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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.

The Automagic Pixie

The “De-mythification” Series

Part 4: The Automagic Pixie

Au∙to∙mag∙ic: (Of a usually complicated technical or computer process) done, operating, or happening in a way that is hidden from or not understood by the user, and in that sense, apparently “magical”

[Source: Dictionary.com]

In previous installments of this series, I de-bunked some of the more common myths around big data analytics. In this final installment, I’ll address one of the most pervasive and costly myths: that there exists an easy button that organizations can press to automagically solve their big data problems. I’ll provide some insights as to how this myth has come about, and recommend strategies for dealing with the real challenges inherent in big data analytics.

Like the single-solution elf, this easy button idea is born of the desire of many vendors to simplify their message. The big data marketplace is new enough that all the distinct types of needs haven’t yet become entirely clear – which makes it tough to formulate a targeted message. Remember in the late 1990’s when various web vendors were all selling “e-commerce” or “narrowcasting” or “recontextualization”? Today most people are clear on the utility of the first two, while the third is recognized for what it was at the time – unhelpful marketing fluff. I worked with a few of these firms, and watched as the businesses tried to position product for a need which hadn’t yet been very well defined by the marketplace. The typical response by the business was to keep it simple – just push the easy button and our technology will do it for you.

I was at my second startup in 2001 (an e-commerce provider using what we would refer to today as a SaaS model) when I encountered the unfortunate aftermath of this approach. I sat down at my desk on the first day of the job, and was promptly approached by the VP of Engineering, who informed me that our largest customer was about to cancel its contract – we’d been trying to upgrade the customer for weeks, during which time their e-commerce system was down. Although they’d informed the customer that the upgrade was a push-button process, it wasn’t. In fact, at the time I started there, the team was starting to believe that an upgrade would be impossible and that they should propose re-implementing the customer from scratch. By any standard, this would be a fail.

Over the next 72 hours, I migrated the customer’s data and got them up and running.   It was a Pyrrhic victory at best – the customer cancelled anyhow, and the startup went out of business a few months later.

The moral of the story? No, it’s not to keep serious data geeks on staff to do automagical migrations. The lesson here is that when it comes to data driven applications – including analytics – the “too good to be true” easy button almost always is. Today, the big data marketplace is full of great sounding messages such as “up and running in minutes”, or “data scientist in a box”.

“Push a button and deploy a big data infrastructure in minutes to grind through that ten petabytes of data sitting on your SAN!”

“Automatically derive predictive models that used to take the data science team weeks in mere seconds! (…and then fire the expensive data scientists)!”

Don’t these sound great?

The truth is, as usual, more nuanced. One key point I like to make with organizations is that big data analytics, like most technology practices, involves different tasks. And those tasks generally require different tools. To illustrate this for business stakeholders, I usually resort to the metaphor of building a house. We don’t build a house with just a hammer, or just a screwdriver. In fact, it requires a variety of tools – each of which is suited to a different task. A brad nailer for drywall. A circular saw for cutting. A framing hammer for framing. And so on. And in the world of engineering, a house is a relatively simple thing to construct. A big data infrastructure is considerably more complex. So it’s reasonable to assume that an organization building this infrastructure would need a rich set of tools and technologies to meet the different needs.

Now that we’ve clarified this, we can get to the question behind the question. When someone asks me “Why can’t we have an easy button to build and deploy analytics?” What they’re really asking is “How can I use technological advances to build and deploy analytics faster, better and cheaper?

Ahh, now that’s an actionable question!

In the information technology industry, we’ve been blessed (some would argue cursed) by the nature of computing. For decades now we’ve been able to count on continually increasing capacity and efficiency. So while processors continue to grow more powerful, they also consume less power. As the power requirements for a given unit of processing become low enough, it is suddenly possible to design computing devices which run on “ambient” energy from light, heat, motion, etc. This has opened up a very broad set of possibilities to instrument the world in ways never before seen – resulting in dramatic growth of machine-readable data. This data explosion has led to continued opportunity and innovation across the big data marketplace. Imagine if each year, a homebuilder could purchase a saw which could cut twice as much wood with a battery half the size. What would that mean for the homebuilder? How about the vendor of the saw? That’s roughly analogous to what we all face in big data.

And while we won’t find one “easy button”, it’s very likely that we can find a tool for a given analytic task which is significantly better than one that was built in the past. A database that operates well at petabyte scale, with performance characteristics that make it practical to use. A distributed filesystem whose economics make it a useful place to store virtually unlimited amounts of data until you need it. An engine capable of extracting machine-readable structured information from media. And so on. Once my colleagues and I have debunked the myth of the automagic pixie, we can have a productive conversation to identify the tools and technologies that map cleanly to the needs of an organization and can offer meaningful improvements in their analytical capability.

I hope readers have found this series useful. In my years in this space, I’ve learned that in order to move forward with effective technology selection, sometimes we have to begin by taking a step backward and undoing misconceptions. And there are plenty! So stay tuned.

The Single-Solution Elf

The “De-mythification” Series

Part 3: The Single-Solution Elf

In this part of the de-mythification series, I’ll address another common misconception in the big data marketplace: that there exists a single piece of technology that will solve all big data problems. Whereas the first two entries in this series focused on market needs, this will focus more on the vendor side of things in terms of how big data has driven technology development, and give some practical guidance on how an organization can better align their needs with their technology purchases.

Big Data is the Tail Wagging the Vendor

Big data is in the process of flipping certain technology markets upside-down. Ten or so years ago, vendors of databases, ETL, data analysis, etc. all could focus on building tools and technologies for discrete needs, with an evolutionary eye – focused on incremental advance and improvement. That’s all changed very quickly as the world has become much more instrumented. Smartphones are a great example. Pre-smartphone, the data stream from an individual throughout the day might consist of a handful of call-detail records and a few phone status records. Maybe a few kilobytes of data at most. The smartphone changed that. Today a smartphone user may generate megabytes, or even gigabytes of data in a single day from the phone, the broadband, the OS, email, applications, etc. Multiply that across a variety of devices, instruments, applications and systems, and the result is a slice of what we commonly refer to as “Big Data”.

Most of the commentary on big data has focused on the impact to organizations. But vendors have been, in many cases, blindsided. With technology designed for orders of magnitude less data, sales teams accustomed to competing against a short list of well-established competitors, marketing messages focused on clearly identified use cases, and product pricing and packaging oriented towards a mature, slow-growth market, many have struggled to adapt and keep up.

Vendors have responded with updated product taglines (and product packaging) which often read like this:

“End-to-end package for big data storage, acquisition and analysis”

“A single platform for all your big data needs”

“Store and analyze everything”

Don’t these sound great?

But simple messages like these mask the reality that there are distinct activities that which comprise big data analytics, and that these activities come with different technology requirements, and much of today’s technology was born in a very different time – so the likelihood of there being a single tool that does everything well is quite low. Let’s start with the analytic lifecycle, depicted in the figure below, and discuss the ways this has driven the state of the technology.

analytic_lifecycle

This depicts the various phases of an analytic lifecycle from the creation and acquisition of data through the exploration and structuring to analysis and modeling, to putting the information to work. These phases often require very different things from technology. Let’s take the example of acquiring and storing of large volumes of data with varying structure. Batch performance is often important here, as is cost to scale. Somewhat less important is ease of use – load jobs tend to change at a lower rate than user queries, especially when the data in a document-like format (e.g. JSON). By contrast, the development of a predictive model requires a highly interactive technology which combines high performance with a rich analytic toolkit. So batch use will be minimal, while ease of use is key.

Historically, many of the technologies required for big data analytics were built as stand-alone technologies: a database, a data mining tool, an ETL tool, etc. Because of this lineage, the time and effort required to re-engineer these tools to work effectively together as a single technology, with orders of magnitude more data, can be significant.

Despite how a vendor packages technology, organizations must ask themselves this question: what do you really need to solve the business problems? When it comes time to start identifying a technology portfolio to address big data challenges, I always recommend that customers start by putting things in terms of what they really need. This is surprisingly uncommon, because many organizations have grown accustomed to vendor messaging which is focused on what the vendor wants to sell as opposed as to what the customer needs to buy. It may seem like a subtle distinction, but it can make all the difference between a successful project and a very expensive set of technology sitting on the shelf unused.

I recommend engaging in a thoughtful dialog with vendors to assess not only what you need today, but to explore things you might find helpful which you haven’t thought of yet. A good vendor will help you in this process. As part of this exercise, it’s important to avoid getting hung up on the notion that there’s one single piece of technology that will solve all your problems: the single solution elf.

Once my colleagues and I dispel the single solution myth, we can then have a meaningful dialog with an organization and focus on the real goal: finding the best way to solve their problems with a technology portfolio which is sustainable and agile.

I’ve been asked, more than once “Why can’t there be a single solution? Things would be so much easier that way.” That’s a great question, which I’ll address in my next blog post as I discuss some common sense perspectives on what technology should – and shouldn’t – do for you.

Next up: The Automagic Pixie

 

Can Big Data Analytics Save Our World?

CI image

If you ask Conservation International this question, they may just say yes. After all, Conservation International has teamed up with HP Earth Insights to provide organizations around the world — from environmentalists to policy makers – with a real-time look at what is happening within our planets most valuable natural resource: the rain forest.

But how does their work relate to you as a start-up organization or a Fortune 500 company?
First, they have surprisingly similar analytical needs to many other start-ups and corporations, collecting data regularly from 16 sites around the globe, performing more than 4 million climate measurements as of this February, and managing more than 3 TB of biodiversity information. As the name implies, this information is incredibly, well… diverse, including everything from photos to hand-recorded measurements to weather station and camera trap imagery. While your company may not be recording/analyzing the metadata of candid photos of elephants and/or chimpanzees, chances are, many of you out there are working with at least more than one type of data.

Collecting and Analyzing Multiple Data Types
All of these different data types have to be funneled into a database, analyzed, and then acted on. Running queries based on millions of climate readings begins to look a lot like doing the same on a diverse customer base like many other companies deal with every day. Many agricultural companies collect sensor data from across their farm lands to get a forecast of how the climate has affected their crops for the upcoming year. These days, utilities companies are launching Advanced Metering Infrastructures (AMI) to deal with the staggering amounts of sensor data collected from the energy usage of millions of homes. HP Vertica coincidentally works as an effective Meter Data Management (MDM) system (read more here).

Visualizing the Data and Reaching More People
Working with HP, Conservation International has built from the ground up their own analytics system and dashboard for visualizing their data from all 16 rainforests around the globe. CI DBA’s discover trends based on over 140 million simulations, and analyze the metadata from over 1.7 million photos. Not only is their custom interface intuitive, it also enables them to generate PDFs instantly and share to social media directly from the dashboard. For CI, this means more people now see more of their impact in more places to proactively address environment threats. For you, it might mean anything from less time spent prepping your data to present to management, or just simply fewer emails to send.

The Power of Prediction for the Greater Good
Like many companies, CI uses standard methodology in processing their data, and uses R for their analysis, as is very common in scientific studies. Using R, CI can proactively assess where the future trouble spots will be, and what parts of their monitored ecosystems are most threatened. Many other HP Vertica customers use R in surprisingly similar ways, such as seeing what neighborhoods a future power outage might affect most, or how serious the next year’s dry season will be to a farmer’s crops

See Conservation International at the HP Vertica Big Data Conference
These are just a few examples of how an incredibly unique organization uses HP Vertica to analyze unique data, yet does it in ways that many other groups might find surprisingly familiar. Sometimes after a closer look, we can see that many organizations have a lot more in common with their data needs than they may think, and HP Vertica is the right tool for the job.

Be sure to attend out upcoming Big Data Conference in Boston MA, where Conservation International is leading the hackathon!

HP Vertica @ Boston TechJam!

photo (1)

Yesterday, myself and a few other fellow members of the HP Vertica team attended Boston TechJam 2014 at the city hall plaza in Boston. Featuring a digital art display by local artist Cindy Bishop entitled “The Way You Move”, our booth was thronged with people wanting to know more about what we do as the leading big data analytics platform. Myself and the rest of my team wanted to send out a huge thank you to everyone who stopped by our booth to talk with us. I personally had an amazing time interacting the rest of the tech community here in beautiful Boston, getting a chance to talk to everyone from up and coming innovators to grizzled tech veterans, (some of whom may be joining our ranks in the future!)

Below are some pictures I snapped of the festivities (when there was a rare break in between people coming up to the booth). I’m already looking forward to next year!

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