Integrate through the big data insights gap

By Bill Creekbaum

Whether you’re an analyst, data scientist, CxO, or just a “plain ol’ business user,” having access to more data represents an opportunity to make better business decisions, identify new and innovative opportunities, respond to hard-to-identify threats … the opportunities abound.

More data – from IoT, machine logs, streaming social media, cloud-native applications, and more – is coming at you with diverse structures and in massive volumes at high velocity. Traditional analytic and integration platforms were never designed to handle these types of workloads.

The above data is often associated with big data and tends to be accessible by a very limited audience with a great deal of technical skill and experience (e.g., data scientists), limiting the business utility of having more data. This creates a big data insights gap and prevents a much broader business user and analyst population from big data benefits. Our industry’s goal should be to help business users and analysts operationalize insights from big data. In fact, Forbes has declared that 2017 is the year that big data goes mainstream.

There are two critical elements needed to close this big data insights gap:

  • A scalable data platform: Handles big data that is compatible with “traditional” analytic platforms
  • An integration platform: Acquires large volumes of high-velocity diverse data without IT dependency

To address the first element, Amazon has released Amazon Redshift Spectrum as part of their growing family of AWS big data services. Optimized for massive data storage (e.g., petabytes and exabytes) that leverages S3 and delivered with the scalable performance of Amazon Redshift, AWS is making the above scenarios possible from an operational, accessibility, and economic perspective:

  • Operational: Amazon Redshift Spectrum allows for interaction with data volumes and diversity not possible with traditional OLAP technology.
  • Accessibility: SQL interface allows business users and analysts to use traditional analytic tools and skills to leverage these extreme data sets.
  • Economic: Amazon Redshift Spectrum shifts the majority of big data costs to S3 service which is far more economical than storing the entire data set in Redshift.

Clearly, Amazon has delivered a platform that can democratize the delivery of extremely large volumes of diverse business data to business users and analysts, allowing them to use the tools they currently employ, such as Tableau, PowerBI, QuickSight, Looker, and other SQL-enabled applications.

However, unless the large volumes of high velocity and diverse data can be captured, loaded to S3, and made available via Redshift Spectrum, none of the above benefits will be realized and the big data insights gap will remain.

The key challenges of acquiring and integrating large volumes of high velocity and diverse data:

  • On-prem in a Cloud-Native World: Many integration platforms were designed long ago to operate on-premises and to load data to an OLAP environment in batches. While some have been updated to operate in the cloud, many will fail with streaming workloads and collapse under the high volume of diverse data required today.
  • Integration is an “IT Task”: Typical integration platforms are intended to be used by IT organizations or systems integrators. Not only does this severely limit who can perform the integration work, it will also likely force the integration into a lengthy project queue, causing a lengthy delay in answering critical business questions.

To address the second element in closing the big data insights gap, business users and analysts themselves must be able to capture the “big data” so that business questions can be answered in a timely manner. If it takes a long and complex IT project to capture the data, the business opportunity may be lost.

To close the big data insights gap for business users and analysts, the integration platform must:

  • Handle large volumes of high velocity and diverse data
  • Focus on integration flow development (not complex code development)
  • Comply with IT standards and infrastructure

With the above approach to integration, the practical benefit is that those asking the business questions and seeking insights from having more data are able to leverage the powerful capabilities of Amazon Redshift Spectrum and will be able to respond business opportunities while it still matters.

Amazon’s Redshift Spectrum and the SnapLogic Enterprise Integration Cloud represent a powerful combination to close the big data insights gap for business users and analysts. In upcoming blog posts, we’ll look at actual use cases and learn how to turn these concepts into reality.

Interested in how SnapLogic empowers cloud warehouse users with up to a 10x improvement in the speed and ease of data integration for Redshift deployments, check out the white paper, “Igniting discovery: How built-for-the-cloud data integration kicks Amazon Redshift into high gear.”

Bill Creekbaum is Senior Director, Product Management at SnapLogic. Follow him on Twitter @wcreekba.

The commoditization of integration

By Dinesh Chandrasekhar

Eight years ago, dozens of integration vendors were offering scores of solutions, all with what seemed to be the same capabilities. Pick any ESB or ETL tool and each seemed to perform the same functions as their competitors. RFPs were no longer a viable way to weed out the inferior vendors as each solution checked all the boxes across the board. Plus, all vendors were ready to lower their prices at the drop of a hat to win your business. It was at this time that the integration market had truly reached a level of commoditization. Consumers could easily pick and choose any solution as there were no true differentiators amongst them.

But, several factors have changed the landscape since then:

  • NoESB – The NoESB architecture had started gaining interest – pushing the idea of the irrelevancy of ESB for many integration scenarios. Yet, an API Gateway was not the right alternative.
  • Cloudification – The cloudification of pretty much all your favorite on-premises enterprise applications began around the same time. Enterprises that were thinking of a digital transformation couldn’t get too far without a definitive cloud strategy in place.
  • Convergence of ESB and ETL – The lines between application integration and data integration were blurring. CIOs and IT managers didn’t want to deal with two different sets of integration tools. With the onset of mobile and IoT, data volumes were exploding daily. As a result, even data warehouses moved to the cloud. To serve such big data needs, the traditional/legacy ESB/ETL tools were incompetent and unfit.
  • Agile Integrations – Finally, the DevOps and Agile movements impacted enterprise integration initiatives as well. They had given rise to new user personas in the enterprise – Citizen Integrators or Citizen Developers. These are the LOB Managers or non-IT personnel that needed quick integrations within their applications to render their data in different views. The reliance on IT to deliver solutions to business was becoming a major hindrance.

All these factors have influenced the iPaaS (Integration Platform as a Service) market. Now, thousands of companies are already leveraging iPaaS solutions to integrate their cloud and on-premises solutions. iPaaS solutions break away from legacy approaches to integration, are cloud-native, intuitive, fast, self-starting, support hybrid architectures, and offer connectors to a wide range of on-premises and on the cloud applications.

Now comes the big question – “Will iPaaS solutions be commoditized, too?” At the moment, the answer is a definite NO and there are multiple reasons why. Beyond scale, latency, tenancy, SLAs, number of connectors etc., one of the key areas that will differentiate iPaaS solutions is the developer experience. The user interface of the solution will determine the adoption rate and the value it brings to the enterprise. So, for a citizen integrator to actually use the system, the interface should be intuitive enough to guide them in building their integration flows quickly, effectively, and most importantly, without the assistance of IT. This alone will make or break the system adoption.

iPaaS vendors are trying to enhance this developer experience with features like drag-and-drop connectors, pipeline snippets, a templates library, a starter kit, mapping enhancements, etc. However, very few vendors are offering AI-driven tooling that enables intelligent ways to predict next steps – based on learnings from hundreds of other users – for your integration flow. AI-assist is truly a great benefit for citizen integrators, who may be non-technical. Even technically savvy developers welcome a significant boost in their productivity. With innovations like this happening, the iPaaS space is quite far away from being commoditized. However, enterprises still need to be wary of cloud-washing iPaaS vendors that offer “1000+” connectors, a thick-client IDE, or an ESB wrapped in a cloud blanket. And, that is a post for a different day!

Dinesh Chandrasekhar is Director of Product Marketing at SnapLogic. Follow him on Twitter @AppInt4All.

Mossberg out. Enterprise technology still in

By Gaurav Dhillon

A few weeks ago, the legendary tech journalist, Walt Mossberg, penned his last column. Although tech journalism today is vastly different than it was in 1991, when his first column appeared in the Wall Street Journal, or even five or 10 years ago, voices like Walt’s still matter. They matter because history matters – despite what I see as today’s widely held, yet unspoken belief that nothing much important existed prior to the invention of the iPhone.

Unpacking that further, history matters because the people who learn from it, and take their cues from it, are those who will drive the future.

Enterprise tech history is still unfolding

I like to think of myself as one of those people, certainly one who believes that all history is meaningful, including tech history. As tech journalism’s eminence grise, Walt not only chronicled the industry’s history, he also helped to define it. He was at the helm of a loose cadre of tech journalists and industry pundits, from Robert X. Cringely to Esther Dyson, who could make or break a company with just a few paragraphs.

Walt is now retiring. So what can we learn from him? The premise of his farewell column in Recode is that tech is disappearing, in a good way.”[Personal] tech was once always in your way. Soon, it will be almost invisible,” he wrote, and further, “The big software revolutions, like cloud computing, search engines, and social networks are also still growing and improving, but have become largely established.”

I’ll disagree with Walt on the second point. The cloud computing revolution, which is changing the way enterprises think and operate, is just beginning. We are at a juncture populated by unimaginably large quantities of data, coupled with an equally unquenchable thirst by enterprises to learn from it. The world has gone mad for artificial intelligence (AI) and analytics, every permutation of which is fueled by one thing: data.

The way we use data will become invisible

In his column, Walt observed that personal tech is now almost invisible. We use and benefit from it in an almost passive way. The way data scientists and business users consume data is anything but. Data is still moved around and manually integrated, on-premises and in the cloud, with processes that haven’t changed much since the 1970s. Think about it – the 1970s! It’s no secret that extract, transfer, and load (ETL) processes remain the bane of data consumers’ existence, largely because many enterprises are still using 25-year-old solutions to manage ETL and integrate data.

Cloud Computing

The good news is, data integration is becoming much easier to do, and is well on its way to becoming invisible. Enterprise integration cloud technology promises to replace slow and cumbersome scripting and manual data movement with fast, open, seamless data pipelines, optimized with AI techniques.

Remember how, as Internet use exploded in the late 1990s, the tech industry was abuzz with companies offering all manner of optimization technologies, like load balancing, data mirroring, and throughput optimization? These days you never hear about these companies anymore; we take high-performance internet service for granted, like the old-fashioned dial tone.

I am confident that we are embarking on a similar era for enterprise data integration, one in which modern, cloud-first technologies will make complex data integration processes increasingly invisible, seamlessly baked into the way data is stored and accessed.

Making history with data integration

I had the pleasure of meeting Walt some years ago at his office, a miniature museum with many of the personal tech industry’s greatest inventions on display. There, his love of tech was apparent and abundant. Apple IIe? Nokia Communicator 9000? Palm Treo and original iPod? Of course. If Walt were to be at his keyboard, in his office, for another couple of years, I’m pretty sure his collection would be joined by a technology with no physical form factor, but of even greater import: the enterprise cloud.

Hats off to you, Walt. And while you may have given your final sign-off, “Mossberg out,” enterprise tech is most definitely still in.

Follow me on Twitter @gdhillon.

Gaurav Dhillon is CEO of SnapLogic. You can follow him on Twitter @gdhillon.

Data management takes center stage at Rutberg 2017 conference

Each year, research-centric investment bank Rutberg & Company gathers top business leaders and technology experts for an intimate, two-day forum where they discuss and debate the technology, ideas, and trends driving global business. The annual Rutberg 2017 conference took place last week in Half Moon Bay, California, and data management was front and center.

SnapLogic CEO Gaurav Dhillon joined Mesosphere CEO Florian Leibert and Segment CEO Peter Reinhardt for a spirited panel discussion on the growing data management opportunities and challenges facing enterprises today. The panel was moderated by Fortune reporter Jonathan Vanian.

A number of important data management and integration trends emerged, including:

  • LOB’s influence grows: Gaurav noted that more and more, “innovation is coming from the LOB,” whether in Sales, Marketing, Finance, HR, or elsewhere in the organization. These LOB leaders are tech-savvy, are responsible for their own P&L’s, and they know speed and agility will determine tomorrow’s winners. So they’re constantly on the hunt for the latest tech solutions that will drive innovation, spur growth, and help them beat the competition.
  • Data fragmentation on the rise: With individual LOBs procuring a flurry of new cloud applications and technologies, the result is often business silos and a disconnected enterprise. “The average enterprise has 10x more SaaS apps than a CIO thinks,” said Gaurav of the increasing SaaS sprawl, which is requiring CIOs to think differently about how they integrate and manage disparate apps and data sources across the enterprise.
  • Self-service integration is here to stay: The bigger a company gets – with more apps, more end-points, more data-types, more fragmentation – there’s never going to be enough humans to manage the required integration in a timely manner, explained Gaurav. Enter new, modern, self-service integration platforms. “The holy grail of integration is self-service and ease-of-use … we have to bring integration out of the dungeon and into the light,” Gaurav continued. And this means getting integration into the hands of the LOB, and making it fast and easy. The days of command-and-control by IT are over: “Trying to put the genie back in the bottle is wrong; instead you need to give the LOBs a self-service capability to wire this up on their own,” noted Gaurav.
  • AI will be a game-changer: Artificial intelligence (AI) and machine learning (ML) are already making apps, platforms, and people smarter. Like with Google auto-complete or shopping on Amazon, we’re already becoming accustomed to assistance from, and recommendations by, machines. “Software without AI will be like Microsoft Word or email without spell-check,” it will be jarring not to have it, said Gaurav. AI is already being applied to complex tasks like app and data integration; it’s not a future state, he said, the start of “self-driving integration is happening today.”
  • The enterprise is a retrofit job: For all the latest advances – new cloud apps, AI and ML technologies, self-service integration platforms – the enterprise remains a “retrofit job,” where the new must work with the old. Large, global enterprises aren’t about to throw out decades of technology investment all at once, particularly if it is working just fine or well-suited to handle certain business processes. So, new cloud technologies will need to work with older on-premise solutions, once again cementing integration platforms as a critical piece of an enterprise technology strategy. “It will be a hybrid world for a long, long time,” concluded Gaurav.

Without question, data has become any organization’s most valuable asset, and those that are able to integrate, manage, and analyze data effectively will be the winners of tomorrow.

Less frosting, more cake: Data integration transforms customer experience

By Nada daVeiga

I’ll start with the frosting. As far as I can tell, it’s been the Year of the Customer for several years now. During this time, every company has gotten the “customer experience” (CX) religion – improve it or die. Thousands of software applications have emerged during what’s now called the Age of the Customer, focused on improving CX by providing the right individual with the right interaction or information, at the right time.

The Age of the Customer has spawned an entirely new software category, marketing technology (martech), chronicled tirelessly by industry analyst Scott Brinker, who goes by @chiefmartec on Twitter. His oft-shared, visual history of the martech product landscape looks like this:

 

 

 

 

 

 

 

 

 

Of the 2017 marketing technology landscape, Brinker notes:

  • There are now 5,381 solutions on the graphic, 39 percent more than last year
  • There are now 4,891 unique companies on the graphic, up 40 percent from last year
  • Only 4.7% of the solutions from 2016 were removed (and another 3.5 percent changed in some fundamental way – their name, their focus, or their ownership)[1]

Where’s the cake?

My point is that there is a lot of frosting here – thousands of applications designed to address the sexiest elements of customer experience. But what’s missing is cake. Data is the cake onto which martech frosting should be added. Integrated enterprise data is the foundation for effective CX strategies to be built on because otherwise, you’re just playing an expensive guessing game.

That’s where enterprise integration comes in. With the expansion of digital channels and new customer initiatives, the variety and volume of customer signals are more diverse than ever. Beyond classical CRM systems around sales and service, understanding the customer lifecycle means bringing together data from, in addition to martech apps, sources including social media, websites, field service, quote management apps, and Internet-enabled things like mobile devices to sensors.

Bake the cake – integrate your enterprise data

More than ever, your company needs to focus on the cake of data, and the enterprise integration required to create it. The good news is, today’s enterprise integration cloud solutions make it easier than ever to build a rich data foundation for comprehensive, effective initiatives in the Age of the Customer.

To learn how to design your integration strategy to enable success with your customer initiatives, download the white paper, “Integration in the age of the customer: The five keys to connecting and elevating customer experience.” In it, you’ll find actionable insights on how to optimize your organization’s data integration strategy for the digital customer, including:

  • Why you need to ensure your organization’s integration strategy is customer-focused
  • How to plan around the entire customer lifecycle
  • Which five integration strategies help speed customer analytics and experience initiatives
  • How to put the odds of customer success in your favor

Download the white paper today!

Nada daVeiga is VP Worldwide Pre-Sales, Customer Success, and Professional Services at SnapLogic. Follow her on Twitter @nrdaveiga.

[1]Marketing Technology Landscape Supergraphic (2017): Martech 5000,” Scott Brinker, May 10, 2017.

 

 

 

Data integration: The key to operationalizing innovation

md_craig-BW-1443725112By Craig Stewart

It’s not just a tongue twister. Operationalizing innovation has proven to be one of the most elusive management objectives of the new millennium. Consider this sound bite from an executive who’d just participated in an innovation conference in 2005:

The real discussion at the meeting was about … how to operationalize innovation. All roads of discussion led back to that place. How do you make your company into a systemic innovator? There is no common denominator out there, no shared understanding on how to do that.[1]

The good news is that, in the 12 years since, cloud computing has exploded, and a common denominator clearly emerged: data. Specifically, putting the power of data – big data, enterprise data, and data from external sources – and analytics into users’ hands. More good news: An entirely new class of big data analysis tools[2] has emerged that allows business users to become “citizen data analysts.”

The bad news: There hasn’t been a fast, easy way to perform the necessary integrations between data sources, in the cloud – an essential first step that is the foundation of citizen data analytics, today’s hottest source of innovation.

Until now.

The SnapLogic Enterprise Integration Cloud is a mature, full-featured Integration Platform-as-a-Service (iPaaS) built in the cloud, for the cloud. Through its visual, automated approach to integration, the SnapLogic Enterprise Integration Cloud uniquely empowers both business and IT users, accelerating analytics initiatives on Amazon Redshift and other cloud data warehouses.

Unlike on-premises ETL or immature cloud tools, SnapLogic combines ease of use, streaming scalability, on-premises and cloud integration, and managed connectors called Snaps. Together, these capabilities present a 10x improvement over legacy ETL solutions like Informatica or other “cloud-washed” solutions originally designed for on-premises use, accelerating integrations from months to days.

By enabling “citizen integrators” to more quickly build, deploy and efficiently manage multiple high-volume, data-intensive integration projects, SnapLogic uniquely delivers:

  • Ease of use for business and IT users through a graphical approach to integration
  • A solution built for scale, offering bulk data movement and streaming data integration
  • Ideal capabilities for hybrid environments, with over 400 Snaps to handle relational, document, unstructured, and legacy data sources
  • Cloud data warehouse-readiness with native support for Amazon Redshift and other popular cloud data warehouses
  • Built-in data governance* by synchronizing data in Redshift at any time interval desired, from real-time to overnight batch.

* Why data governance matters

Analytics performed on top of incorrect data yield incorrect results – a detriment, certainly, in the quest to operationalize innovation. Data governance is a significant topic, and a major concern of IT organizations charged with maintaining the consistency of data routinely accessed by citizen data scientist and citizen integrator populations. Gartner estimates that only 10% of self-service BI initiatives are governed[3] to prevent inconsistencies that adversely affect the business.

Data discovery initiatives using desktop analytics tools risk creating inconsistent silos of data. Cloud data warehouses afford increased governance and data centralization. SnapLogic helps to ensure strong data governance by replicating source tables into Redshift clusters, where the data can be periodically synchronized at any time interval desired, from real-time to overnight batch. In this way, data drift is eliminated, allowing all users who access data, whether in Redshift or other enterprise systems, to be confident in its accuracy.

To find out more about how SnapLogic empowers citizen data scientists, and how a global pharmaceutical company is using SnapLogic to operationalize innovation, get the white paper, “Igniting discovery: How built-for-the-cloud data integration kicks Amazon Redshift into high gear.

Craig Stewart is Vice President, Product Management at SnapLogic.

[1] “Operationalizing Innovation–THE hot topic,” Bruce Nussbaum, Bloomberg, September 28, 2005. https://www.bloomberg.com/news/articles/2005-09-28/operationalizing-innovation-the-hot-topic

[2] “The 18 Best Analytics Tools Every Business Manager Should Know,” Bernard Marr, Forbes, February 4, 2016. https://www.forbes.com/sites/bernardmarr/2016/02/04/the-18-best-analytics-tools-every-business-manager-should-know/#825e6115d397

[3] “Predicts 2017: Analytics Strategy and Technology,” Kurt Schlegel, et. al., Gartner, November 30, 2016. ID: G00316349

Iris – Can you build an integration pipeline for me?

The promise of Artificial Intelligence technology is flourishing. From Amazon shopping recommendations, Facebook image recognition, and personal assistants like Siri, Cortana, and Alexa,  AI is becoming part of our everyday lives, whether we know it or not. These apps use information collected from your past requests to make predictions and deliver results that are tailored to your preferences. 

The importance of AI in today’s world is not lost upon us at SnapLogic. We are always trying to keep up with the latest innovations and technologies, so making our software fast, efficient, and automated for our customers has always been our goal. With the Spring release, SnapLogic launched the SnapLogic Integration Assistant. The SnapLogic Integration Assistant is a recommendation engine that uses Artificial Intelligence and machine learning to predict the next step in building a data pipeline architecture. Iris uses advanced algorithms to collect information from millions of metadata elements and billions of data flows to make predictions and deliver results that are tailored to the customer’s needs.

Currently, customers build pipelines by searching and selecting from over 400 Snaps in the SnapLogic catalog and dragging and dropping them into the canvas. Repeating this step for every single Snap, although easy, can make a pipeline building process somewhat tedious and time-consuming. But with the Integration Assistant, operations like these are simplified to give business users the right next steps in the building process, making pipeline building easy and efficient. Besides efficiency and speed, the “self-driving” software shortens the learning curve for line-of-business users to manage their data flows while freeing technology staff for higher-value software development. See how it works in this video.

In the next few steps,  learn how to enable this feature and start building interactive pipelines yourself.

Right now, we have two ways of building pipelines:

  • Choose a Snap from the SnapLogic catalog
  • Use the Integration Assistant for recommending the right Snaps

How to enable the Integration Assistant feature

By default, the Integration Assistant option is turned off,  allowing you to continue building pipelines by selecting Snaps in the SnapLogic Catalog. However, to utilize the Integration Assistant, just head to the Settings icon and check the Integration Assistant option.

Once the Integration Assistant is enabled, you’ll immediately see the benefits of the self-guided user interface. Drag the first snap onto the canvas and the Integration Assistant instantly kicks in and highlights the next suitable Snap. At the same time, it also opens up another panel that lists suggested Snaps on the right-hand side of the canvas. These AI-driven Snap recommendations are based on the historical metadata from your previous workflows.

Next, you can choose to click the highlighted Snap or pick from the recommended list by dragging the suitable Snap into the canvas. This process continues further until you select a snap with a closed output. At this point, the Integration Assistant will stop suggesting Snaps and the pipeline will be ready for execution.

As you can see, the Integration Assistant improves your pipeline building experience by suggesting Snaps that are the best fit for your organization based on the historical metadata flows.

Interested in learning more? Watch a quick demo on our YouTube channel – SnapLogic Spring 2017: Integration Assistant.”

Namita Prabhu is Senior QA Manager at SnapLogic.