Monday, June 10, 2019

CEO Henshall on Citrix’s 30-year journey to make workers productive, IT stronger, and partners more capable

The next BriefingsDirect intelligent workspaces discussion explores how for 30 years Citrix has pioneered ways to make workers more productive, IT operators stronger, and a vast partner ecosystem more capable.

We will now hear how Citrix is by no means resting on its laurels by charting a new future of work that abstracts productivity above apps, platforms, data, and even clouds. The goal: To empower, energize, and enlighten disaffected workers while simplifying and securing anywhere work across any deployment model.

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

To hear more about Citrix’s evolution and ambitious next innovations, please welcome David Henshall, President and CEO of Citrix. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: To me Citrix is unique in that for 30 years it has been consistently disruptive, driven by vision, and willing to take on both technology and culture -- which are truly difficult things to do. And you have done it over and over again.

As Citrix was enabling multiuser remote access -- or cloud before there was even a word for it -- you knew that changing technology for delivering apps necessitated change in how users do their jobs. What’s different now, 30 years later? How has your vision of work further changed from delivery of apps?

Do your best work

Henshall: I think you said it well. For 30 years, we have focused on connecting people and information on-demand. That has allowed us to let people be productive on their terms. The fundamental challenge of people is to have access to the tools and resources necessary to get their jobs done -- or as we describe it, to do their best work.

We look at that as an absolute necessity. It’s one of the things that makes people feel empowered, feel accomplished, and it allows them to drive better productivity and output. It allows engagement at the highest levels possible. All of these have been great contributing factors.

What’s changed? The technology landscape continues to evolve as applications have evolved over the years – and so have we. You referred to the fact that we’ve reinvented ourselves many times in the last three decades. All great companies go through the same regeneration against a common idea, over-and-over again. We are now in what I would describe as the cloud-mobile era, which has created unprecedented flexibility from the way people used to manage IT. Everything from new software-as-a-service (SaaS) services are being consumed with much less effort, all the way to distributed edge services that allow us to compute in new ways that we’ve never imagined.

And then, of course, on the device side, the choices are frankly nearly infinite. Being able to support the device of your choice is a critical part of what we do -- and we believe that matters.

Gardner: I was fortunate enough to attend a press conference back in 1995 when Citrix WinFrame, as it was called at that time, was delivered. The late Citrix cofounder Ed Iacobucci was leading the press conference. And to me, looking back, that set the stage for things like desktop as a service (DaaS), virtual desktop infrastructure (VDI), multi-tenancy, and later SaaS. We all think of these as major mainstream technologies.

Do you feel that what you’re announcing about the future of work, and of inserting intelligence in context to what people do at work, will similarly set off a new era in technology? Are we repeating the past in terms of the scale and magnitude of what you are biting off?

Future productivity goes beyond products 

Henshall: The interesting thing about the future is that it keeps changing. Against that backdrop we are rethinking the way people work. It’s the same general idea about just giving people the tools to be productive on their terms.

A few years back that was about location, of being able to work outside of a traditional office. Today more than half the people do not work in a typical corporate headquarters environment. People are more distributed than ever before.

The challenge we are now trying to solve takes it another step forward. We think about it from a productivity standpoint and an engagement template. The downside of technology is that it does make everything possible. So therefore the level of complexity has gone up dramatically. The level of interruptions -- and what we call context shifting -- has gone up dramatically. And so, we are looking for ways to help simplify, automate common workflows, and modernize the way people engage with applications. All of these point toward the same common outcome of, “How do we make people more productive on their terms?”

Gardner: To solve that problem of location flexibility years ago, Citrix had to deal with the network, servers, performance and capacity, and latency -- all of which were invisible. End users didn’t know that it was Citrix behind-the-scenes.

Will people know the Citrix name and associate it with workspaces now that you are elevating your value above IT?
Henshall: We are solving broader challenges. We have moved gradually over the years from being a behind-the-scenes infrastructure technology. People have actually used the company’s name as a verb. “I have Citrixed into my environment,” for example. That will slowly evolve into still leveraging Citrix as a verb, but meaning something like, “I Citrixed to get my job done.” That takes on an even broader definition around productivity and simplification, and it allows us more degrees of freedom.

We are working with ecosystem partners across the infrastructure landscape, all types of application vendors. We therefore are a bridge between all of those. It doesn’t mean we necessarily have to have our name front and center, but Citrix is still a verb for most people in the way they think about getting their jobs done.

Gardner: I commend you for that because a lot of companies can’t resist making their name part-and-parcel of a solution. Perhaps that’s why you’ve been such a good partner over the years. You’ve been supplying a lot of the workhorses to get jobs done, but without necessarily having to strut your stuff.

Let’s get back to the issues around worker talent, productivity, and worker user experience. It seems to me we have lot of the bits and parts for this. We have great apps, great technology, and cloud distribution. We are seeing interactivity via chatbots, and robotic programming automation (RPA).

Why do you think being at the middle is the right place to pull this all together? How can Citrix uniquely help, whereas none of the other individual parts can?

Empower the people, manage the tech

Henshall: It’s a problem they are all focused on solving. So take a SaaS application, for example. You have applications that are incredibly powerful, best of breed, and they allow for infinite flexibility. Therein lies part of the challenge. The vast majority of people are not power users. They are not looking for every single bell and whistle across a workflow. They are looking for the opportunity to get something done, and it’s usually something fairly simple.

We are designing an interface to help abstract away a lot of complexity from the end user so they can focus on the task more than the technology itself. It’s an interesting challenge because so much technology is focused on the tech and how great and powerful and inflexible it is, and they lose sight of what people are trying to accomplish.
We start by working backward. We start with the end user, understand what they need to be productive, empowered, and engaged. We let that be a guiding principle behind our roadmap. That gives us flexibility to empathize, to understand more about customers.

We start by working backward. We start with the end user, understand what they need to be productive, empowered, and engaged. We let that be a guiding principle behind our roadmap. That gives us flexibility to empathize, to understand more about customers and end users more effectively than if we were building something purely for technology’s sake.

Gardner: For younger workers who have grown up all-digital all the time, they are more culturally attuned to being proactive. They want to go out and do things with choice. So culturally, time is on your side.

On the other hand, getting people to change their behaviors can be very challenging. They don’t know that it could be any better, so they can be resistant. This is more than working with an IT department on infrastructure. We are talking about changing people’s thinking and how they relate to technology.

How do you propose to do that? Do you see yourself working in an ecosystem in such a way that this is not just, “If we build it, they will come,” affair, but evangelizing to the point where cognitive patterns can be changed?

Henshall: A lot of our relationships and conversations have been evolving over the last few years. We’ve been moving further up what I would call “the IT hierarchy.” We’re having conversations with CIOs now about broad infrastructure, ways that we can help address the use cases of all their employees, not just those that historically needing all the power of virtualization.

But as we move forward, there is a large transformation going on. Whether we use terms like digital transformation and others, those are less technology conversations and more about business outcomes – more than any time in my 30-year-career.

Because of that, you’re not only engaging the CIO, you may have the same conversation with a line of business executive, a chief people officer, the chief financial officer (CFO), or someone in another functional organizations. And this is because they’re all trying to accomplish a specific outcome more than focusing on the technology itself.

And that allows us to elevate the discussion in a way that is much more interesting. It allows us to think about the human and business outcomes more so than ever before. And again, it’s just one more extension of how we are getting out of the “technology for technology’s sake” view and much more into the, “What is it that we are actually trying to accomplish” view.

Gardner: David, as we tackle these issues, elevate the experience, and let people work the way they want, it seems we are also opening up the floodgates for addition of more intelligence.

Whether you call it artificial intelligence (AI), machine learning (ML), or augmented intelligence, the fact is that we are able to deal with more data, derive analytics from it, learn patterns, reapply those learning lessons, and repeat. So injecting that into work, and how people get their jobs done, is the big question these days. People are trying to tackle it from a variety of different directions.

You have said an advantage Citrix has, is in access to data. What kind of data are we talking about, and why is that going to put Citrix in a leadership position?

Soup to nuts supervision of workflow 

Henshall: We have a portfolio that spans everything from the client device through the application, files, and the network. We are able to instrument many different parts of the entire workflow. We can capture information about how people are using technologies, what their usage patterns look like, where they are coming in from, and how the files are being used.

In most cases, we take that and apply it into contextual outcomes. For example, in the case of security, we have an analytics platform and we use those security analytics. We can create a risk score that’s very similar to your credit score for an individual user’s behavior if something anomalous happens. For example, you’re here with me and you’re in front of your computer, but you also tried to log on from another part of the globe at the same time.

Things like that can be flagged almost instantaneously and allows the organization to identify and -- in many cases -- automatically address those types of scenarios. In that case, it may immediately ask for two-factor authentication.

We are not capturing personally identifiable information (PII) and other types of broader data that fall under a privacy umbrella. We access a lot of anonymized things that provide the insights.
Citrix operates in about 100 countries around the world. We are already very familiar with local compliance and data privacy regulations. We are making sure that we can operate within those and give our customers in those markets the tools to make sure they are operating within those constraints as well.

Every company has [had privacy discussions] and will continue to evolve over time as technology evolves because the underlying platforms are becoming very powerful. Citrix operates in about 100 countries around the world. We are already very familiar with local compliance and data privacy regulations. We are making sure that we can operate within those and certainly give our customers in those markets the tools to make sure that they are operating effectively within the constraints as well.

Gardner: The many resources people rely on to do their jobs come from different places -- public clouds, private clouds, a hybrid between them, different SaaS providers, and different legacy systems of record.

You are in a unique position in the middle of that. You can learn from it and begin to suggest how people can improve. Those patterns can be really powerful. It’s not something we’ve been able to do before.

What do we call that? Is it AI? Or a valet or digital assistant to help in your work while protective of privacy and adhering to all the laws along the way? And where do you see that going in terms of having an impact on the economy and on companies?

AI, ML to assist and automate tasks

Henshall: Two very broad questions. From the future standpoint, AI and ML capabilities are helping turn all the data we have into more useful or actionable information. And in our case, you mentioned virtual assistance. We will be using intelligent assistance to help you automate simple tasks.

And many of those could be tasked between applications. For example, you could ask your assistant to move a meeting to next Thursday or any time your other meeting participants happen to be available. The bots will go out, search for that optimal time, and take those actions. Those are the types of things that we envision more for the virtual assistants going forward, and I think those will be interesting.

Beyond that, it becomes a learning mechanism whereby we can identify that your bot came back and told you you’ve had the same conflict two meetings in a row. Do you want to change all future meetings so that this doesn’t happen again? It can become much more predictive.

And so, this journey that Citrix has been on for many years started with helping to simplify IT so that it became easier to deliver the infrastructure. The second part of that journey was making it easier for people to consume those resources across the complexities we have talked about.
Now, the products we announced at our May 2019 Citrix Synergy Conference are more about guiding work to help simplify the workflows. We will be doing more in this last space on how to anticipate what you will need so that we can automate it ahead of time. And that’s an interesting journey. It will take a few years to get there, but it’s going to be pretty powerful when we do.

Gardner: As you’re conducting product development, I assume you’re reflecting these capabilities back to your own workforce, the Citrix global talent pool. Do you drink your own champagne? What are you finding? Does it give you a sense as the CEO that your workforce has an advantage by employing these technologies? Do we have any proof points that the productivity is in fact enhanced?

Henshall: It’s still early days. A lot of these are brand-new technologies that don’t have enough of a base of learning yet.

But some of the early learnings can identify areas where you’re multitasking too much, or are in an inefficient process, or in my case, I tend to look at automating opportunities for how much I am multitasking inside of a meeting. That helps me understand whether I should be in that meeting in the first place, whether I am a 100 percent focused and committed -- or have I been distracted by other elements.

Those are interesting learnings that are more about personal productivity and how we can optimize from that respect.

More broadly speaking, our workforce is globally distributed. We absolutely drink our own champagne when it comes to engaging a global team. We have teams now in about 40 countries around the world and we are very, very virtual. In fact, among my leadership team, I am the only member that lives full-time in [Citrix’s headquarters] in South Florida. We make that work because we embrace all of our own technology, stay on top of common projects, communicate across all the various mediums, and collaborate where need be.

That allows us to tap into nontraditional workforce populations, to differentiate, and enable folks who need different types of flexibility for their own lifestyles. You miss great talent if you are far too rigid. Personally, I believe the days are gone when everybody is expected to work inside a corporate headquarters. It’s just not practical anymore.

Gardner: For those businesses that recognize there is tremendous change afoot, are using new models like cloud, and don’t want complexity to outstrip productivity – what advice do you have for them as they start digital transformation efforts? What should they be putting in place now to take advantage of what companies like Citrix will be providing them in a few years?

Business-first supports global collaboration 

Henshall: The number one thing on any digital transformation project is to be currently clear about what the outcome is you are trying to achieve. Start with the outcome and work backward. You can leverage platforms like Citrix, for example, to look across multiple technologies, focus on those business outcomes, and leave the technology decision in many cases to last. It shouldn’t be the other way around because if you do, you will self-limit what those outcomes should be.

Make sure you have buy-in across all stakeholders. As I talked about earlier, have a conversation with the CFO, head of marketing, head of human resources, and many others. Look for breadth of outcomes, because you don’t want to solve problems for one small team, you want to solve problems across the enterprise. That’s where you get the best leverage. It allows you the best opportunity to simplify the complexity that has built up over the last 30 to 40 years. This will help people get out from under that problem.

Gardner: Lastly, for IT departments specifically, the people who have been most aware of Citrix as a brand, how should IT be thinking about entering this new era of focusing on work and productivity? What should IT be thinking about to transform themselves to be in the best position to attain these better business outcomes?

Henshall: I have already seen the transformation happening. Most IT administrators want to focus on larger business problems, more than just maintaining the existing infrastructure. Unfortunately, the budgets have been relatively limited for innovation because of all the complexity we have talked about.
But my advice for everyone is, take a step back, understand how to be the champion of the business, to be the hero by providing great outcomes, great experiences, and higher productivity. That’s not a technology conversation first and foremost. Obviously it has a technology element but understand and be empathetic of the needs of the business. Then work backward, and Citrix will help you get there.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Citrix.

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Friday, June 7, 2019

How real-time data streaming and integration set the stage for AI-driven DataOps

The next BriefingsDirect business intelligence (BI) trends discussion explores the growing role of data integration in a multi-cloud world.

Just as enterprises seek to gain more insights and value from their copious data, they’re also finding their applications, services, and raw data spread across a hybrid and public clouds continuum. Raw data is also piling up closer to the edge -- on factory floors, in hospital rooms, and anywhere digital business and consumer activities exist.

Stay with us now as we examine the latest strategies for uniting and governing data wherever it resides. By doing so, businesses are enabling rapid and actionable analysis -- as well as entirely new levels of human-to-augmented-intelligence collaboration.

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

To learn more about the foundational capabilities that lead to a total data access exploitation, we’re now joined by Dan Potter, Vice President of Product Marketing at Attunity, a Division of Qlik. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Dan, what are the business trends forcing a new approach to data integration?

Potter: It’s all being driven by analytics. The analytics world has gone through some very interesting phases of late: Internet of Things (IoT), streaming data from operational systems, artificial intelligence (AI) and machine learning (ML), predictive and preventative kinds of analytics, and real-time streaming analytics.

So, it’s analytics driving data integration requirements. Analytics has changed the way in which data is being stored and managed for analytics. Things like cloud data warehouses, data lakes, streaming infrastructure like Kafka -- these are all a response to the business demand for a new style of analytics.

As analytics drives data management changes, the way in which the data is being integrated and moved needs to change as well. Traditional approaches to data integration – such as batch processes, more ETL, and scripted-oriented integration – are no longer good enough. All of that is changing. It’s all moving to a much more agile, real-time style of integration that’s being driven by things like the movement to the cloud and the need to move more data in greater volume, and in greater variety, into data lakes, and how do I shape that data and make it analytics-ready.

With all of these movements, there have been new challenges and new technologies. The pace of innovation is accelerating, and the challenges are growing. The demand for digital transformation and the move to the cloud has changed the landscape dramatically. With that came great opportunities for us as a modern data integration vendor, but also great challenges for companies that are going through this transition.

Gardner: Companies have been doing data integration since the original relational database (RDB) was kicked around. But it seems the core competency of managing the integration of data is more important than ever.

Innovation transforms data integration

Potter: I totally agree, and if done right, in the future, you won’t have to focus on data integration. The goal is to automate as much as possible because the data sources are changing. You have a proliferation of NoSQL databases, graph databases; it’s no longer just an Oracle database or RDB. You have all kinds of different data. You have different technologies being used to transform that data. Things like Spark have emerged along with other transformation technologies that are real-time-oriented. And there are different targets to where this data is being transformed and moved to.
It's difficult for organizations to maintain the skills set -- and you don’t want them to. We want to move to an automated process of data integration. The more we can achieve that, the more valuable all of this becomes. You don’t spend time with mundane data integration; you spend time on the analytics -- and that’s where the value comes from.

Gardner: Now that Attunity is part of Qlik, you are an essential component of a larger undertaking, of moving toward DataOps. Tell me why automated data migration and integration translates into a larger strategic value when you combine it with Qlik?

Potter: DataOps resonates well for the pain we’re setting out to address. DataOps is about bringing the same discipline that DevOps has brought to software development. Only now we’re bringing that to data and data integration for analytics.

How do we accelerate and remove the gap between IT, which is charged with providing analytics-ready data to the business, and all of the various business and analytics requirements? That’s where DataOps comes in. DataOps is technology, but that’s just a part of it. It’s as much or more about people and process -- along with enabling technology and modern integration technology like Attunity.

We’re trying to solve a problem that’s been persistent since the first bit of data hit a hard drive. Data integration challenges will always be there, but we’re getting smarter about the technology that you apply and gaining the discipline to not boil the ocean with every initiative.

The new goal is to get more collaboration between what business users need and to automate the delivery of analytics-ready data, knowing full-well that the requirements are going to change often. You can be much more responsive to those business changes, bring in additional datasets, and prepare that data in different ways and in different formats so it can be consumed with different analytics technologies.

That’s the big problem we’re trying to solve. And now, being part of Qlik gives us a much broader perspective on these pains as relates to the analytics world. It gives us a much broader portfolio of data integration technologies. The Qlik Data Catalyst product is a perfect complement to what Attunity does.
Our role in data integration has been to help organizations move data in real-time as that data changes on source systems. We capture those changes and move that data to where it's needed -- like a cloud, data lake, or data warehouse. We prepare and shape that data for analytics.

Our role in data integration has been to help organizations move data in real-time as that data changes on source systems. We capture those changes and move that data to where it’s needed -- like a cloud, data lake, or data warehouse. We prepare and shape that data for analytics.

Qlik Data Catalyst then comes in to catalog all of this data and make it available to business users so they can discover and govern that data. And it easily allows for that data to be further prepared, enriched, or to create derivative datasets.

So, it’s a perfect marriage in that the data integration world brings together the strength of Attunity with Qlik Data Catalyst. We have the most purpose-fit, modern data integration technology to solve these analytics challenges. And we’re doing it in a way that fits well with a DataOps discipline.

Gardner: We not only have the different data types, we have another level of heterogeneity to contend with and that’s cloud, hybrid cloud, multi-cloud, and edge. We don’t even know what more is going to be coming in two or three years. How does an organization stay agile given that level of dynamic complexity?

Real-time analytics deliver agility 

Potter: You need a different approach for a different style of integration technology to support these topologies that are themselves very different. And what the ecosystem looks like today is going to be radically different two years from now.

The pace of innovation just within the cloud platform technologies is very rapid. Just the new databases, transformation engines, and orchestration engines -- it’s just proliferates. And now you have multiple cloud vendors. There are great reasons for organizations to use multiple clouds, to use the best of the technologies or approaches that work for your organization, your workgroup, your division. So you need that. You need to prepare yourself for that, and modern integration approaches definitely help.

One of the interesting technologies to help organizations provide ongoing agility is Apache Kafka. Kafka is a way to move data in real-time and make the data easy to consume even as it’s flowing. We see that as an important piece of the evolving data infrastructure fabric.

At Attunity we create data streams from systems like mainframes, SAP applications, and RDBs. These systems weren’t built to stream data, but we stream-enable that data. We publish it into a Kafka stream and that provides great flexibility for organizations to, for example, process that data in real time for real-time analytics such as fraud detection. It’s an efficient way to publish that data to multiple systems. But it also provides the agility to be able to deliver that data widely and have people find and consume that data easily.
Such new, evolving approaches enable a mentality that says, “I need to make sure that whatever decision I make today is going to future-proof me.” So, setting yourself up right and thinking about that agility and building for agility on day one is absolutely essential.

Gardner: What are the top challenges companies have for becoming masterful at this ongoing challenge -- of getting control of data so that they can then always analyze it properly and get the big business outcomes payoff?

Potter: The most important competency is on the enterprise architecture (EA) level, more than on the people who traditionally build ETL scripts and integration routines. I think those are the piece you want to automate.

The real core competency is to define a modern data architecture and build it for agility so you can embrace the changing technologies and requirements landscape. It may be that you have all of your eggs in one cloud vendor today. But you certainly want to set yourself up so you can evolve and push processing to the most efficient place, and to attain the best technology for the kinds of analytics or operational workloads you want.

That’s the top competency that organizations should be focused on. As an integration vendor, we are trying to reduce the reliance on technical people to do all of this integration work in a manual way. It’s time-consuming, error-prone, and costly. Let’s automate as much as we can and help companies build the right data architecture for the future.

Gardner: What’s fascinating to me, Dan, in this era of AI, ML, and augmented intelligence is that we’re not just creating systems that will get you to that analytic opportunity for intelligence. We are employing that intelligence to get there. It’s tactical and strategic. It’s a process, and it’s a result.

How do AI tools help automate and streamline the process of getting your data lined up properly?

Automated analytics advance automation 

Potter: This is an emerging area for integration technology. Our focus initially has been on preparing data to make it available for ML initiatives. We work with vendors such as Databricks at the forefront of processing, using a high performance Spark engine and processing data for data science, ML, and AI initiatives.

We need to ask, “How do we apply cognitive engines, things like Qlik, to the fore within our own technology and get smarter about the patterns of integration that organizations are deploying so we can further automate?” That’s really the next way for us.

Gardner: You’re not just the president, you’re a client.

Potter: Yeah, that’s a great way to put it.

Gardner: How should people prepare for such use of intelligence?

Potter: If it’s done right -- and we plan on doing it right -- it should be transparent to the users. This is all about automation done right. It should just be intuitive. Going back 15 years when we first brought out replication technology at Attunity, the idea was to automate and abstract away all of the complexity. You could literally drag your source, your target, and make it happen. The technology does the mapping, the routing, and handles all the errors for me. It’s that same elegance. That’s where the intelligence comes in, to make it so intuitive that you are not seeing all the magic that’s happening under the covers.
This is all about automation done right. It should just be intuitive. When we first brought out replication technology at Attunity, the idea was to automate and abstract away all of the complexity. That's now where the intelligence comes in, to make it so intuitive that you are not seeing all the magic under the covers.

We follow that same design principle in our product. As the technologies get more complex, it’s harder for us to do that. Applying ML and AI becomes even more important to us. So that’s really the future for us. You’ll continue to see, as we automate more of these processes, all of what is happening under the covers.

Gardner: Dan, are there any examples of organizations on the bleeding edge? They understand the data integration requirements and core competencies. They see this through the lens of architecture.

Automation insures insights into data 

Potter: Zurich Insurance is one of the early innovators in applying automation to their data warehouse initiatives. Zurich had been moving to a modern data warehouse to better meet the analytics requirements, but they realized they needed a better way to do it than in the past.

Traditional enterprise data warehousing employs a lot of people, building a lot of ETL scripts. It tends to be very brittle. When source systems change you don’t know about it until the scripts break or until the business users complain about holes in their graphs. Zurich turned to Attunity to automate the process of integrating, moving it to real-time, and automatically structuring their data warehouse.

Their capability to respond to business users is a fraction of what it was. They reduced 45-day cycles to two-day cycles for updating and building out new data marts for users. Their agility is off the charts compared to the traditional way of doing it. They can now better meet the needs of the business users through automation.

As organizations move to the cloud to automate processes, a lot of customers are embracing data lakes. It’s easy to put data into a data lake, but it’s really hard to derive value from the data lake and reconstruct the data to make it analytics-ready.

For example, you can take transactions from a mainframe and dump all of those things into a data lake, which is wonderful. But how do I create any analytic insights? How do I ensure all those frequently updated files I’m dumping into the lake can be reconstructed into a queryable dataset? The way people have done it in the past is manually. I have scriptures using Pig and other languages try to reconstruct it. We fully automate that process. For companies using Attunity technology, our big investments in data lakes has had a tremendous impact on demonstrating value.

Gardner: Attunity recently became part of Qlik. Are there any clients that demonstrate the combination of two-plus-two-equals-five effect when it comes to Attunity and the Qlik Catalyst catalog?

DataOps delivers the magic 

Potter: It’s still early days for us. As we look at our installed base -- and there is a lot of overlap between who we sell to -- the BI teams and the data integration teams in many cases are separate and distinct. DataOps brings them together.

In the future, as we take the Qlik Data Catalyst and make that the nexus of where the business side and the IT side come together, the DataOps approach leverages that catalog and extends it with collaboration. That’s where the magic happens.

So business users can more easily find the data. They can send the requirements back to the data engineering team as they need them. By, again, applying AI and ML to the patterns that we are seeing from the analytics side will help better apply that to the data that’s required and automate the delivery and preparation of that data for different business users.

That’s the future, and it’s going to be very interesting. A year from now, after being part of the Qlik family, we’ll bring together the BI and data integration side from our joint customers. We are going to see some really interesting results.

Gardner: As this next, third generation of BI kicks in, what should organizations be doing to get prepared? What should the data architect, who is starting to think about DataOps, do to put them in an advantageous position to exploit this when the market matures?

Potter: First they should be talking to Attunity. We get engaged early and often in many of these organizations. The hardest job in IT right now is [to be an] enterprise architect, because there are so many moving parts. But we have wonderful conversations because at Attunity we’ve been doing this for a long time, we speak the same language, and we bring a lot of knowledge and experience from other organizations to bear. It’s one of the reasons we have deep strategic relationships with many of these enterprise architects and on the IT side of the house.

They should be thinking about what’s the next wave and how to best prepare for that. Foundationally, moving to more real-time streaming integration is an absolute requirement. You can take our word for it. You can go talk to analysts and other peers around the need for real-time data and streaming architectures, and how important that is going to be in the next wave.
Data integration is strategic, it unlocks the value of the data. If you do it right, you're going to set yourself up for long-term success.

So, preparing for that and again thinking about the agility in the automation that’s going to get them the desired results because if they’re not preparing for that now, they are going to be left behind, and if they are left behind the business is left behind, and it is a very competitive world and organizations are competing on data and analytics. So the faster that you can deliver the right data and make it analytic-ready, the faster and better decisions you can make and the more successful you’ll be.

So it really is a do-or-die kind of proposition and that’s why data integration, it’s strategic, it’s unlocking the value of this data, and if you do it right, you’re going to set yourself up for long-term success.