Thursday, October 15, 2015

How Sprint employs orchestration and automation to bring IT into DevOps readiness

The next BriefingsDirect DevOps innovation case study explores how telecommunications giant Sprint places an emphasis on orchestration and automation to bring IT culture and infrastructure into readiness for cloud, software-defined data center (SDDC) and DevOps.

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

Learn how Sprint has made IT infrastructure orchestration and automation a pillar of its strategic IT architecture future from Chris Saunderson, Program Manager and Lead Architect for Data Center Automation at Sprint in Kansas City, Missouri. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: I'm intrigued by your emphasis on working toward IT infrastructure, of getting to more automation at a strategic level. Tell us why you think automation and orchestrations are of strategic benefit to IT.
IT automation is an urgent priority
Automation and orchestration can pay huge dividends
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Saunderson: We've been doing automation since 2011, but it came out of an appreciation that the velocity of change inside that data center is just going to increase over time.

In 2009, my boss and I sat down and said, "Look, this is going nowhere. We're not going to make a significant enough impact on the way that the IT division works ... if we just keep doing the same thing."

Saunderson
That’s when we sat down and saw the orchestrated data center coming. I encapsulated it as the "data center of things." When you look at the journey that most enterprises go through, right around 2009 is when the data center of things emerged. You then began to lose track of where things are, what they are, who uses them, how long they’ve been there, and what their state is.

When we looked at automation and orchestration in 2009, it was very solidly focused on IT operational efficiency, but we had the longer-term view that it that it was going to be foundational for the way to do things going forward -- if for nothing else than to handle the data center of things. We could also see changes coming in the way that our IT organization was going to have to respond to the change in our business, let alone just the technology change.

Gardner: So that orchestration has allowed you to not only solve the problems of the day, but put a foundation in place for the new problems, rapid change, cloud, mobile, all of these things that are happening. Before we go back to the foundational aspects, tell us a little bit about Sprint itself, and why your business is changing.

Provider of choice

Saunderson: The Sprint of today ... We're number three, with aspirations to be bigger, better, and faster, and the provider of choice in wireless, voice and data. We're a tier 1 network service provider of global IP network along with private network, MPLS backbone, all that kind of stuff. We're a leader in TRS -- Telecommunication Relay Services for the deaf.

The Sprint of old is turning into the Sprint of new, where we look at mobile and we say mobile is it, mobile is everything -- that Internet of Things (IoT). That's what we want to foster growth. I see an exciting company that’s coming in terms of connecting people not only to each other, but to their partners, the people who supply services to them, to their entertainment, to their business. That’s what we do.

Gardner: When you started that journey for automation -- getting out of those manual processes and managing complexity, but repeatedly getting the manual labor out of the way -- what have you learned that you might relate to other people? What are some of the first things people should keep in mind as they embark on this journey?

Saunderson: It’s really a two-part answer. Orchestration comes after automation, because orchestration is there to consume the new automation services. So let’s take that one first. The big things to remember is that change is hard for people. Not technology change. People are very good about doing technology change, but unwiring people’s brains is a problem, and you have to acknowledge that up-front. You’re going to have a significant amount of resistance from people to change the way that they're used to doing things.
Orchestration comes after automation, because orchestration is there to consume the new automation services.

Now addressing that is also a human problem, but in a certain sense, the technology helps because you're able to say things like, "Let's just look at the result and let's compare what it takes to get to the result. Was it the humans doing it, and what does it take to get to the result with the machines doing it?" Let’s just call it what it is. It’s machines doing things. If the result is the same, then it doesn't require the humans. That’s challenge number one, unwiring people’s minds.

The second is making sure that you are articulating the relevance of what you’re doing. We had an inbuilt advantage, at least in the automation space, of having some external forces that were driving us to do this.

It’s really regulatory compliance, right? Sarbanes-Oxley (SOX) is what it is. PCI is what it is --  SAS70, FISMA, those things. We had to recognize the excessive amount of labor that we were expending to try and keep up with regulatory change.

PCI changes every year or 18 months. So it's just going through every rule set and saying, "Yes, this doesn’t apply to me; I'm more restricted." That takes six people. We were able to turn that. We were able to address the requirement to continue to do compliance more effectively and more efficiently. Don’t lose that upward communication, the relevancy thing -- which is not only are we doing this more efficiently, but we are better at it?

When you get to orchestration, now you’re really talking about some interesting stuff because this is where you begin to talk about being able to do continuous compliance, for example. That says, "Okay, we used to treat this activity as once a quarter or maybe once a month. Let's just do it all the time, but don’t even have a human involved in it." Anybody who has talked to me about this will hear this over and over again. I want smart people working on smart things. I do not want smart people working on dumb things. Truth be told, 99 percent of the things that IT people do are dumb things.

Orchestration benefits

The problem with them is that they're dumb because they force a human to look at the thing and make a decision. Orchestration allows you take that one level out, look at the thing, and figure out how to make that decision without a human having to make it. Then, tie that to your policy, then report on policy compliance, and you're done.

The moment you do that, you’re freeing people up to go have the harder discussions. This is where we start to talk about DevOps and this is where we start to talk about some of the bigger blocks that grind against each other in the IT world.

Gardner: "Continuous" is very interesting. You use the PCI compliance issue, but it's also very important when it comes to applications, software development, test, and deploy. Is there anything that you can explain for us about the orchestration and automation that lends itself to that continuous delivery of applications? People might not put the two together, but I'm pretty sure there's a connection here.
IT automation is an urgent priority
Automation and orchestration can pay huge dividends
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Saunderson: There is. DevOps is a philosophy. There was a fantastic discussion from Adobe where it was very clear that DevOps is a philosophy, an organizational discussion. It’s not necessarily a technology discussion. The thing that I would say, though, is that you can apply continuous everywhere.

The successes that we're having in that orchestration layer is that it's a much easier discussion to go in and say, "You know how we do this over here? Well, what if it was a release candidate code?" The real trick there, when you go back to the things that I want people to think about, is that DevOps is a philosophy, because it requires development and operations to work together, not one hand off to the other, and not one superior to the other; it’s together.

If they’re not willing to walk down the same path together, then you have an organizational problem, but you may also have a toolset problem as well. We're an Application Lifecycle Manager (ALM) shop. We have it there. Does it cover all of our applications? No. Are we getting all of the value out of it that we could? No.

But that’s because we're spending time in getting ready to do things like connect ALM into the running environment. The bigger problem, Dana, is that the organization has to be ready for it, because your philosophical changes are way more difficult than technical changes. Continuous means everything else has to be continuous along with it.

If you're in the ITIL model, you’re still going to need configuration items (CIs). How do CIs translate to Docker containers? Do they need to be described in the same way? If the operations team isn't necessarily as involved in the management of continuously deployed applications, who do I route a ticket to and how do they fix it?

This is where I look at it and say that this is the opportunity for orchestration to sit underneath that and say it not only has the capability to enable people to deploy continuously -- whether it’s into test or production, disaster recovery, or any other environment.

To equip them to be able to operate the continuous operation (that’s coming after the continuous integration and development and deployment), that has to be welded on because you’re going to enforce dis-synergy if you don’t address it all at the same time as you do with integration and deployment.

Gardner: Let’s look at some other values that you can derive from better orchestration and automation. I'm thinking about managing complexity, managing scale, but also more of the software-defined variety. We are seeing a lot of software-defined storage (SDS), software-defined networking (SDN), ultimately software-defined data center (SDDC), all of which is abstracted and managed. How do you find the path to SDDC, vis-à-vis better orchestration and automation?

At the core

Saunderson: Orchestration is going to have to be at the core of that. If you look at the product offerings just across the space, you’re starting to see orchestration pop up in every last one of them -- simply because there's no other way to do it.

RESTFul APIs are nice, but it’s not enough because, at that point, you’re asking customers to start bolting things together themselves, as opposed to saying, "I'm going to give you a nice orchestrated interface, where I have a predefined set of actions that are going be executed when you poll that orchestration to make it work and then apply that across the gamut."

SDS is coming after SDN. Don’t misunderstand me. We're not even at the point of deploying software defined networks, but we look at it and we say, "I have to have that, if for no other reason than I need to remove the human hands out of the delivery chain for things that touch the network."
We should never lose sight of the fact that the whole reason to do this is to say, "Deploy the thing."

I go back to the data center of things. The moment you go to 10Gbit, where you are using virtual context, just anything that’s in the current lexicon of new networking as opposed to VLANs, versus all that stuff, switchboards, etc., you’re absolutely losing visibility.

Without orchestration, and, behind that, without the analytics to look at what's happening in the orchestration that’s touching the elements in your data center, you’re going to be blind. Now, we’re starting to talk about going back to the dark ages. I think we're smarter than that.

By looking at orchestration as the enabler for all of that, you start to get better capability to deliver that visibility that you’re after, as well as the efficiency. We should never lose sight of the fact that the whole reason to do this is to say, "Deploy the thing."

That’s fine, but how do I run it, how do I assure it, how do I find it? This keeps coming up over and over. Eventually, you’re going to have to do something to that thing, whether it’s deployed again, whether you have some kind of security event that is attached to it, or the business just decides not to do it any more. Then, I have to find it and do something to it.

Gardner: Given your deep knowledge and understanding of orchestration and automation, what would you like to see done better for the tools that are provided to you to do this?

Is there a top-three list of things you’d like to see that would help you extend the value of your orchestration and automation, do things like software-defined, do things like DevOps as a philosophy, ultimately to be have more of a data-driven IT of strategic operation?

Development shop

Saunderson: I'm not sure I have a top three. I can certainly talk about generic principal stuff, which is, I want open. That’s what I really want. Just to take the sideline for a second, it’s fascinating. It’s just absolutely fascinating. IT operations is starting to become a software development shop now.

I'm not resistant to that in the least because, just in this conversation, we've been talking about RESTFul APIs and we were talking about orchestration. None of this is IT operations stuff. This isn’t electrons flowing through copper anymore. It’s business process translated into a set of actions, open, and interoperable.

Then, just give me rich data about those things, very rich data. We’re getting to that point, just by the shear evolution of big data, that it doesn’t matter anymore. Just give it all to me, and I will filter it out to what I'm looking for.

Gardner: The thing that is interesting with Hewlett Packard Enterprise (HPE) is that they do have a big-data capability, as well as a leading operations capability and they're starting to put it all together.

Saunderson: In the same way the orchestration is starting to pop up everywhere. If you look at the HPE product portfolio and you look at network coordination, it’s going to have an operations orchestration interface into it. Server automation is welded into operations orchestration and it’s going to appear everywhere else. Big data is coming with it.
Server automation is welded into operations orchestration and it’s going to appear everywhere else.

I'm not hesitant on it. It's just that it introduces complexity for me. The fact that the reporting engine is starting to turn big data is good. I'm happy for that. It just has to get more. It’s not enough to just be giving me job results that are easy to find and easy to search. Now, I want to get some really rich metadata out of things.

Software-defined network is a good example. The whole open flow activity just by itself looks like network management until it goes into a big-data thing and then suddenly, now I have a data source that I can start correlating events to that turn into actions inside the control that turns into change on the network. 

Let’s extend that concept. Let’s put that into orchestration, into service management, or into automation. Give me that and it doesn’t have to be the single platform. Give me a way to anticipate HPE’s product roadmap. The challenge for HPE is delivery.

Gardner: Before we sign off, one of the important things about IT investment is getting the buy-in and support from your superiors or the other aspects of your enterprise. Are there some tangible metrics of success, returns on investment (ROIs), improvements and productivity that you can point to from your orchestration, not just helping smart people do smart things, but benefiting the business at large? 

Business case

Saunderson: So organizations often only do the things that the boss checks. The obvious priorities for us are straight around our business case.

If you want to look at real tangibles, our virtual server provisioning, even though it’s the  heavyweight process that it is today, is turning from days into hours. That’s serious change, that’s serious organizational cultural change, but it’s not enough. It has to be minutes not hours, right? 

Then there's compliance. I keep coming back to it as this is a foundational thing. We're able to continue to pass SOX and PCI every time, but we do it efficiently. That’s a cultural change as well, but that’s something that CIOs and above do care about, because it’s kind of important.

One gets your CFO in trouble, and the other ones stops you taking payments. That gets people's attention really quickly. The moment you can delve into those and demonstrate that not only are you meeting those regulatory requirements, and you're able to report all of them and have auditors look at it and say yes we agree, you are doing all those things that you should be doing.
IT automation is an urgent priority
Automation and orchestration can pay huge dividends
Read the report
Then, you can flip that into the next area which is that we do have to go look at our applications for compliance. We have rich metadata over here that was able to articulate things. So let’s apply it there.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

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Monday, October 5, 2015

How fast analytics changes the game and expands the market for big data value

The next BriefingsDirect big-data thought leadership discussion highlights how fast analytics -- or getting to a big data analysis value in far less time than before -- expands the market for advanced data infrastructure to gain business insights.

We'll learn how bringing analytics to a cloud services model also allows smaller and less data-architecture-experienced firms to benefit from the latest in big-data capabilities. And we'll explore how Dasher Technologies is helping to usher in this democratization of big data value to more players in less time.

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

To share how a fast ramp-up for big data as a service has evolved, we're joined by Justin Harrigan, Data Architecture Strategist at Dasher Technologies, as well as Chris Saso, Senior Vice President of Technology at Dasher Technologies in Campbell, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Justin, how have big-data practices changed over the past five years to set the stage for rapid leveraging of big-data capabilities?

Harrigan: Back in 2010, we saw big data become mainstream. Hadoop became a household name in the IT industry, doing scale-out architectures. Linux databases were becoming common practice. Moving away from traditional legacy, smaller, slower databases allowed this whole new world of analytics to open up to previously untapped resources within companies. So data that people had just been sitting on could now be used for actionable insights.

Harrigan
Fast forward to 2015, and we've seen big data become more approachable. Five years ago, only the largest organizations or companies that were specifically designed to leverage big-data architectures could do so. The smaller guys had maybe a couple of hundred or even tens of terabytes, and it required too much expertise or too much time and investment to get a big-data infrastructure up and running.

Today, we have approachable analytics, analytics as a service, hardened architectures that are almost turnkey with back-end hardware, database support, and applications -- all integrating seamlessly. As a result, the user on the front end, who is actually interacting with the data and making insights, is able to do so with very little overhead, very little upkeep, and is able to turn that data into business-impact data, where they can make decisions for the company.

Gardner: Justin, how big of an impact has this had? How many more types of companies or verticals have been enabled to start exploring advanced, cutting-edge, big-data capabilities? Is this a 20 percent increase? Perhaps almost any organization that wants to can start doing this.

Tipping point

Harrigan: The tipping point is when you outgrow your current solutions for data analytics. Data analytics is nothing new. We've been doing it for more than 50 years with databases. It’s just a matter of how big you can get, how much data you can put in one spot, and then run some sort of query against it and get a timely report that doesn’t take a week to come back or that doesn't time out on a traditional database.

Saso
Almost every company nowadays is growing so rapidly with the type of data they have. It doesn’t matter if you're an architecture firm, a marketing company, or a large enterprise getting information from all your smaller remote sites, everyone is compiling data to create better business decisions or create a system that makes their products run faster.

For people dipping their toes in the water for their first larger dataset analytics, there's a whole host of avenues available to them. They can go to some online providers, scale up a database in a couple of minutes, and be running.

They can download free trials. HP Vertica has a community edition, for example, and they can load it on a single server, up to terabytes, and start running there. And it’s significantly faster than traditional SQL.

It’s much more approachable. There are many different flavors and formats to start with, and people are realizing that. I wouldn’t even use the term big data anymore; big data is almost the norm.

Gardner: I suppose maybe the better term is any data, anytime.

Harrigan: Any data, anytime, anywhere, for anybody.

Gardner: I suppose another change over the past several years has been an emphasis away from batch processing, where you might do things at an infrequent or occasional basis, to this concept that’s more applicable to a cloud or an as-a-service model, where it’s streaming, continuous, and then you start reducing the latency down to getting close to real time.

Are we starting to see more and more companies being able to compress their feedback, and start to use data more rapidly as a result of this shift over the past five years or so?

Harrigan: It’s important to address the term big data. It’s almost like an umbrella, almost like the way people use cloud. With big data, you think large datasets, but you mentioned speed and agility. The ability to have real-time analytics is something that's becoming more prevalent and the ability to not just run a batch process for 18 hours on petabytes of data, but having a chart or a graph or some sort of report in real time. Interacting with it and making decisions on the spot is becoming mainstream.

We did a blog post on this not long ago, talking about how instead of big data, we should talk about the data pipe. That’s data ingest or fast data, typically OLTP data, that needs to run in memory or on hardware that's extremely fast to create a data stream that can ingest all the different points, sensors, or machine data that’s coming in.

Smarter analysis

Then we've talked about smarter analytic data that required some sort of number-crunching dataset on data that was relevant, not data that was real-time, but still fairly new, call it seven days or older and up to a year. And then, there's the data lake, which essentially is your data repository for historical data crunching.

Those are three areas you need to address when you talk about big data. The ability to consume that data as a service is now being made available by a whole host of companies in very different niches.

It doesn’t matter if it’s log data or sensor data, there's probably a service you can enable to start having data come in, ingest it, and make real-time decisions without having to stand up your own infrastructure.

Gardner: Of course, when organizations try to do more of these advanced things that can be so beneficial to their business, they have to take into consideration the technology, their skills, their culture -- people, process and technology, right?

Chris, tell us a bit about Dasher Technologies and how you're helping organizations do more with big-data capabilities, how you address this holistically, and this whole approach of people, process and technology.
Dasher has built up our team to be able to have a set of solutions that can help people solve these kinds of problems.

Saso: Dasher was founded in 1999 by Laurie Dasher. To give you an idea of who we are, we're a little over 65 employees now, and the size of our business is somewhere around $100 million.

We started by specializing in solving major data-center infrastructure challenges that folks had by actually applying the people, process and technology mantra. We started in the data center, addressing people’s scale out, server, storage, and networking types of problems. Over the past five or six years, we've been spending our energy, strategy, and time on the big areas around mobility, security, and of course, big data.

As a matter of fact, Justin and I were recently working on a project with a client around combining both mobility information and big data. It’s a retail client. They want to be able to send information to a customer that might be walking through a store, maybe send a coupon or things like that. So, as Justin was just talking about, you need fast information and making actionable things happen with that data quickly. You're combining something around mobility with big data.

Dasher has built up our team to be able to have a set of solutions that can help people solve these kinds of problems.

Gardner: Justin, let’s flesh that out a little bit around mobility. When people are using a mobile device, they're creating data that, through apps, can be shared back to a carrier, as well as application hosts and the application writers. So we have streams of data now about user experience and activities.

We also can deliver data and insights out to people in the other direction in that real-time of fashion, a closed loop, regardless of where they are. They don’t have to be at their desk, they don’t have to be looking at a specific business-intelligence (BI) application for example. So how has mobility changed the game in the past five years?

Capturing data

Harrigan: Dana, it’s funny you brought up the two different ways to capture data. Devices can be both used as a sensor point or as a way to interact with data. I remember seeing a podcast you did with HP Vertica and GUESS regarding how they interacted with their database on iPads.

In regards to interacting with data, it has become not only useful to data analysts or data scientists, but we can push that down into a format so lower-level folks who aren't so technical. With a fancy application in front of them, they can use the data as well to make decisions for companies and actually benefit the company.

You give that data to someone in a store, at GUESS for example, who can benefit by understanding where in the store to put jeans to impact sales. That’s huge. Rather than giving them a quarterly report and stuff that's outdated for the season, they can do it that same day and see what other sites are doing.

On the flip side, mobile devices are now sensors. A mobile device is constantly pinging access points over wi-fi. We can capture that data and, through a MAC address as an unique identifier, follow someone as they move through a store or throughout a city. Then, when they return, that person’s data is captured into a database and it becomes historical. They can track them through their device.
Read more on tackling big data analytics
Learn how the future is all about fast data
Find out how big data trends affect your business
It allows a whole new world of opportunities in terms of the way retailers interact with where they place merchandise, the way they interact with how they staff stores to make sure they have the proper amount of people for the certain time, what weather impact has on the store.

Lastly, as Chris mentioned, how do we interact with people on devices by pushing them data that's relevant as they move throughout their day?

The next generation of big data is not just capturing data and using it in reports, but taking that data in real time and possibly pushing it back out to the person who needs it most. In the retail scenario, that's the end users, possibly giving them a coupon as they're standing in front of something on a shelf that is relevant and something they will use.

Gardner: So we're not just talking about democratization of analytics in terms of the types of organizations, but now we're even talking about the types of individuals within those organizations.

Do you have any examples of some Dasher’s clients that have been able to exploit these advances and occurrences with mobile and cloud working in tandem, and how that's produced some sort of a business benefit?

Business impact

Harrigan: A good example of a client who leveraged a large dataset is One Kings Lane. They were having difficulty updating the website their users were interacting with because it’s a flash shopping website, where the information changes daily, and you have to be able to update it very quickly. Traditional technologies were causing a business impact and slowing things down.

They were able to leverage a really fast columnar database to make these changes and actually grow the inventory, grow the site, and have updates happen in almost real time, so that there was no impact or downtime when they needed to make these changes. That's a real-world example of when big data had the direct impact on the business line.

Gardner: Chris, tell us a little bit about how Dasher works with Hewlett Packard Enterprise technologies, and perhaps even some other HP partners like GoodData, when it comes to providing analytics as a service?
Once Vertica . . . has done the analysis, you have to report on that and make it in a nice human-readable form or human-consumable form.

Saso: HP has been a longtime partner from the very beginning, actually when we started the company. We were a partner of Vertica before HP purchased them back in 2011.

We started working with Vertica around big data, and Justin was one of our leads in that area at the time. We've grown that business and in other business units within HP to combine solutions, Vertica, big data, and hardware, as Justin was just talking about. You brought up the applications that are analyzing this big data. So we're partners in the ecosystem that help people analyze the data.

Once HP Vertica, or what have you, has done the analysis, you have to report on that and make it in a nice human-readable form or human-consumable form. We’ve built out our ecosystem at Dasher to have not only the analytics piece, but also the reporting piece.

Gardner: And on the as a service side, do you work with GoodData at all or are you familiar with them?

Saso: Justin, maybe you can talk a little bit about that. You've worked with them more I think on their projects.

Optimizing the environment

Harrigan: GoodData is a large consumer of Vertica and they actually leverage it for their back-end analytics platform for the service that they offer. Dasher has been working with GoodData over the past year to optimize the environment that they run on.

Vertica has different deployment scenarios, and you can actually deploy it in a virtual-machine (VM) environment or on bare-metal. And we did an analysis to see if there was a return on investment (ROI) on moving from a virtualized environment running on OpenStack to a bare-metal environment. Through a six-month proof of concept (POC), we leveraged HP Labs in Houston. We had a four-node system setup with multiple terabytes of data.

We saw 4:1 increase in performance in moving from a VM with the same resources to a bare-metal machine. That’s going to have a significant impact on the way they move data in their environment in the future and how they adjust to customers with larger datasets.

Gardner: When we think about optimizing the architecture and environment for big data, are there any other surprises or perhaps counter-intuitive things that have come up, maybe even converged infrastructure for smaller organizations that want to get in fast and don’t want to be too concerned with the architecture underlying the analytics applications?
That’s going to have a significant impact on the way they move data in their environment in the future and how they adjust to customers with larger datasets.

Harrigan: There's a tendency now with so many free solutions out there to pick a free solution, something that gets the job done now, something that grows the business rapidly, but to forget about what businesses will need three years down the road, if it's going to grow, if it’s going to survive.

There are a lot of startups out there that are able to build a big data infrastructure, scale it to 5,000 nodes, and then they reach a limit. There are network limits on how fast the switch can move data between nodes, constantly pushing the limits of 10 Gbyte, 40 Gyte and soon 100 Gbyte networks to keep those infrastructures up.

Depending on what architecture you choose, you may be limited in the number of nodes you can go to. So there are solutions out there that can process a million transactions per second with 100 nodes, and then there are solutions that can process a million transactions per second with 20 nodes, but may cost slightly more.

If you think long-term, if you start in the cloud, you want to be able to move out of the cloud. If you start with an open ecosystem, you want to make sure that your hardware refresh is not going to cost so much that the company can’t afford it three years down the road. One of the areas we help consult with, when picking different architectures, is thinking long-term. Don't think six weeks down the road, how are we going to get our service up and running? Think, okay, we have a significant client install base, how we are going to grow the business from three to five years and five to 10 years?

Gardner: Given that you have quite a few different types of clients, and the idea of optimizing architecture for the long-term seems to be important, I know with smaller companies there’s that temptation to just run with whatever you get going quickly.

What other lessons can we learn from that long-term view when it comes to skills, security, something more than the speeds and feeds aspects of thinking long term about big data?

Numerous regulations

Harrigan: Think about where your data is going to reside and the requirements and regulations that you may run into. There are a million different regulations we have to do now with HIPAA, ITAR, and money transaction processes in a company. So if you ever perceive that need, make sure you're in an ecosystem that supports it. The temptation for smaller companies is just to go cloud, but who owns that data if you go under, or who owns that data when you get audited?

Another problem is encryption. If you're going to start gaining larger customers once you have a proven technology or a proven service, they're going to want to make sure that you're compliant for all their regulations, not just your regulations that your company is enforcing.

There's logging that they're required to have, and there is going to be encryption and protocols and the ability to do audits on anyone who is accessing the data.

Gardner: On this topic of optimizing, when you do it right, when you think about the long term, how do you know you have that right? Are there some metrics of success? Are there some key performance indicators (KPIs) or ROIs that one should look to so they know that they're not erring on the side of going too commercial or too open source or thinking short term only? Maybe some examples of what one should be looking for and how to measure that.
If you implement a system and it costs you $10 million to run and your ROI is $5 million, you've made a bad decision.

Harrigan: That’s going to be largely subjective to each business. Obviously if you're just going to use a rule of thumb, it shouldn't cost you more money than it makes you. If you implement a system and it costs you $10 million to run and your ROI is $5 million, you've made a bad decision.

The two factors are the value to the business. If you're a large enterprise and you implement big data, and it gives you the ability to make decisions and quantify those decisions, then you can put a number to that and see how much value that big-data system is creating. For example, a new marketing campaign or something you're doing with your remote sites or your retail branches and it’s quantifiable and it’s having an impact on the business.

The other way to judge it is impact on business. So, for ad serving companies, the way they make money is ad impressions, and the more ad impressions they can view, for the least cost in their environment, the higher return they're going to make. The delta is between the infrastructure costs and the top line that they get to report to all their investors.

If they can do 56 billion ad impressions in a day, and you can double that by switching architectures, that’s probably a good investment. But if you can only improve it by 10 percent by switching architectures, it’s probably too much work for what it’s worth.
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Gardner: One last area on this optimization idea. We've seen, of course, organizations subjectively make decisions about whether to do this on-premises, maybe either virtualized or on bare metal. They will do their cost-benefit analysis. Others are looking at cloud and as a service model.

Over time, we expect to have a hybrid capability, and as you mentioned, if you think ahead that if you start in the cloud and move private, or if you start private you want to be able to move to the cloud, we're seeing the likelihood of more of that being able to move back and forth.

Thinking about that, do you expect that companies will be able to do that? Where does that make the most sense when it comes to data? Is there a type of analysis that you might want to do in a cloud environment primarily, but other types of things you might do private? How do we start to think about breaking out where on the spectrum of hybrid cloud set of options one should be considering for different types of big-data activity?

Either-or decision

Harrigan: In the large data analytics world, it’s almost an either-or decision at this time. I don’t know what it will look like in the future.

Workloads that lend themselves extremely well to the cloud are inconsistent, maybe seasonal, where 90 percent of your business happens in December. Seasonal workloads like that lend themselves extremely well to the cloud.

Or, if your business is just starting out, and you don't know if you're going to need a full 400-node cluster to run whatever platform or analytics platform you choose, and the hardware sits idle for 50 percent of the time, or you don’t get full utilization. Those companies need a cloud architecture, because they can scale up and scale down based on needs.

Companies that benefit from on-premise are ones that can see significant savings by not using cloud and paying someone else to run their environment. Those companies typically pin the CPU usage meter at 100 percent, as much as they can, and then add nodes to add more capacity.

The best advice I could give is, if you start in the cloud or you start on bare metal, make sure you have agility and you're able to move workloads around. If you choose one sort of architecture that only works in the cloud and you are scaling up and you have to do a rip and replace scenario just to get out of the cloud and move to on-premise, that’s going to be significant business impact.

One of the reasons I like HP Vertica is that it has a cloud instance that can run on a public cloud. That same instance, that same architecture runs just as well on bare metal, only faster.

Gardner: Chris, last word to you. For those organizations out there struggling with big data, trying to figure out the best path, trying to think long term, and from an architectural and strategic point of view, what should they consider when coming to an organization like Dasher? Where is your sweet spot in terms of working with these organizations? How should they best consider how to take advantage of what you have to offer?

Saso: Every organization is different, and this is one area where that's true. When people are just looking for servers, they're pretty much all the same. But when you're actually trying to figure out your strategy for how you are going to use big-data analytics, every company, big or small, probably does have a slightly different thing they are trying to solve.

That's where we would sit down with that client and really listen and understand, are they trying to solve a speed issue with their data, are they trying to solve massive amounts of data and trying to find the needle in a haystack, the golden egg, golden nugget in there? Each of those approaches certainly has a different answer to it.
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So coming with your business problem and also what you would like to see as a result -- we would like to see x-number of increase in our customer satisfaction number or x-number of increase in revenue or something like that -- helps us define the metric that we can then help design toward.

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Tuesday, September 15, 2015

How content in context within apps and process strengthens marketing muscle

The next BriefingsDirect discussion explores the changing role and impact of content marketing, using the IT industry as a prime example. Just as companies now communicate with their consumers and prospects in much different ways -- with higher emphasis on social interactions, user feedback, big data analysis, and even more content to drive conversations -- so too the IT industry has abruptly changed.

There's more movement to cloud models, to mobile applications, to leveraging data at every chance -- and they are also facing lower-margin subscription business models. The margin for error is shrinking in the IT industry. If any industry is the poster child for how to deal with rapid change on all fronts, including marketing, it's surely the global information technology market.

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

To examine how the IT industry is adjusting to the dynamic nature of marketing, we're joined by Lora Kratchounova, the Founder and Principal at Scratch Marketing and Media in Cambridge, Mass. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Lora, you and I have been talking about marketing for years now. We're in an interesting field, and it’s been such a dynamic time. I have some interesting ideas about where technology is going and where marketing is intercepting, and how they are both changing.

So, let’s start at a high level. Content marketing has proven to be very successful, and you and I have had a hand in this. Creating compelling stories, narratives about what’s going on, and how people can learn from peers as they go through problems and solve them, has become a mainstay in marketing. From your perspective, why is content marketing so important? Why has it been so successful?

Kratchounova: There are couple of reasons for that. The pace of change is tremendous now. People are trying to get their bearings on what’s going on in their markets, and a lot of times, they need to get educated. What has changed with social media now, information is a lot more immediate and transparent, and you can get it from many more sources than the just online presence of a company, for example.

Kratchounova
The top-down modeling in the marketing is changing. We used to rely on companies to tell us how to think about the world, and now we can form our own opinions. As we realize that the customer is in the driver’s seat, they educate themselves, and they make the right decisions about how to go about change, companies are realizing that they need to feed into that flow and be part of that discussion. So content marketing has been so successful, because you become an educator, not just selling to people, and especially in IT.

Gardner: And I think people have become much more accustomed to conversations, rather than just a one-direction information flow. "We're the seller and we're going to tell you what it is." Now, people want to relate. They want to hear what others have to think. It’s much more of an actual conversation.

Ongoing conversation

Kratchounova: Exactly. Look at any IT domain. It’s interesting when we look at who is influencing and who the main voices in it are, who the voices that people consider experts are. You pretty much consistently see reporters, journalists, and the analysts folks like you, but then we see that there are a lot of C-level executives from IT companies who are becoming that kind of a voice as well.

That just points to the need for that ongoing conversation, the need for sharing at all levels of the buyer funnel. Once people have bought into a selection, they need to make sure of adoption, and they are maximizing the investment.

So the conversation is very important, and the immediacy of having access to folks and having the ability to exchange a few thoughts on Twitter or LinkedIn has changed the dynamic completely. So it’s absolutely about conversations and storytelling, but it's still mapped to the buyer’s funnel.
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People are still educating and still looking at options for a change or for replacement, one or the other, until they select the people they want to work with. And it’s usually people in brands. It's not just that they want to work with this company, but the people behind it. We're moving more to a people economy.
Gardner: As you point out, you can get to the real source of the knowledge nowadays. Publishing is available to anybody whether they're tweeting, blogging, posting on Facebook, or putting something up on their company website. Anybody who has something to say can say it. It can get indexed and it can be made available to anybody who wants to hear about that particular topic.
The ability to publish is great, and it democratizes the means of how we communicate with each other and educate each other, but yet you still have to earn it.

Most people now don’t just sit back and wait for information to reach them. They're proactive. They go out, they start to search, they do hashtag searches on Twitter, and they can do Google or Bing on web.

It’s much more of, "I know something; I'm putting it out there." And there's another case of someone saying, "I need to know something; I am seeking it." They come together on their own. The content makes that possible. The better the content, the better the likelihood that those in a need to know and those in a need to tell come together.

Kratchounova: Exactly, but I think you hit on something very important. Everybody can publish, and a lot of people are publishing. Yet, we're interested in a love for your people, falling in love for your people, and what they have to say.

The ability to publish is great, and it democratizes the means of how we communicate with each other and educate each other, but yet you still have to earn it. This is very important. People who really are influential are usually domain experts and they're there to help other people. That’s the other aspect of it that both companies and their marketing teams and their executives need to think about. You have to actively participate and show your expertise, it doesn’t come for granted.

Important of curation

Gardner: And there's another aspect to greasing the skids between the knowledge and the acquirer of the knowledge, and that is content curation. There are people who point at things, give it credence, and say that it's a good thing, you should read it; or that’s a bad thing, don’t waste your time -- and that helps refine this.

Kratchounova: It’s pretty exciting.

Gardner: There are machines doing the same thing. There are algorithms, there's indexing, there's both human and machine aspects of winnowing down the good stuff and providing it to people in a need to know, and that’s when we are going to get more powerful.

Kratchounova: Great. I'm sure you know about Narrative Science. I've had a professional crush on this company for few years now. They take data, turn it into storytelling, and they think this is phenomenal. Obviously, that’s not going to replace some of the human storytelling that needs to happen, but some of the data storytelling will come from technology. This is one particular application where marketing and technology come together to bring something completely new into life.

Gardner: So we can get knowledge through expertise or we can get knowledge through experience, someone who has gone through it already and is willing to share that with you. If you're acquiring IT, it’s super important to avail yourself of everything, because it changes so rapidly and the costs are high.
IT depends on the IT buyer, because we can’t necessarily lump them together and ask how the IT buyer goes about it. There are people with different needs, and it depends on their role.

If you make a big mistake in how you're designing a data center, you're out millions of dollars, your products don’t work, and your front office are going to come screaming down on you. You have to make the big decisions and you have to make them correctly in IT. It’s not just a service to the business; it is the business.

So, let’s think about the IT industry in particular, and then think about how content marketing as we’ve discussed is powerful. How do IT people acquire content marketing? Do they get it through websites, emails, or tweets? Is it delivered to them at a webinar that they opt into? How does content marketing reach somebody who's an IT buyer?

Kratchounova: IT depends on the IT buyer, because we can’t necessarily lump them together and ask how the IT buyer goes about it. There are people with different needs, and it depends on their role. If you're CIO or CTO, there is a different mix of channels and sources you use. If you're on the dev or on the ops side and looking for specific solutions, you're going into completely different channels.

For example, if you're a DevOps professional, you're maybe on Stack Overflow and you might be seeking advice from other folks. You might be on GitHub and sharing open-source code and getting feedback on that.

If you're a CIO or CTO, what we have found working with number of different companies, be that global companies or maybe companies that are growing, is that they do seek their peers to validate what the peers are going through. One of the best things that companies can do, when they try to talk to the C-level, is expose some of those connections that they already have from their customers. Make sure that the customers are part of the discussion, and they can chime in.

Another important source of information for the C level in IT would be folks like you, analysts, and strategic system integrators like Accenture and Deloitte, because these folks are exposed to the kinds of challenges that a CIO or CTO would go through. So they have a lot to bring to the table in terms of risk mitigation, optimal deployment, and maximization of the investment in IT. Making those connections and sharing those experiences we have seen work really, really well.

Let me just throw this in as well. The other thing we have seen is that the C level is still going on Google. They're still doing the searches. We have compelling data, across the board, that in any B2B complex enterprise environment folks are self-educating as well. So it’s not a question of either/or; it’s what’s the right mix for each company depending on channels, depending on where people sit.

Spectrum of content

Gardner: So there is a spectrum of content, some highly technical and defined, on places like GitHub that are germane to a technologist. Then, there is that spectrum up from there to a higher level toward peer review of products and peer review of solutions. Then, there are more business topics about what is strategic, what’s the forward direction, how do I understand at an architectural-level decision processes, and where can I go for more information to find out what’s coming down the pike and then put it in place.

Kratchounova: Think about Spiceworks. They're probably at five million IT professionals at this point, and the community is there for a reason. So again, with each particular, there isn’t one size fits all. One thing that we always recommend to folks is that if you’re looking to develop an influential strategy and approach IT, it really depends on what domains you span.

You find that even if you're doing mobile application development, the folks who were really influential and set the standards of that stage are somewhat different from the folks who are concerned with security in mobile app development. So there isn’t necessarily one pool of influencers that you need to go then to develop a relationship and understand what’s in their mind. It really depends on your domain.

Gardner: So if you're a marketer and you recognize that quality content is super important, you need to have a spectrum of content. It needs to be some content that would be germane to a technologist that’s highly detailed, a how-to type. You need to have peer review and stories, case studies, testimonial type content where the customer is telling what they’ve done, why it benefited them, and what you can learn from that.

You also need to have higher-level discussions with experts to help people chart the next course, the strategic level. So content needs to come across a spectrum, and we recognize that the way in which people get that content might be through search. It might be through web, e-mail, webinars, webcasts, reading certain online sites, listening to certain Twitter feeds or groups, or having a select group of people that you follow. All of that happens.

But what’s interesting to me, Lora, is that all has to do with the web. But what we're seeing in IT is a rapid movement toward mobile apps, rather than just the web. And in many cases, they're starting to overtake the web as to where people spend their time. I'm sure you're using a smartphone and you have mobile apps. You're not going on the web to find a cab; you’re going to the Uber app to find a cab.

If you're looking for a restaurant review, you’re not necessarily going on the web and doing a search. You’re going into a specific app on Yelp, OpenTable, or somewhere else to find out where your restaurants are and you’re going into Google Maps to find out how to get there.
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So more-and-more, we're seeing, on the consumer side, people using mobile apps for more of their processes, for their inquiry, for their actual productivity. Then, on the enterprise side, the business-to-employee (B2E) side, we're seeing people using cloud services.

We're moving more toward mobile applications, cloud services, an API-driven world that leverages big data and analytics in order to put context into process. It's all about user experiences, and mobile delivers the best. How then does content continue to reach people? Do we lose the ability to deliver content when they are in apps?

Different perspective

Kratchounova: I have a different perspective on what you're describing. I don’t know that we are moving to a mobile app experience necessarily. When we think about the apps and the examples you gave -- Yelp or Uber -- yes, they're best-of-breed applications that we use because these are the most frequently used applications.

But what you're seeing is actually a digital transformation. Digital no longer means the web, as we know it, going online through your computer. You're actually navigating on a mobile device. So it’s this digital transformation that’s happening, and the trend that we're seeing is aggregation.

It’s not about one individual app, but it’s more about what is the Flipboard within the enterprise. You're seeing that sort of aggregation bubbling up to the top because information overload is a huge problem. People can’t prioritize anymore. They can’t toggle among those different applications and companies.

For example, one of our clients, not to necessarily add a plug for them, actually is very germane to the discussion. Harmon.ie does exactly that.
Once you understand, then you understand what a partner is trying to do. Why are they are here, what’s the context, what’s the most logical next step or the optimal next step?

In those kinds of environments, what we're finding and where I totally agree with you, is the ability to read and understand context, so that you can support the user, be that an employee with internal work experience, or external customers, to support them to get the job done.


The role of content is actually merging with big data, because big data is helping us to understand context and say, "What do we serve this person here?" On the marketing side, and the lingo side it’s more about ongoing customer journeys. Think about the same thing on the employee side, ongoing employee journeys or partner journeys.

Once you understand, then you understand what a partner is trying to do. Why are they are here, what’s the context, what’s the most logical next step or the optimal next step? Now, content becomes both an ability for people to find something, but also for marketers or product development folks. I think those functions are emerging as well to deliver the right content in the right format so that the user can get the job done. That’s my perspective on that.

Gardner: There's no disagreement from me on this issue of context to process, context to location, context to need for knowledge all being much more granular and powerful going forward. What I am concerned about is that, when I talk to developers, the vast majority of them are much more interested in a mobile-first, cloud-first world.

They're not much interested in building what we used to think of as big honking applications in the enterprise. They're much more interested in how to bring services -- and microservices -- together in context to provide a better productive outcome and how to leverage low-cost services in APIs and from any cloud.

Discovering inference

So, to me, it becomes, on one hand, all the more important to have the ability to deliver content contextually into these processes, but at the same time these processes are becoming fragmented. They're going across hybrid-cloud environments, they include both what we call cloud and SaaS, and I'm not sure where the marketer now can get enough inference to support the injection of content appropriately.

The ways that it’s been done now is usually through the web where we have links, and we have code, and we can do cookies. It’s sort of like, it’s Web 1.0 mechanisms by which marketers are injecting content, but we are moving not only pass Web 2.0, we're into Web 3.0  cloud platform. To me this is a big question mark.

Kratchounova: It is a question mark. I don’t know that there is going to be one mode of delivering what we're talking about or one approach or one framework. I'll give you one example. Look at how web content management has changed. It used to be about managing pages and updating content. Now, web content management is becoming the Marketing Command Center, if you look at a web content management system like Sitefinity, for example.

Now, marketers can deal with the customer through his own mobile and on the web, so they can inject the content that needs to happen there. The reason they can do this now is because there is this ability, the analytics that come from all of these customer interactions of you, actually creating cohorts of people as they're going through your web experience or online experience. You know why they're there and what’s the optimal path for them to get where they need to be.
You're seeing this ability to distribute content to post content to people, but in a much more contextual way. So, there is going to be a pull and push, but the push is getting a lot smarter and very contextual.

So, you're seeing this ability to distribute content to post content to people, but in a much more contextual way. So, there is going to be a pull and push, but the push is getting a lot smarter and very contextual.

Gardner: So it’s incumbent upon us who are examining this marketing evolution in the context of the IT industry to create that spectrum of content to make it valuable, to make it appropriate and not too commercial or crass, but useful. And at the same time now, think about how to get this in front of right people at the right time.

It seems to me that if I'm an IT company, and more and more of my services, whether it’s a B2B, B2C, B2E, or all of the above, I need to be thinking about ways that I'm going to communicate with my existing universe or market and move them toward new products and services as they need them in context of their process.

Think about this in a B2C environment in retail, where I am walking through Wal-Mart. I have my smartphone and, as I turn the corner, they know that now I am interested in home goods, and they are going to start to incentivize me to buy something. That’s kind of an understood mechanism by which my location and the fact that I turned a corner and made a decision provides an inference that then they can react to with content or information.

But take that now to the B2B environment where I'm in a business setting. I'm in procurement, I'm in product development, or I'm looking for a supply chain efficiency. I want to move into a new geographic location and I need to find the means to do that. All of those things are also like turning a corner in a Wal-Mart, except you're in a business application using cloud services, using a mobile device and apps.

If I'm an IT vendor, I'm going to want to have content or information that I can bring to that situation, perhaps even through an example of what other people have done when they face that same process crossroads. So the content can be more important and more germane. These are multi-million-dollar decisions in some cases.

Don’t you think that big companies should be starting to make content with the idea that it’s going to become part of their application services, part of their cloud delivery services, and that they need to use big data and analytics to know when to inject it?

Understanding context

Kratchounova: I absolutely agree. I think that difference between the example you just gave for Wal-Mart and a B2B environment is that, in Wal-Mart, you don’t need to understand so much about who the person is, what their role is, whether they work at an accounting firm or whether they are a physician, for example.

In a B2B environment you do need to understand context, and context is the location or the point where they are in their journey, whatever that journey maybe, and their role as well, because different people do have different decisions to make.

It’s a little bit more complex to bring context in a B2B environment, but it’s absolutely essential. You used the word inference. We always get enamored by the concept of the big data and guess what, once the machines are there, they're going to analyze everything and it's going to be this perfect world of marketing where everyone is aligned. 

Just look at the history of marketing. We don’t know ourselves as people. We individually don’t know ourselves as well, let alone someone else getting to know us that well. Inference is very important, but it’s going to be a balance between inferring what the person needs and allowing the person to customize this experience as well. So it’s going to come both ways.
Some people still believe that it’s a relationship-based world and, therefore, there's no need for a digital experience for their customers or for their potential buyers, which is actually never the case.

Some people going to one extreme or the other. Some people still believe that it’s a relationship-based world and, therefore, there's no need for a digital experience for their customers or for their potential buyers, which is actually never the case. Other people believe that it’s all digital; therefore they don’t need to touch them in any other way, which is rarely the case, especially in IT. 

Gardner: I also suggest to you that the data is more readily available, because I, as an employer, as a corporation, control what’s going on. I know what that employee is doing. I know what apps they're using. I know what data they're seeking. 

They're going to provide a feed of data back to you about what’s going on, on those apps from your very own employees.

What I'm suggesting then, as we begin to think about closing out this fascinating conversation, is that you need to have content, stories, and customers lined up, so that you can uncover their path to truth, their path to value, and have that content context-ready. Not only you are going to be using it in webinars, webcasts, podcasts, blogs, but pretty soon, if my hypothesis is correct, you're going to be using that content in the context of process and inside of applications in cloud services and on mobile devices.

Way of the future

Kratchounova: Maybe this is an opportunity, because it is the way of the future, and some people are more mature and others are less mature, but maybe we can bring other people into the discussion and see what other folks in the field think about where the content is going, how to contextualize and how to deliver it. One of the biggest question is how do we scale this. You can still do a meaningful experience or create a meaningful experience one-on-one, but it’s hard to recreate that even if your customers are 200, 500, or even 5,000 within the IT space. 

Gardner: You also have to remember that people's connections to apps, cloud services and context-aware processes are only going to increase. The Internet of Things and new classes of devices like the Apple Watch are expanding the end points and ways to connect to them. One of the things that’s important with the Apple Watch functionally is that it’s very good at alerts and notifications. It can also detect a lot of context of what you're doing physically and your location, and it can relate, because it integrates to your phone, with what you're doing with applications and cloud services.

Wouldn’t it be interesting if you're wearing an Apple Watch or equivalent, you're in a business setting, and you come up against a problem that you might not even know yet, but all of these services working together are going to say, "That person is going to be facing a problem; they are going to need to make a decision. Let’s put some information, content, and use cases together for them that will help them as they face that situation to make a better decision." That’s the kind of role I think we're heading toward. 

Before we sign off, Lora, tell me more about Scratch Marketing and Media, what you do and why that’s related to this discussion we have had today.
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Kratchounova: Scratch Marketing and Media is an integrated marketing agency. We help B2B technology companies with market growth. Sometimes that means helping the sales folks within IT companies and sometimes it means working with the marketing folks on things like content marketing programs, PR, and all its relations, and influence their relations in social media.

Gardner: And how could they find out more information about Scratch Marketing Media?

Kratchounova: You can go online at www.scratchmm.com.

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