Monday, August 24, 2015

Rolta AdvizeX experts on hastening big data analytics in healthcare and retail

The next BriefingsDirect big data innovation case study interview highlights how Rolta AdvizeX in Independence, Ohio creates analytics-driven solutions in the healthcare and retail industries.

We'll also delve into how the right balance between open-source and commercial IT products helps in creating a big-data capability, and we'll further explore how converged infrastructure solutions are hastening the path to big-data business value and cloud deployment options.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy.

To learn more about how big data can be harnessed for analysis benefits in healthcare and retail, please join me in welcoming our guests, Dennis Faucher, Enterprise Architect at Rolta AdvizeX, and Raajan Narayanan, Data Scientist at Rolta AdvizeX. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Dennis, what makes big data so beneficial and so impactful for the healthcare and retail sectors?

Faucher: What we're finding at Rolta AdvizeX is that our customers in healthcare and retail have always had a lot of data to make business decisions, but what they're finding now is that they want to make real-time decisions -- but they've never been able to do that. There was too much data, it took too long to process, and maybe the best they could do was get weekly or maybe monthly information to improve their businesses.

We're finding that the most successful healthcare and retail organizations are now making real-time decisions based upon the data that's coming in every second to their organization.

Gardner: So it's more, faster, and deeper, but is there anything specific about healthcare, for example? What are some top trends that are driving that?

Two sides of healthcare

Faucher: You have two sides of healthcare, even if it's a not-for-profit organization. Of course, they're looking for better care for their patients. In the research arms of hospitals, the research arms of pharmaceutical companies, and even on the payer side, the insurance companies, there is a lot of research being done into better healthcare for the patient, both to increase people's health, as well as to reduce long-term costs. So you have that side, which is better health for patients.

On the flip side, which is somewhat related to that, is how to provide customers with new services and new healthcare, which can be very, very expensive. How can they do that in a cost-effective manner?
Learn more about Rolta AdvizeX Solutions
For the Retail Industry

And for Healthcare Companies
So it's either accessing research more cost-effectively or looking at their entire pipeline with big data to reduce cost, whether it's providing care or creating new drugs for their patients.

Gardner: And, of course, retail is such a dynamic industry right now. Things are changing very rapidly. They're probably interested in knowing what's going on as soon as possible, maybe even starting to get proactive in terms of what they can anticipate in order to solve their issues.

Faucher: There are also two sides to retail as well. One is the traditional question of, How can I replenish my outlets in real time? How can I get product to the shelf before it runs out? Then, there's also the traditional side of the cross-sell, up-sell, and what am I selling in a shopping cart, to try to get the best mix of products within a shopping cart that will maximize my profitability for each customer.

Those are the types of decisions our customers in retail have been making for the last 30-50 years, but now they have even more data to help them with that. It's not just the typical sales data that they're getting from the registers or from online, but now we can go into social media as well and get sentiment analysis for customers to see what products they're really interested in to help with stocking those shelves, either the virtual shelves or the physical shelves.
So it's either accessing research more cost-effectively or looking at their entire pipeline with big data to reduce cost.

The second side, besides just merchandising and that market-basket analysis, is new channels for consumers. What are the new channels? If I'm a traditional brick-and-mortar retailer, what are the new channels that I want to get into to expand my customer base, rather than just the person who can physically walk in, but across many, many channels?

There are so many channels now that retailers can sell to. There is, of course, their online store, but there may be some unique channels, like Twitter and Facebook adding a "buy" button. Maybe they can place products within a virtual environment, within a game, for customers to buy. There are many different areas to add channels for purchase and to be able to find out real-time what are people buying, where they're buying, and also what they're likely to buy. Big data really helps with those areas in retail.

Gardner: Raajan, there are clearly some compelling reasons for looking at just these two specific vertical industries to get better data and be more data-driven. The desire must be there, even the cost efficiencies are more compelling than just a few years ago. What’s the hurdle? What prevents them from getting to this goal of proactive, and to the insights that Dennis just described?

Main challenge

Narayanan: One of the main challenges that organizations have is to use the current infrastructure for analytics. The three Vs: velocity, variety and the volume of data serve up a few challenges for organizations in terms of how much data I can store, where do I store it, and do I have the current infrastructure to do that?

In a traditional business, versus the new flash areas, how do you best access the data? How fast you need to access the data is one of the challenges that organizations have.

In addition, there are lots of analytics tools out there. The ecosystem is growing by the day. There are a few hundred offerings out there and they are all excellent platforms to use. So the choice of what kind of analytics I need for the set purpose is the bigger challenge. To identify the right tool and the right platform that would serve my organization needs would be one of the challenges.

The third challenge would be to have the workforce or the expertise to build these analytics or have organizations to address these challenges from an analytical standpoint. This is one of the key challenges that organizations have.

Gardner: Dennis, as an enterprise architect at Rolta AdvizeX, you must work with clients who come at this data issue compartmentalized. Perhaps marketing did it one way; R and D did it another; supply chain and internal business operations may have done it a different way. But it seems to me that we need to find more of a general, comprehensive approach to big data analytics that would apply to all of those organizations.
We work with a company, look at everything they're doing, and set a roadmap for the next three years to meet their short-term and long-term goals.

Is there some of that going on, where people are looking not just for a one-off solution different for each facet of their company, but perhaps something more comprehensive, particularly as we think about more volume coming with the Internet of Things (IoT) and more data coming in through more mobile use? How do we get people to think about big-data infrastructure, rather than big-data applications?

Faucher: There are so many solutions around data analytics, business intelligence (BI), big data, and data warehouse. Many of them work, and our customers unfortunately have many of them and they have created these silos of information, where they really aren’t getting the benefits that they had hoped for.

What we're doing with customers from an enterprise architecture standpoint is looking at the organization holistically. We have a process called Advizer, where we work with a company, look at everything they're doing, and set a roadmap for the next three years to meet their short-term and long-term goals.

And what we find when we do our interviews with the business people and the IT people at companies is that their goals as an organization are pretty clear, because they've been set by the head of the organization, either the CEO or the chief scientist, or the chief medical director in healthcare. They have very clear goals, but IT is not aligned to those goals and it’s not aligned holistically.

Not organized

There could be skunk works that are bringing up some big-data initiatives. There could be some corporate-sponsored big data, but they're just not organized. All it takes is for us to get the business owners and the IT owners in a room for a few hours to a few days, where we can all agree on that single path to meet all needs, to simplify their big data initiatives, but also get the time to value much faster.

That’s been very helpful to our customers, to have an organization like Rolta AdvizeX come in as an impartial third-party and facilitate the coming together of business and IT. Many times, as short as a month, we have the three-year strategy that they need to realize the benefits of big data for their organization.

Gardner: Dennis, please take a moment to tell us a little bit more about AdvizeX and Rolta.
We don’t lead with products. We develop solutions and strategy for our customers.

Faucher: Rolta AdviseX, is an international systems integrator. Our US headquarters is in Independence, Ohio, just outside of Cleveland. Our international headquarters are in Mumbai, India.

As a systems integrator, we lead with our consultants and our technologists to build solutions for our customers. We don’t lead with products. We develop solutions and strategy for our customers.

There are four areas where we find our customers get the greatest value from Rolta AdvizeX. At the highest level are our advisory services, which I mentioned, which set a three-year roadmap for areas like big data, mobility, or cloud.

The second area is the application side. We have very strong application people at any level for Microsoft, SAP, and Oracle. We've been helping customers for years in those areas.

The third of the four areas is infrastructure. As our customers are looking to simplify and automate their private cloud, as well as to go to public cloud and software as a service (SaaS), how do they integrate all of that, automate it, and make sure they're meeting compliance.

The fourth area, which has provided a lot of value for our customers, is managed services. How do I expand my IT organization to a 7x24 organization when I'm really not allowed to hire more staff? What if I could have some external resources taking my organization from a single shift to three shifts, managing my IT 7x24, making sure it’s secure, making sure it’s patched, and making sure it’s reliable?

Those are the four major areas that we deliver as a systems integrator for our customers.

Data scientists

Gardner: Raajan, we've heard from Dennis about how to look at this from an enterprise architecture perspective, taking the bigger picture into account, but what about data scientists? I hear frequently in big data discussions that companies, in this case in healthcare and retail, need to bring that data scientist function into their organizations more fully. This isn't to put down the data analysts or business analysts. What is it about being a data scientist that is now so important? Why, at this point, would you want to have data scientists in your organization?

Narayanan: One of the key functions of a data scientist is to be able to look at data proactively. In a traditional sense, a data analyst's job is reflective. They look at transactional data in a traditional manner, which is quite reflective. Bringing in a data scientist or a data-scientist function can help you build predictive models on existing data. You need a lot of statistical modeling and a lot of the other statistical tools that will help you get there.

This function has been in organizations for a while, but it’s more formalized these days. You need a data scientist in an organization to perform more of the predictive functions than the traditional reporting functions.
We're seeing that in the open-source, big-data tools as well. Customers have embraced open-source big-data tools rapidly.

Gardner: So, we've established that big data is important. It’s huge for certain verticals, healthcare and retail among them. Organizations want to get to it fast. They should be thinking generally, for the long term. They should be thinking about larger volumes and more velocity, and they need to start thinking as data scientists in order to get out in front of trends rather than be reactive to them.

So with that, Dennis, what’s the role of open source when one is thinking about that architecture and that platform? As a systems integrator and as enterprise architect, what do you see as the relationship between going to open source and taking advantage of that, which many organizations I know are doing, but also looking at how to get the best results quickly for the best overall value? Where does the rubber hit the road best with open source versus commercial?

Faucher: That’s an excellent question and one that many of our customers have been grappling with as there are so many fantastic open-source, big-data platforms out there that were written by Yahoo, Facebook, and Google for their own use, yet written open source for anyone to use.

I see a little bit of an analogy to Linux back in 1993, when it really started to hit the market. Linux was a free alternative to Unix. Customers were embracing it rapidly trying to figure out how it could fit in, because Linux had a much different cost model than proprietary Unix.

We're seeing that in the open-source, big-data tools as well. Customers have embraced open-source big-data tools rapidly. These tools are free, but just like Linux back then, the tools are coming out without established support organizations. Red Hat emerged to support the Linux open-source world and say that they would help support you, answer your phone calls, and hold your hand if you needed help.

Now we're seeing who are going to be the corporate sponsors of some of these open-source big data tools for customers who may not have thousands of engineers on staff to support open source. Open-source tools definitely have their place. They're very good for storing the reams and reams, terabytes, petabytes, and more of data out there, and to search in a batch manner, not real time, as I was speaking about before.

Real-time analytics

Some of our customers are looking for real-time analytics, not just batch. In batch, you ask a question and will get the answer back eventually, which many of the open-source, big-data tools are really meant for. How can I store a lot of data inexpensively that I may need access to at some point?

We're seeing that our customers have this mix of open-source, big-data tools, as well as commercial big-data tools.

I recently participated in a customer panel where some of the largest dot-coms talked about what they're doing with open source versus commercial tools. They were saying that the open-source tools was where they may have stored their data lake, but they were using commercial tools to access that data in real time.

They were saying that if you need real-time access, you need a big-data tool that takes in data in parallel and also retrieves it in a parallel manner, and the best tools to do that are still in the commercial realm. So they have both open source for storage and closed source for retrieval to get the real-time answers that they need to run their business.

Gardner: And are there any particular platforms on the commercial side that you're working with, particularly on that streaming, real-time, at volume, at scale equation?
Learn more about Rolta AdvizeX Solutions
For the Retail Industry

And for Healthcare Companies
Faucher: What we see on our side with the partners that we work with is that HP Vertica is the king of that parallel query. It’s extremely fast to get data in and get data out, as well as it was built on columnar, which is a different way to store data than relational is. It was really meant to get those unexpected queries. Who knows what the query is going to be? Whatever it is, we'll be able to respond to it.

Another very popular platform has been SAP HANA, mostly for our SAP customers who need an in-memory columnar database to get real-time data access information. Raajan works with these tools on a daily basis and can probably provide more detail on that, as well as some of the customer examples that we've had.

Gardner: Raajan, please, if you have some insight into what’s working in these verticals and any examples of how organizations are getting their big data payoff, I'd be very curious to hear that.

Narayanan: One of the biggest challenges is to be able to discover the data in the shortest amount of time, and I mean discovery in the sense that I get data into the systems, and how fast I can get some meaningful insights.

Works two ways

It works two ways. One is to get the data into the system, aggregate it into your current environment, transform it so that data is harmonious across all the data sources that provide it, and then also to provide analytics over that.

In a traditional sense, I'll collect tons and tons of data. It goes through reams and reams of storage. Do I need all that data? That's the question that has to be answered. Data discovery is becoming a science as we speak. When I get the data, I need to see if this data is useful, and if so, how do I process it.

These systems, as Dennis alluded to, Vertica and SAP HANA, enable that data discovery right from the get-go. When I get data in, I can just write simple queries. I don't need a new form of analytic expertise. I can use traditional SQL to query on this data. Once I've done that, then if I find the data useful, I can send it into storage and do a little bit more robust analytics over that, which can be predictive or reporting in nature.

A few customers see a lot of value in data discovery. The whole equation of getting in Hadoop as a data lake is fantastic, and these platforms play very well with the Hadoop technologies out there.
Once you get data into these platforms, they provide analytic capabilities that go above and beyond what a lot of the open-source platforms provide.

Once you get data into these platforms, they provide analytic capabilities that go above and beyond what a lot of the open-source platforms provide. I'm not saying that open source platforms don’t perform these functions, but there are lots of tools out there that you need to line up in sequence for them to perform what Vertica or SAP HANA will do. The use cases are pretty different, but nevertheless, these platforms actually enable lot of these functions.

Gardner: Raajan, earlier in our discussion you mentioned the importance of skills and being able to hire enough people to do the job. Is that also an issue in making a decision between an open-source and a commercial approach?

Narayanan: Absolutely. With open source, there are a lot of code bases out there that needs to be learned. So there is a learning curve within organizations.

Traditionally, organizations rely more on the reporting function. So they have a lot of the SQL functions within the organization. To retrain them is something that an organization would have to think about. Then, even to staff for new technologies is something that an organization would have to cater for in the future. So it’s something that an organization would have to plan in their roadmap for big-data growth.

Gardner: Dennis, we can back at the speed and value and getting your big data apparatus up and running, perhaps think about it holistically across multiple departments in your organization, and anticipate even larger scale over time, necessitating a path to growth. Tell us a little bit about what's going on in the market with converged infrastructure, where we're looking at very tight integration between hardware and software, between servers that are supporting workloads, usually virtualized, as well as storage also usually virtualized.

For big data, the storage equation is not trivial. It’s an integral part of being able to deliver those performance requirements and key performance indicators (KPIs). Tell us a bit about why converged infrastructure makes sense and where you're seeing it deployed?

Three options

Faucher: What we're seeing with our customers in 2015 is that they have three options for where to run their applications. They have what we call best-of-breed, which is what they've done forever. They buy some servers from someone, some storage from someone else, some networking from someone else, and some software from someone else. They put it together, and it’s very time-consuming to implement it and support it.

They also have the option of going converged, which is buying the entire stack -- the server, the storage, and the networking -- from a single organization, which will both factory integrate it, load their software for them, show up, plug it in, and you are in production in less than 30 days.

The third option, of course, is going to cloud, whether that’s infrastructure as a service (IaaS) or SaaS, which can also provide quick time to value.

For most of our customers now, there are certain workloads that they are just not ready to run in IaaS or SaaS, either because of cost, security, or compliance reasons. For those workloads that they have decided are not ready for Saas, IaaS, or platform as a service (PaaS) yet, they need to put something in their own data center. About 90 percent of the time, they're going with converged.
Our customers’ data centers are getting so much bigger and more complex that they just cannot maintain all of the moving parts.

Beside the fact that it’s faster to implement, and easier to support, our customers’ data centers are getting so much bigger and more complex that they just cannot maintain all of the moving parts. Thousands of virtual machines and hundreds of servers and all the patching needs to happen, and keeping track of interoperability between server A, network B, and storage C. The converged takes that all away from them and just pushes it to the organizations they bought it from.

Now, they can just focus on their application and their users which is what they always wanted to focus on and not have to focus on the infrastructure and keeping the infrastructure running.

So converged infrastructure has really taken off very, very quickly with our customers. I would say even faster than I would have expected. So it's either converged -- they're buying servers and storage and networking from one company, which both pre-installs it at a factory and maintains it long-term -- or hyper-converged, where all of the server and storage and networking is actually done in software on industry-standard hardware.

For private cloud, a large majority of our customers are going with converged for the pieces that are not going to public cloud.

Gardner: So 90 percent; that’s pretty impressive. I'm curious if that’s the rate of adoption for converged, what sort of rate of adoption are you seeing on the hyper-converged side where it’s as you say software-defined throughout?

Looking at hyper-converged

Faucher: It’s interesting. All of our customers are looking at hyper-converged right now to figure out where it is it fits for them. The thing about hyper-converged, where it’s just industry standard servers that I'm virtualizing for my servers and storage and networking, is where does hyper-converged fit? Sometimes, it definitely has a much lower entry point. So they'll look at it and say, "Is that right for my tier-1 data center? Maybe I need something that starts bigger and scales bigger in my tier-1 data center."

Hyper-converged may be a better fit for tier-2 data centers, or possibly in remote locations. Maybe in doctor's offices or my remote retail branches, they go with hyper-converged, which is a smaller unit, but also very easy to support, which is great for those remote locations.

You also have to think that hyper-converged, although very easy to procure and deploy, when you grow it, you only grow it in one size block. It’s like this block that can run 200 virtual machines, but when I add, I have to add 200 at a time, versus a smaller granularity.

So it’s important to make the correct decision. We spend a lot of time with our customers helping them figure out the right strategy. If we've decided that converged is right, is it converged or is it hyper-converged for the application? Now, as I said, it typically breaks down to for those tier 1 data centers it’s converged, but for those tier 2 data centers or those remote locations, it’s more likely hyper-converged.
But some of the vendors that provide cloud, hyper-converged and converged, have come up with some great solutions for rapid scalability.

Gardner: Again, putting on your enterprise architect hat, given that we have many times unpredictable loads on that volume and even velocity for big data, is there an added value, a benefit, of going converged and perhaps ultimately hyper-converged in terms of adapting to demand or being fit for purpose, trying to anticipate growth, but not have to put too much capital upfront and perhaps miss where the hockey puck is going to be type of thinking?

What is it about converged and hyper-converged that allow us to adapt to the IoT trend in healthcare, in retail, where traditional architecture, traditional siloed approaches would maybe handicap us?

Faucher: For some of these workloads, we just don’t know how they're going to scale or how quickly. We see that specifically with new applications. Maybe we're trying a new channel, possibly a new retail channel, and we don’t know how it’s going to scale. Of course, we don’t want to fail by not scaling high enough and turning our customers away.

But some of the vendors that provide cloud, hyper-converged and converged, have come up with some great solutions for rapid scalability. A successful solution for our customers has been something called flexible capacity. That’s where you've decided to go private cloud instead of public for some good reasons, but you wish that your private cloud could scale as rapidly as the public cloud, and also that your payments for your private cloud could scale just like a public cloud could.

Typically, when customers purchase for a private cloud, they're doing a traditional capital expense. So they just spend the money when they have it, and maybe in three or five years they spend more. Or they do a lease payment and they have a certain lease payment every month.

With flexible capacity, I can have more installed in my private cloud than I'm paying for. Let’s say, there is 100 percent there, but I'm only paying for 80 percent. That way, if there's an unexpected demand for whatever reason, I can turn on another 5, 10, 15, or 20 percent immediately without having to issue a PO first, which might takes 60 days in my organization, then place the order, wait 30 days for more to show up, and then meet the demand.

Flexible capacity

Now I can have more on site than I'm paying for, and when I need it I just turn it on and I pay a bill, just like I would if I were running in the public cloud. That’s what is called flexible capacity.

Another options is the ability to do cloud bursting. Let’s say I'm okay with public cloud for certain application workloads -- IaaS, for example -- but what I found is that I have a very efficient private cloud and I can actually run much more cost-effectively in my private cloud than I can in public, but I'm okay with public cloud in certain situations.

Well, if a burst comes, I can actually extend my application beyond private to public to take on this new workload. Then, I can place an order to expand my private cloud andwait for the new backing equipment to show up. That takes maybe 30 days. When it shows up, I set it up, I expand my on-site capability and then I just turn off the public cloud.

The most expensive use of public cloud many times is just turning it on and never turning it off. It’s really most cost-effective for short-term utilization, whether it’s new applications or development or disaster recovery (DR). Those are the most cost-effective fuses of public cloud.

Gardner: As a data scientist, you're probably more concerned with what the systems are doing and how they are doing it, but is there a benefit from your perspective of going with converged infrastructure or hyper-converged infrastructure solutions? Whether it’s bursting or reacting to a market demand within your organization, what is it about converged infrastructure that’s attractive for you as a data scientist?
One of the biggest challenges would be to have a system that will allow an organization to go to market soonest.

Narayanan: One of the biggest challenges would be to have a system that will allow an organization to go to market soonest. With the big-data platform, there are lots of moving parts in terms of network. In a traditional Hadoop technology, there are like three copies of data, and you need to scale that across various systems so that you have high availability. Big-data organizations that are engaging big data are looking at high availability as one of the key requirements, which means that anytime a node goes down, you need to have the data available for analysis and query.

From a data scientist standpoint, stability or the availability of data is a key requirement. The data scientists, when they build your models and analytic views, are churning through tons and tons of data, and it requires tremendous system horsepower and also network capabilities that pulls data from various sources.
Learn more about Rolta AdvizeX Solutions
For the Retail Industry

And for Healthcare Companies
With the converged infrastructure, you get that advantage. Everything is in a single box. You have it just out there, and it is very scalable. For a data scientist, it’s like a dream come true for the analytic needs. 

Gardner: I'm afraid we are coming up towards the end of our time. Let’s look at metrics of success. How do you know you are doing this well? Do you have any examples, Dennis or Raajan, of organizations that have thought about the platform, the right relationship between commercial and open source, that have examined their options on deployment models, including converged and hyper-converged, and what is it that they get back? How would you know that you are doing this right? Any thoughts about these business or technology metrics of success?

New application

Faucher: I have a quick one that I see all the time. Our customers today measure how long it takes to get a new business application out the door. Almost every one of our customers has a measurement around that. How quickly can we get a business application out the door and functional, so that we can act upon it?

Most of the time it can be three months or six months, yet they really want to get these new applications out the door in a week, just constant improvement to their applications to help either their patients or to help their customers out or get into new channels.

What we're finding is they already have a metric that says, today it takes us three months to get a new application out the door. Let’s change that. Let’s really look at the way we are doing things -- people, process and IT end-to-end -- typically where they are helped through something like an Advizer, and let’s look at all the pieces of the process, look at it all from an ITIL standpoint or an ITSM standpoint and ask how can we improve the process.
There are tons of data sources out there. The biggest challenge would be to integrate all that in the fastest amount of time and make sure that value is realized at the soonest.

And then let’s implement the solution and measure it. Let’s have constant improvement to take that three months down to one month, and down to possibly one week, if it’s a standardized enough application.

So for me, from a business standpoint, it’s the fastest time to value for new applications, new research, how quickly can I get those out the door better than I am doing today.

Narayanan: From a technical standpoint Dana, it’s how much data I can aggregate at the fastest. There are tons of data sources out there. The biggest challenge would be to integrate all that in the fastest amount of time and make sure that value is realized at the soonest. With the given platform, any platform that allows for that would definitely serve the purpose for the analytic needs.

Gardner: Listening to you both, it almost sounds as if you're taking what you can do with big data analytics and applying it to how you do big data analytics, is there some of that going on?

Faucher: Absolutely. It’s interesting, when we go out and meet with customers, when we do workshops and gather data from our customers, even when we do Advizers and we capture data from our customers, we use that. We take all identifying customer information out of it, but we use that to help our customers by saying that of the 2,000 customers that we do business with every year, this is what we are seeing. With these other customers, this is where we have seen them be successful, and we use that data to be able to help our customers be more successful faster.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy. Sponsor: HP.

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Tuesday, August 18, 2015

The future of business intelligence as a service with GoodData and HP Vertica

The next BriefingsDirect big data innovation case study interview highlights how GoodData expands the realms and possibilities for delivering business intelligence (BI) and data warehousing as a service. We'll learn how they're exploring new technologies to make that more seamless across more data types for more types of users -- all in the cloud.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy.

To learn the ups and downs of BIaaS, we welcome Jeff Morris, Vice President of Marketing at GoodData in San Francisco, and Chris Selland, Vice President for Business Development at HP Vertica. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tell us about GoodData, what you do, and why it's different.

Morris: GoodData is an analytics platform as a service (PaaS). We cover the full spectrum end-to-end use case of creating an analytic infrastructure as a service and delivering that to our customers.
We take on the challenges of collecting the data, whatever it is, structured and unstructured. We use a variety of technologies as appropriate, as we do that. We warehouse it in our multitenant, massively scalable data warehouse that happens to be powered by HP Vertica.

We then combine and integrate it into whatever the customer’s particular key performance indicators (KPIs) are. We present that in aggregate in our extensible analytics engine and then present it to the end users through desired dashboards, reports, or discoverable analytics.

Our business is set up such that about half of our business operates on an internal use case, typically a sales and marketing and social analytic kind of use case. The other half of our business, we call "Powered by GoodData." and those customers are embedding the GoodData technology in their own products. So we have a number of companies creating these customer-facing data products that ultimately generate new streams of revenue for their business.

40,000 customers

We've been at this since 2007. We're serving about 40,000 customers at this point and enjoying somewhere around 2.4 million data uploads a week. We've built out the service such that it's massively scalable. We deliver incredibly fast time to market. Last quarter, about two thirds of our deployments were delivered within 16 weeks or less.

One of the divisions of HP, in fact, deployed GoodData in less than six weeks. They are giving their first set of KPIs and delivering that value to them. What’s making us different in the marketplace right now is that we're eliminating all of the headaches associated with creating your own big data lake-style BI infrastructure and environment.

What we end up doing is affording you the time to focus on the analytics and the results that you gain from them—without having to manage the back-end operations.

Gardner: You're creating analytic applications on datasets that are easily contributed to your platform.
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Morris: Yes, indeed. The datasets themselves also tend to be born in the cloud. As I said, the types of applications that we're building typically focus on sales and marketing and social, and e-commerce related data, all of which are very, very popular, cloud-based data sources. And you can imagine they're growing like crazy.
We see a leaning in our customer base of integrating some on-premise information, typically from their legacy systems, and then marrying that up with the Salesforce, or the market data or social information that they want to integrate and build a full view of their customers -- or a full exposure of what their own applications are doing.
What we end up doing is affording you the time to focus on the analytics and the results that you gain from them—without having to manage the backend operations.

Gardner: So you're providing an excellent example of how HP Vertica forms a cloud-borne analytics platform. Are any of your clients doing this both on-premises and taking advantage of what the cloud does best? Are we now on the vanguard of hybrid BI?

Morris: We're getting there, and there are certainly some industries are more cloud friendly than others right now. Interestingly, the healthcare space is starting to, but they're still nascent. The financial services industry is still nascent. They're very protective of their information. But retailers, e-commerce organizations, technology ISVs, and digital media agencies have adopted the cloud-based model very aggressively.

We're seeing a terrific growth and expansion there and we do see use cases right now where we're beginning to park the cloud-based environment alongside your more traditional analytics environments to create that hybrid effect. Often, those customers are recognizing that the speed at which data is growing in the cloud is driving them to look for a solution like ours.

Gardner: Chris, how unique is GoodData in terms of being all cloud moving toward hybrid?

Special relationship

Selland: GoodData is certainly a very special partner and a very special relationship for us. As you said, Vertica is fundamentally a software platform that was purpose-built for big data that is absolutely cloud-enabled. But GoodData is the best representation of the partner who has taken our platform and then rolled out service offerings that are specifically designed to solve specific problems. It's also very flexible and adaptable.

So, it’s a special partnership and relationship. It's a great proof point for the fact that the HP Vertica platform absolutely was designed to be running in the cloud for those customers who want to do it.

As Jeff said, though, it really varies greatly by industry. A large majority of the customers in our customer advisory board (CAB), which tend to be some of our largest customers and some pretty well-known industries, were saying how they will never put their data in the cloud.

Never is a very long time, but at the same time, there are other industries that are adopting it very rapidly. So there is a rate of change that’s going on in the industry. It varies by size of company, by the type of competitive environment, and by the type of data. And yes, there is a lot of hybridization going on out there. We're seeing more of the hybridization in existing organizations that are migrating to the cloud. There's a lot of new breed companies who started in the cloud and have every intent of staying there.

But there's a lot of dynamism in this industry, a lot of change, and this is a partnership that is a true win-win. As I said, it's a very special relationship for both companies.

Gardner: There's more than just HP Vertica. There's HP Haven, which includes Hadoop, Autonomy, security and applications. Is there a path that you see whereby you can try to be as many things to as many types of customer and vertical industries?

Morris: Absolutely. The HP Haven-style architecture is a vision in a direction that we are going. We do use Hadoop right now for special use cases of expanding and providing structure, creating structure out of unstructured information for a number of our customers, and then moving that into our Vertica-based warehouse.

The beauty of Vertica in the cloud is the way we have set this up and this also helps address both the security and the reliability issues that might be a thought of as issues in the cloud. We're triple clustering each set of instances of our vertical warehouses, so they are always reliable and redundant.

Daily updates

We, like the biggest enterprises out there, are vigilantly maintaining our network. We update our network on behalf of our customers on a daily basis, as necessary. We roll out and maintain a very standardized operating environment, including an open stack-based operating environment, so that customers never need to even care about what versions of the SSL libraries exist or what versions of the VPN exist.

We're taking care of all of that really deep networking and things that the most stalwart enterprise-style IT architects are concerned about. We have to do that, too, and we have to do it at scale for this multi-tenant kind of use-case.

As I said, the architecture itself is very Haven-like, it just happens to be exclusively in the cloud -- which we find interesting and unique for us. As for the Hadoop piece, we don’t use Autonomy yet, but there are some interesting use cases that we are exploring there. We use Vertica in a couple of places in our architecture, not only that central data warehouse, but we also use it as a high-performance storage vehicle for our analytic data marts.

So when our customers are pushing a lot of information through our system, we're tapping into Vertica’s horsepower in two spots. Then, our analytic engine can ingest and deal with those massive amounts of data as we start to present it to customers.
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On the Haven architecture side, we're a wonderful example of where Haven ends up in the cloud. For the applications themselves, the kind of things that customers are creating, might be these hybrid styles where they're drawing legacy information in from their existing on-premise systems. Then, they're gathering up, as I said before, their sales and marketing information and their social information.

The one that we see as a wonderful green field for us is capturing social information. We have our own social analytic maturity model that we describe to customers and partners on how to capitalize on your campaigns and how to maximize your exposure through every single social channel you can think of.

We're very proficient at that, and that's what's really driving the immense sizes of data that our customers are asking for right now. Where we used to talk in tens of terabytes for a big system, we're now talking in the world of hundreds, multiple hundreds of terabytes, for a system. Case by case by case, we're seeing this really take off.

Gardner: Do you have any companies, either named or unnamed, that provide a great use case example of BI as a service?
Where we used to talk in tens of terabytes for a big system, we're now talking in the world of hundreds, multiple hundreds of terabytes, for a system.

Morris: One of our oldest and most dear customers is Zendesk. They have a very successful customer-support application in the cloud. They provide both a freemium model and degrees of for-fee products to their customers.

And the number one reason why their customers upgrade from freemium to general and then general to the gold level of product is the analytics that they're supplying inside of there. They very recently announced a whole series of data products themselves, all powered by GoodData, as the embedded analytic environment within Zendesk.

We have another customer, Service Channel which is a wonderful example of marrying together two very disparate user communities. Service Channel is a facility’s management enterprise resource planning (ERP) application. They bring together the facility managers of your favorite brick-and-mortar retailers with the suppliers who provide those retail facilities service, janitorial services, air-conditioning guy, the plumbers.

Disparate customers

Marrying disparate types of customers, they create their own data products as well, where they are integrating third-party information like weather data. They score their customers, both the retailers as well as the suppliers, and benchmark them against each other. They compare how well one vendor provides service to another vendor and they also compare how much one of the retailers spends on maintaining their space.

Of course, Apple gets incredibly high marks. RadioShack, right now, as they transition their stores, not so much. Service Channel knew this information long before the industry did, because they're watching spend. They, too, are starting to create almost a bidding network.

When they integrated their weather data into the environment, they started tracking and saying, "Apple would like to gain first right of refusal on the services that they need." So if Apple’s air conditioning goes out, the service provider comes in and fixes the air-conditioning sooner than Best Buy and all of their competitors. And they'll bid up for that. So they've created almost a marketplace. As I said before, these data products are really quite an advantage for us.

Gardner: What's coming next?

Morris: We're seeing a number of great opportunities, and many are created and developed by the technologies we've chosen as our platform. We love the idea of creating not only predictive, but prescriptive, types of applications in use cases on top of the GoodData environment. We have customers that are doing that right now and we expect to see them continue to do that.

What I think will become really interesting is when the GoodData community starts to share their analytic experiences or their analytic product with each other. We feel like we're creating a central location where analysts, data scientists, and our regular IT can all come together and build a variety of analytic applications, because the data lives in the same place. The data lives in one central location, and that’s an unusual thing. In most of the industry your data is still siloed. Either you keep it to yourself on-premise or your vendors keep it to themselves in the cloud and on-premise.

But we become this melting pot of information and of data that can be analytically evaluated and processed. We love the fact that Vertica has its own built-in analytic functions right in the database itself. We love the fact that they run our predictive language without any other issue and we see our customers beginning to build off of that capability.
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My last point about the power of that central location and the power of GoodData is that our whole goal is to free time for those data scientists and those IT people to actually perform analytics and get out of the business of maintaining the systems that make analytics available, so that you can focus on the real intellectual capital that you want to be creating.
Identifying trends

Gardner: So, Chris, to cap this off, I think we've identified some trends. We have PaaS for BI. We have hybrid BI. We have cloud data joins and ecosystems that create a higher value abstraction from data. Any thoughts about how this comes together, and does this fit into the vision that you have at HP Vertica and that you're seeing in other parts of your business?

Selland: We're very much only at the front end of the big data analytics revolution. I ultimately don’t think we are going to be using the term "big data" in 10 years.

I often compare big data today to eBusiness 10, 12 years ago. Nobody uses that term anymore, but that was when everything was going online, and now everything is online, and the whole world has changed. The same thing is happening with analytics today.

With a hundred times more data we can actually get 10,000 times more insight. And that's true, but it's not just the amount of data; it's the ability to cross-correlate. That's the whole vision of what Jeff was just talking about that GoodData is trying to do.
We're very much only at the front end of the big data/analytics revolution. I ultimately don’t think we are going to be using the term "big data" in 10 years.

It's the vision of Haven, to bring in all types of data and to be able to look at it more holistically. One of my favorite examples, just to make that concrete, is that there is an airline we were talking to. They were having a customer service issue. They were having a lot of their passengers tweeting angrily about them, and they were trying to analyze the social media data to figure out how to make this stop and how to respond.

In a totally separate part of the organization, they had a predictive maintenance project, almost an Internet-of-things (IoT) type of project, going on. They were looking at data coming off the fleet, and trying to do better job of keeping their flights on time.

If you think about this, you say, "Duh." There was a correlation between the fact that they were having service problems and that the flights were late with the fact that the passengers were angry. Suddenly, they realized that maybe by focusing less on the social data in this case, or looking at that as the symptom as opposed to cause, they were able to solve the problem much more effectively. That's a very, very simple example.

I cite that because it makes real for people that it's when you really start cross-correlating data you wouldn't normally think belong together -- social data and maintenance data, for example -- you get true insights. It's almost a silly simple example, but those types of examples we're going to see much more. The more of this we can do, the more power we are going to get. I think that the front end of the revolution is here.

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Tuesday, August 11, 2015

HP pursues big data opportunity with updated products, services, developer program

HP today at its Big Data Conference in Boston unveiled a series of new products, services, and programs designed to help organizations better leverage data and analytics.

The company announced:
  • A new release of HP Vertica, called Excavator, that feature data streaming and advanced log file text search to power high-speed analytics on Internet of Things (IoT) data.
  • Broader support for and contributions to open source technologies, including optimized Hadoop performance, integration with the Apache Kafka distributed messaging system, and advancements in Distributed R predictive analytics.
  • The HP Haven Startup Accelerator program, which provides early-stage companies with fast, affordable access to both HP Big Data and Application Delivery Management software and services.
"Big data helps us make more sense of it all, a byproduct of what we do everyday," said Robert Youngjohns, Executive Vice President and General Manager at HP Software, in a keynote address. "Big data solves everyday problems like security, inventory, and empowers workers ... and you can now exploit 100 percent of your data."

Youngjohns said that HP at its HP Protect show in a few weeks will announce how to better mine IT systems analytics to make enterprises more secure.

"Big data changes the game in the idea economy," said Colin Mahony, Senior Vice President and General Manager of Big Data at HP. "Big data is core to all apps, but customized composite analytic applications are coming."

The key question for enterprises is, how can you embed analytics into the role you play in your organization?, said Mahony, adding that, enterprises need to spin up new apps constantly to identify and analyze via context, which they cannot get from packaged apps.

"Developers are the new heroes of the idea economy," said Mahony. "Through our Haven and Haven OnDemand platforms, we are empowering these heroes to transform their business through data, by allowing them to harness the value of all forms of information, rapidly connect and apply open source, and quickly access the tools they need to build winning businesses." [Disclosure: HP Enterprise is a sponsor of BriefingsDirect podcasts.]

Also addressing the keynote audience was recent Turing Award winner Mike Stonebraker, CTO and co-founder of Tamr. He said that the development of the column store database was the most disruptive thing I ever did. "It transformed the market," he said, and lead to the Vertica big data platform that HP acquired in 2011.

But he cautioned against getting caught up in marketing buzz over data science substance. "I’ve been doing big data forever. The buzzword is meaningless, it’s about solving data volume, velocity, and variety problems."

"Analytics is moving to complex analytics, using machine learning and statistics, so you need to get smart at data science," said Stonebraker.


Capabilities in the new version of Vertica, codenamed "Excavator," include:
  • Data-streaming analytics offering native support for the Apache Kafka open-source distributed messaging system to enable organizations to quickly ingest and analyze high-speed streaming data, including IoT, in near real time. This capability delivers actionable insight for a wide range of use cases, including manufacturing process control, supply-chain optimization, healthcare monitoring, financial risk management, and fraud detection.
  • Advanced machine log text search to enable organizations to collect and index large log file data sets generated by systems and business applications, helping IT organizations quickly identify and predict application failures and cyber-attacks, and investigate authorized and unauthorized access.
HP also released a series of solutions designed to enable organizations to combine the innovation of open source with the enterprise-scale, reliability, and security. These include:
"Developers are the new heroes of the Idea Economy."
  • HP Vertica for SQL on Hadoop native file support, bringing significant increase in performance on popular Hadoop formats like ORC and Parquet. Specifically, HP worked collaboratively with Hortonworks to develop a new high-performance access layer that enables SQL queries to run directly on ORC files, resulting in a 5x increase in execution times.
  • HP Vertica Flex Zone Table Library with which HP has open sourced its innovative Flex Table "schema on-need" technology to the global developer community. With this move, organizations will be able to fully harness virtually any form of semi-structured data to meet their unique needs.
  • HP announced its commitment to integrate Vertica with Apache Spark. This will enable accelerated data transfer between Vertica and Spark, allowing organizations to take full advantage of their Spark-based deployments. This future capability will enable the developer community to build their models in Spark and run them in Vertica for high-speed and sophisticated analytics.
Startup Accelerator

HP also unveiled The HP Haven Startup Accelerator, a new program designed to support and expand HP's ecosystem of developers and innovators by making HP Big Data and Application Delivery Management software products accessible to early-stage companies. The program removes traditional barriers for organizations looking to leverage analytics and data to build powerful, differentiated applications. Qualified participants will benefit from the following program components:
  • Free use of the community versions of HP IDOL and HP Vertica with expanded capacity.
  • Premium version of HP IDOL and HP Vertica with attractive pricing
  • HP Application Delivery Management tools including HP Agile Manager, HP LeanFT, and HP LoadRunner.
HP also announced an innovative framework of technology and proven best practices to accelerate the development of next-generation analytical applications. This framework extends the HP Haven Big Data Platform with quick-start visualization, syndicated data feeds, open on-premise and cloud-based APIs. This enables HP Professional Services and HP partners to quickly deliver a range of solutions, such as voice of customer, smart cities, and fraud detection.
"HP is uniquely positioned to help businesses and developers thrive in this new world."

Optimized platforms for big data include traditional HP ProLiant DL380 clusters, purpose-built Apollo 4510, 4530 and 4200 compute and storage servers, and HP's innovative asymmetric Big Data Reference Architecture. These systems enable customers to optimize their big data workloads, delivering the power of Haven to their business.

Availability Planned availability for new HP Haven Big Data offerings and services is set for fall of 2015.

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Monday, August 10, 2015

How eCommerce sites harvest big data across multiple clouds

The next BriefingsDirect big data innovation thought leadership interview highlights how a consultant helps large ecommerce organizations better manage their big data architectures across cloud environments.

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To learn more about how big data is best architected for the largest web applications, BriefingsDirect sat down with Jimmy Mohsin, Principal Software Architect at Norjimm LLC, a consultancy based in Princeton, New Jersey. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: How are large web applications deciding on the right big data architecture? 

Mohsin: There's a lot of interest in trying to deal with large data volumes, not only large data volumes, but also data that changes rapidly. Now, there are many companies that have very large datasets, some in terabytes, some in petabytes and then they're getting live feeds.

The data is there and it’s changing rapidly. The traditional databases sometimes can’t handle that problem, especially if you're using that database as a warehouse and you're reporting against it.

Basically, we have kind of a moving-target situation. With HP Vertica, what we've seen is the ability to solve that problem in at least some of the cases that I've come across, and I can talk about specific use cases in that regard.

Input/output issues

Gardner: Before we get into a specific use case, I'm interested particularly in some of these input/output issues. People are trying to decide how to move the data around. They're toying with cloud. They're trying to bring data for more types of traditional repositories. And, as you say, they're facing new types of data problems with streaming and real-time feeds.

How do you see them beginning this process when they have to handle so many variables? Is it something that’s an IT architecture, or enterprise architecture, or data architecture? Who's responsible for this, given that it’s now a rather holistic problem?
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Mohsin: In my present project, we ran into that. The problem is that many companies don't even have a well defined data-architecture team. Some of them do. You'll find a lot of companies with an enterprise-architect role and you'll have some companies with a haphazard definition of an architectural group.
Net-net, at least at this point, unless companies are more structured, it becomes a management issue in the sense that someone at the leadership level needs to know who has what domain knowledge and then form the appropriate team to skin this cat.

I know of a recent situation where we had to build a team of four people, and only one was an architect. But we built a virtual team of four people who were able to assemble and collate all the repositories that spanned 15 years and four different technology flavors, and then come up with an approach that resulted in a single repository in HP Vertica.

So there are no easy answers yet, because organizations just aren't uniformly structured.

Gardner: Well, I imagine they'll be adapting, just like we all are, to the new realities. In the meantime, tell me about a specific use case that demonstrates the intensity of scale and velocity, and how at least one architecture has been deployed to manage that?

Mohsin: One of my present projects deals with one of the world's largest retailers. It's eCommerce, online selling. One of the things they do, in addition to their transactions of buying and selling, is email campaign management. That means staying in touch with the customer on the basis of their purchases, their interests, and their profiles.

One of the things we do is see what a certain customer’s buying preferences have been over the past 90 days. Knowing that and the customer’s profile, we can try to predict what their buying patterns will be. So we send them a very tailored message in that regard. In this project, we're dealing with about 150 to 160 million emails a day. So this is definitely big data.

Here we have online information coming into one warehouse as to what's happening in the world of buying and selling. Then, behind the scenes, while that information is being sent to the warehouse, we're trying to do these email campaigns.

This is where the problem becomes fairly complicated. We tried traditional relational database management systems (RDBMS), and they kind of worked, but we ran into a slew of speed and performance issues. That's really where the big-data world was really beneficial. We were able to address that problem in about a seven-month project that we ran.

Gardner: And this was using HP Vertica?

Large organization

Mohsin: We did an evaluation. We looked at a few databases, and the corporate choice was Vertica. We saw that there is a whole bunch of big-data vendors. The issue is that many of the vendors don't have any large organizations behind them, and Vertica does. The company management felt that this was a new big database, but HP was behind it, and the fact that they also use HP hardware helped a lot.

They chose Vertica. The team I was managing did a proof of concept (POC) and we were able to demonstrate that Vertica would be able to handle the reporting that is tied to the email campaign management. We ran a 90 day POC, and the results were so positive that there was an interest in going live. We went live in about another 90 days, following a 90-day POC.

Gardner: I understand that Vertica is quite versatile. I've heard of a number of ways in which it's used technically. But this email campaign problem almost sounds like a transactional issue, a complex event processing issue, or a transfer agent scaling issue. How does big data, Vertica, and analytics come to bear on this particular problem?

Mohsin: It's exactly what you say it is. As we are reporting and pushing out the campaigns, new information is coming in every half hour, sometimes even more frequently. There's a live feed that's updating the warehouse. While the warehouse is being updated, we want to report against it in real time and keep our campaigns going.
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The key point is that we can't really stop any of these processes. The customers who are managing the campaigns want to see information very frequently. We can’t even predict when they would want their information. At the same time, the transactional systems are sending us live feeds.

The problem we ran into with the traditional RDBMS is that the reporting didn't function when the live feeds were underway. We couldn't run our back-end email campaign reports when new data was coming in.

One of the benefits Vertica has, due to its basic architecture and its columnar design is that it's better positioned to do that. This is what we were able to demonstrate in the live POC, and nobody was going to take our word for it.

The end user said, "Take few of our largest clients. Take some of our clients that have a lot of transactions. Prove that the reports will work for those clients." That's what we did in 30 days. Then, we extended it, and then in 90 days, we demonstrated the whole thing end to end. Following that was the go-live.

Gardner: You had to solve that problem of the live feeds, the rapidity of information. Rather going to a stop, batch process, analyze, repeat, you've gained a solution to your problem.

But at the same time, it seems like you're getting data into an environment where you can analyze it and perhaps extract other forms of analysis, in addition to solving your email, eCommerce trajectory issues. It seems to me that you're now going to have the opportunity to add a new dimension of analysis to what's going on and perhaps we find these transactions more toward a customer inference benefit.

More than a database

Mohsin: One of the things internally that I like to say is that Vertica isn't just a big database, it’s more than just a database. It's really a platform, because you have distributed all, you are publishing other tools. When we adopted it and went live with this technology, we first solved the feeds and speeds problem, but now we're very much positioned to use some of the capabilities that exist in Vertica.

We had Distributed R being one of them, Inference Analysis being another one, so that we can build intelligent reports. To date, we've been building those outside the RDBMS. RDBMS has no role in that. With Vertica, I call it more of a data platform. So we definitely will go there, but that would be our second phase.

As the system starts to function and deliver on the key use cases, the next stage would be to build more sophisticated reports. We definitely have the requirements and now we have the ability to deliver.

Gardner: Perhaps you could add visualization capabilities to that. You could make a data pool available to more of the constituents within this organization so that they could innovate and do experiments. That’s a very powerful stuff indeed.

Is there anything else you can tell us for other organizations that might be facing similar issues around real-time feeds and the need to analyze and react, now that you have been through this on this particular project. Are there any lessons learned for others.
One of the issues in big data at least today is that you can’t find a whole slew of clients who have already gone live and who are in production.

If you're facing transactional issues and you haven't thought about a big-data platform as part of that solution, what do you offer to them in terms of maybe lighting a light bulb in their mind about looking for alternatives to traditional middleware.

Mohsin: Like so many people try to do, we tried to see if anyone else had done this. One of the issues in big data at least today is that you can’t find a whole slew of clients who have already gone live and who are in production.

There are lots of people in development, and some are live, but in our space, we couldn't find anyone who was live. We solved that issue via a quick-hit POC. The big lesson there was that we scoped the POC right. We didn’t want to do too much and we didn’t want to do too little. So that was a good lesson learned.

The other big thing is the data-migration question. Maybe, to some extent, this problem will never be solved. It's not so easy to pull data out of legacy database systems. Very few of them will give you good tools to migrate away from them. They all want you to stay. So we had to write our own tooling. We scoured the market for it, but we couldn’t find too many options out there.

Understand your data

So a huge lesson learned was, if you really want to do this, if you want to move to big data, get a handle on understanding your data. Make sure you have the domain experts in-house. Make sure you have the tooling in place, however rudimentary it might be, to be able to pull the data out of your existing database. Once you have it in the file system, Vertica can take it in minutes. That’s not the problem. The problem is getting it out.
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We continue to grapple with that and we have made product enhancement recommendations. But in fairness to Vertica, this is really not something that Vertica can do much about, because this is more in the legacy database space.

Gardner: I've heard quite a few people say that, given the velocity with which they are seeing people move to the cloud, that obviously isn't part of their problem, as the data is already in the cloud. It's in the standardized architecture that that cloud is built around, if there is a platform-as-a-service (PaaS) capability, then getting at the data isn't so much of a problem, or am I not reading that correctly?
There is still a lingering fear of the cloud. People will tell you that the cloud is not secure.

Mohsin: No, you're reading that correctly. The problem we have is that a lot of companies are still not in the cloud. There is still a lingering fear of the cloud. People will tell you that the cloud is not secure. If you have customer information, if you have personalized data, many organizations don't want to put it in the cloud.

Slowly, they are moving in that direction. If we were all there, I would completely agree with you, but since we still have so many on-premise deployments, we're still in a hybrid mode -- some is on-prem, some is in the cloud.

Gardner: I just bring it up because it gives yet another reason to seriously consider cloud. It’s a benefit that is actually quite powerful -- the data access and ability to do joins and bring datasets together because they're all in the same cloud.

Mohsin: I fundamentally agree with you. I fundamentally believe in the cloud and that it really should be the way to go. Going through our very recent go-live, there is no way we could have the same elasticity in an on-prem is deployment that we can have in a cloud. I can pick up the phone, call a cloud provider, and have another machine the next day. I can't do that if it’s on-premise.

Again, a simple question of moving all the assets into the cloud, at least in some organizations, will take several months, if not years.

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