten thousand times as many widgets?  How do I quantify the payoff?”

It may be challenging just to get a handle on the costs of a big data program for reasons mentioned in earlier parts of this series, much less the potential payoff.  This can make for a very challenging return-on-investment calculation.

We’re not ready

I believe I may have heard this particular form of worry more than anything else.  The infrastructure isn’t ready, the people aren’t ready to build big data applications, the business isn’t ready to consume the new data, and so on.  And, in fact, the company may not be prepared to size the big data effort because the team may not have the know-how for the ROI calculation (see above).  Also, the executive leadership may be unprepared to make a strategic wager on the program because of the uncertainty around the risks and benefits.

This can seem like a true show-stopper.  It’s not easy to change an organization.  Skills and technologies may not appear to be aligned with big data needs.  The various lines of business may not realize the ways they can improve or revolutionize their business.  The leadership team may be unaccustomed to making big bets on unproven technologies, or may believe that big data is a fad and will pass.

We’re too late

I hear this a lot too.  Everywhere a business turns today there’s a story about how someone has transformed their business, created new markets, broken old barriers, etc.  It’s easy to believe that all the opportunity is gone – that there’s no more benefit to tackling big data because it’s already been done.  It’s also easy to believe that it would be impossible to “catch up” with others because of all the time and effort required.

While this can be an intimidating belief, it can also be hard to characterize accurately.  After all, do you think your competitors will announce that the big data project they recently publicized in the media is a year late and $10M USD over budget?  Instead, they’ll play it up as if it’s a runaway success.  Vendors help this along too – who wouldn’t want to tout that their product helped a company?

So the saying goes – “The darkest hour is just before the dawn.”  Sage words written long before computers that apply to this situation.   But this is actually a positive place to be, because once a team has moved through anger, denial, bargaining , and into depression, it’s ready to come to terms with the situation and make an action plan to move forward.  I’ll discuss that next week in the final part of this series: acceptance.

Next week the series concludes with…acceptance.  “We can do this.”

Is Big Data Giving You Grief? Part 3: Bargaining

Is Big Data Giving You Grief?  Part 3: Bargaining

“Can’t we work with our current technologies (and vendors)? But they cost too much!”
Continuing the five part series about the stages of big-data grief that organizations experience, this segment focuses on the first time organizations explore the reality of the challenges and opportunities presented by big data and start to work their way forward…with bargaining.
Coping with a missed opportunity often brings some introspection. And with that comes the need to explore what-ifs that may provide a way forward. Here are some of the more common what-ifs that organizations explore during this phase.

What if I go to my current vendor? They’re sure to have some great technology. That’ll fix the problem.
This is a perfectly fine path of inquiry to explore. The only issue with this, as mentioned in my previous post (Part 2: Anger) is that vendors have a tendency to re-label their technology to suit a desirable market. So their technology offerings may not actually be suited to big data needs. And spending time and effort exploring these technologies to verify this can distract and prevent you from moving forward.

Also, vendors may have a business that relies on high-margin technology or services that were priced for a time before the big data explosion. So, the economics of their technology may suit them, but not the organization in need – your company. For example, if I need to store a petabyte of data in a data warehouse, I might require a several hundred node data warehouse cluster. If my current vendor charges a price of a hundred thousand US dollars per node, this isn’t economically feasible since I can now find alternatives that are purpose-built for large scale database processing and are priced at 1/5th or 1/10th that (or less!).

What if I hire some smart people? They’ll bring skills and insight. They’ll fix the problem.
Like the question above, this is a perfectly reasonable question to ask. But hiring bright people with the perfect skills can be very difficult today – the talent pool for big data is slim, and the hiring for these folks is highly competitive. Furthermore, hiring from outside doesn’t bring in the context of the business. In almost every business, there are nuances to the products, culture, market, and so forth that have a meaningful impact on the business. Hired guns, no matter how skilled, often lack this context.

Also, just bringing in new people doesn’t necessarily mean that your organization’s technology will suit them. Most analytic professionals develop their way of operating—their “game plan”—early in their career, and often prefer a particular set of technologies. It’s likely your new hires will want to introduce technologies they’re familiar with to your organization. And that can introduce additional complexity. A classic example of this is hiring a data science team who have spent the last decade analyzing data with the SAS system. If the organization doesn’t use SAS to begin with, the new team will likely press to introduce it.. And that may conflict with how the how the organization approaches analytics.

What if I download this cool open source software? I hear that stuff is magic, so that’ll fix the problem.
Unlike the first two what-ifs, this one should be approached with great caution! As mentioned in my previous post, open source software has something of a unique tendency to be associated with vague, broad, exaggerated, and often contradictory claims of functionality. This brings to mind a classic bit of satire by the Saturday Night Live crew, first aired in 1976: “New Shimmer is both a floor wax and a dessert topping!” The easy mistake to make here is for the technology team to rush forward, install the new stuff and start to experiment with it to the exclusion of all else. Six months (and several million dollars of staff time) later, the sunk cost in the open source option is so huge that it becomes a fait accompli. Careers would be damaged if the team admitted that it just wasted six months proving that the technology does not do what it claims, so it becomes the default choice.

What if I do what everybody else is doing? Crowds have wisdom, so that’ll fix the problem.
The risk with this thinking is similar to that posed by open source. This often goes hand-in-hand with hiring big data smarts – companies often bring in people from the outside and pay them to do what they’ve done elsewhere. It can definitely accelerate a big data program. But it can also guarantee that the efforts are more of a me-too duplication of something the rest of the industry has already done rather than true innovation. And while this may be suited for some businesses, the big money in big data is in being the first to derive new insights.

These are all perfectly acceptable questions that come up as organizations begin to acknowledge, for the first time, the reality of big data. But this isn’t the end of the discussion by any means. It’s important to avoid getting so enamored with exploring one or two of the above options that you don’t follow through on the “grief” process. But the natural next step is to be intimidated by the challenge, which will serve as an important reality check. I’ll cover this in the next segment: depression. So stay tuned!

Next up: Depression “The problem is too big. How can we possibly tackle it?”

Is Big Data Giving You Grief? Part 2: Anger

Is Big Data Giving You Grief? Part Two: Anger

“We missed our numbers last quarter because we’re not leveraging Big Data! How did we miss this?!”

Continuing this five part series focused on how organizations frequently go through the five stages of grief when confronting big data challenges, this post will focus on the second stage: anger.

It’s important to note that while an organization may begin confronting big data with something very like denial, anger usually isn’t far behind. As mentioned previously, very often the denial is rooted in the fact that the company doesn’t see the benefit in big data, or the benefits appear too expensive. And sometimes the denial can be rooted in a company’s own organizational inertia.

Moving past denial often entails learning – that big data is worth pursuing. Ideally, this learning comes from self-discovery and research – looking at the various opportunities it represents, casting a broad net as to technologies for addressing it, etc. Unfortunately, sometimes the learning can be much less pleasant as the competition learns big data first…and suddenly is performing much better. This can show up in a variety of ways – your competitors suddenly have products that seem much more aligned with what people want to buy; their customer service improves dramatically while their overhead actually goes down; and so on.

For better or worse, this learning often results in something that looks an awful lot like organizational “anger”. As I look back at my own career to my days before HP, I can recall more than a few all-hands meetings hosted by somber executives highlighting deteriorating financials, as well as meetings featuring a fist pounding leader or two talking about the need to change, dammit! It’s a natural part of the process wherein eyes are suddenly opened to the fact that change needs to occur. This anger often is focused at the parties involved in the situation. So, who’re the targets, and why?

The Leadership Team

At any company worth its salt, the buck stops with the leadership team. A shortcoming of the company is a shortcoming of the leadership. So self-reflection would be a natural focus of anger. How did a team of experienced business leaders miss this? Companies task leaders with both the strategic and operational guidance of the business – so if they missed a big opportunity in big data, or shot it down because it looked to costly or risky, this is often seen as a problem.

Not to let anybody off the hook, but company leadership is also tasked with a responsibility to the investors. And this varies with the type of company, stage in the market, etc. In an organization tasked with steady growth, taking chances on something which appears risky – like a big data project where the benefits are less understood than the costs – is often discouraged. Also, leaders often develop their own “playbook” – their way of viewing and running a business that works. And not that many retool their skills and thinking over time. So their playbook might’ve worked great when brand value was determined by commercial airtime, and social media was word of mouth from a tradeshow. But the types and volume of information available are changing rapidly in the big data world, so that playbook may be obsolete.

Also, innovation is as much art as science. This is something near & dear to me both in my educational background as well as career interests. If innovation was a competence that could just be taught or bought, we wouldn’t see a constant flow of companies appearing (and disappearing) across markets. We also wouldn’t see new ideas (the web! social networking!) appear overnight to upend entire segments of the economy. For most firms, recognizing the possibilities inherent in big data and acting on those possibilities represents innovation, so it’s not surprising to see that some leadership teams struggle.

The Staff

There are times when the upset over a missed big data opportunity is aimed at the staff. It’s not unusual to see a situation where the CEO of a firm asked IT to research big data opportunities, only to have the team come back and state that they weren’t worthwhile. And six months later, after discovering that the competition is eating their lunch, the CEO is a bit upset at the IT team.

While this is sometimes due to teams being “in the bunker” (see my previous post here), in my experience it occurs far more often due to the IT comfort zone. Early in my career, I worked in IT for a human resources department. The leader of the department asked a group of us to research new opportunities for the delivery of information to the HR team across a large geographic area (yeah, I’m dating myself a bit here…this was in the very early days of the web). We were all very excited about it, so we ran back to our desks and proceeded to install a bunch of software to see what it could do. In retrospect I have to laugh at myself about this – it never occurred to me to have a conversation with the stakeholders first! My first thought was to install the technology and experiment with it, then build something.

This is probably the most common issue I see in IT today. The technologies are different but the practice is the same. Ask a room full of techies to research big data with no business context and…they’ll go set up a bunch of technology and see what it can do! Will the solution meet the needs of the business? Hmm. Given the historical failure rate of large IT projects, probably not.

The Vendors

It’s a given that the vendors might get the initial blame for missing a big data opportunity. After all, they’re supposed to sell us stuff that solves our problems, aren’t they? As it turns out, that’s not exactly right. What they’re really selling us is stuff that solves problems for which their technology was built. Why? Well, that’s a longer discussion that Clayton Christensen has addressed far better than I ever could in “The Innovator’s Dilemma”. Suffice it to say that the world of computing technology continues to change rapidly today, and products built twenty years ago to handle data often are hobbled by their legacy – both in the technology and the organization that sells it.

But if a company is writing a large check every year to a vendor – it’s not at all unusual to see firms spend $1 million or more per year with technology vendors – they often expect a measure of thought leadership from that vendor. So if a company is blindsided by bad results because they’re behind on big data, it’s natural to expect that the vendor should have offered some guidance, even if it was just to steer the IT folks away from an unproductive big data science project (for more on that, see my blog post coming soon titled “That Giant Sucking Sound is Your Big Data Lab Experiment”).

Moving past anger

Organizational anger can be a real time-waster. Sometimes, assigning blame can gain enough momentum that it distracts from the original issue. Here are some thoughts on moving past this.

You can’t change the past, only the future. Learning from mistakes is a positive thing, but there’s a difference between looking at the causes and looking for folks to blame. And it’s critical to identify the real reasons the opportunity was missed instead of playing the “blame game”, as it would suck up precious time and in fact may prevent the identification of the real issue. I’ve seen more than one organization with what I call a “Teflon team” – a team which is never held responsible for any of the impacts their work has on the business, regardless of their track record. Once or twice, I’ve seen these teams do very poor work, but the responsibility has been placed elsewhere. So the team never improves and the poor work continues. So watch out for the Teflon team!

Big data is bigger than you think. It’s big in every sense of the word because it represents not just the things we usually talk about – volume of data, variety of data, and velocity of data – but it also represents the ability to bring computing to bear on problems where this was previously impossible. This is not an incremental or evolutionary opportunity, but a revolutionary one. Can a business improve its bottom line by ten percent with big data? Very likely. Can it drive more revenue? Almost certainly. But it can also develop entirely new products and capabilities, and even create new markets.

So it’s not surprising that businesses may have a hard time recognizing this and coping with it. Business leaders accustomed to thinking of incremental boosts to revenue, productivity, margins, etc. may not be ready to see the possibilities. And the IT team is likely to be even less prepared. So while it may take some convincing to get the VP of Marketing to accept that Twitter is a powerful tool for evaluating their brand, asking IT to evaluate it in a vacuum is a recipe for confusion.

So understanding the true scope of big data and what it means for an organization is critical to moving forward.

A vendor is a vendor. Most organizations have one or more data warehouses today, along with a variety of tools for the manipulation, transformation, delivery, analysis, and consumption of data. So they will almost always have some existing vendor relationships around technologies which manage data. And most of them will want to leverage the excitement around big data, so will have some message along those lines. But it’s important to separate the technology from the message. And to distinguish between aging technology which has simply been rebranded and technology which can actually do the job.

Also, particularly in big data, there are “vendorless” or “vendor-lite” technologies which have become quite popular. By this I mean technologies such as Apache Hadoop, Mongodb, Cassandra, etc. These are often driven less by a vendor with a product goal and more by a community of developers who cut their teeth on the concept of open-source software which comes with very different business economics. Generally without a single marketing department to control the message, these technologies can be associated with all manner of claims regarding capabilities – some of which are accurate, and some which aren’t. This is a tough issue to confront because the messages can be conflicting, diffused, etc. The best advice I’ve got here is – if an open source technology sounds too good to be true, it very likely is.

Fortunately, this phase is a transitional one. Having come to terms with anger over the missed big data opportunity or risk, businesses then start to move forward…only to find their way blocked. This is when the bargaining starts. So stay tuned!

Next up: Bargaining “Can’t we work with our current technologies (and vendors)? …but they cost too much!”

Meet the team: Ben Vandiver

This week I sat down with Ben Vandiver, a Vertica veteran who’s been with the company since 2008, and talked about everything from influencing presidential elections, making an impact, and sword-fighting with interns.

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