7 Data Predictions for 2017

As data increasingly becomes the means by which businesses compete, companies are restructuring operations to build systems and processes liberating data access, integration and analysis up and down the value chain. Effective data management has become so important that the position of Chief Data Officer is projected to become a standard senior board level role by 2020, with 92 percent of CIOs stating that a CDO is the best person to determine data strategy.

With this in mind as you evaluate your data strategy for 2017, here are seven predictions to contemplate to build a solid framework for data management and optimization.

  1.  Self-Service Data Integration Will Take Off
    Eschewing the IT bottleneck designation and committed to being a strategic partner to the business, IT is transforming its mindset. Rather than be providers of data, IT will enable users to achieve data optimization on a self-service basis. IT will increasingly decentralize app and data integration – via distributed Centers of Excellence based on shared infrastructure, frameworks and best practices – thereby enabling line-of-business heads to gather, integrate and analyze data themselves to discern and quickly act upon insightful trends and patterns of import to their roles and responsibilities. Rather than fish for your data, IT will teach you how to bait the hook. The payoff for IT: satisfying business user demand for fast and easy integrations and accelerated time to value; preserving data integrity, security and governance on a common infrastructure across the enterprise; and freeing up finite IT resources to focus on other strategic initiatives.
  1. Big Data Moves to the Cloud
    As the year takes shape, expect more enterprises to migrate storage and analysis of their big data from traditional on-premise data stores and warehouses to the cloud. For the better part of the last decade, Hadoop’s distributed computing and processing power has made it the standard open source platform for big data infrastructures. But Hadoop is far from perfect. Common user gripes include complexity and instability – not all that surprising given all the software developers regularly contributing their improvements to the platform. Cloud environments are more stable, flexible, elastic and better-suited to handling big data, hence the predicted migration.
  1. Spark Usage Outside of Hadoop Will Surge
    This is the year we will also see more Spark use cases outside of Hadoop environments. While Hadoop limps along, Spark is picking up the pace. Hadoop is still more likely to be used in testing rather than production environments. But users are finding Spark to be more flexible, adaptable and better suited for certain workloads – machine learning and real-time streaming analytics, as examples. Once relegated to Hadoop sidekick, Spark will break free and stand on its own two feet this year. I’m not alone in asking the question: Hadoop needs Spark but does Spark need Hadoop?
  1. A Big Fish Acquires a Hadoop Distro Vendor?
    Hadoop distribution vendors like Cloudera and Hortonworks paved the way with promising technology and game-changing innovation. But this past year saw growing frustration among customers lamenting increased complexity, instability and, ultimately, too many failed projects that never left the labs. As Hadoop distro vendors work through some growing pains (not to mention limited funds), could it be that a bigger, deeper-pocketed established player – say Teradata, Oracle, Microsoft or IBM – might swoop in to buy their sought after technology and marry it with a more mature organization? I’m not counting it out.
  1. AI and ML Get a Bit More Mainstream
    Off the shelf AI (artificial intelligence) and ML (machine learning) platforms are loved for their simplicity, low barrier to entry and low cost. In 2017, off the shelf AI and ML libraries from Microsoft, Google, Amazon and other vendors will be embedded in enterprise solutions, including mobile varieties. Tasks that have until now been manual and time-consuming will become automated and accelerated, extending into the world of data integration.

6. Yes, IoT is Coming, Just Not This Year
Connecting billions and billions of sensor-embedded devices and objects over the internet is inevitable, but don’t yet swallow all the hype. Yes, there is a lot being done to harness IoT for specific aims, but the pace toward the development of a general-purpose IoT platform is closer to a canter than a gallop. IoT solutions are too bespoke and purpose-built to solve broad, commonplace problems – the market still nascent with standards gradually evolving – that a general-purpose, mass-adopted IoT platform to collect, integrate and report on data in real-time will take, well, more time. Like any other transformation movement in the history of enterprise technology, brilliant bits and pieces need to come together as a whole. It’s coming, just not in 2017.

  1. APIs Are Not All They’re Cracked Up to Be
    APIs have long been the glue connecting apps and services, but customers will continue to question their value vs investment in 2017. Few would dispute that APIs are useful in building apps and, in many cases, may be the right choice in this regard. But in situations where the integration of apps and/or data is needed and sought, there are better ways. Case in point is iPaaS (integration platform as a service), which allows you to quickly and easily connect any combination of cloud and on-premise technologies. Expect greater migration this year toward cloud-based enterprise integration platforms – compared to APIs, iPaaS solutions are more agile, better equipped to handle the vagaries of data, more adaptable to changes, easier to maintain and far more productive.

I could go on and on, if for no other reason that predictions are informed “best guesses” about the future. If I’m wrong on two or three of my expectations, my peers will forgive me. In the rapidly changing world of technology, batting .400 is a pretty good statistic.

SnapLogic Sits Down with theCUBE at AWS re:Invent to Talk Self-Service Cloud Analytics

SnapLogic was front-and-center at AWS re:Invent last week in Las Vegas, with our team busier than ever meeting with customers and prospects, showcasing our solutions at the booth, and networking into the evening with event-goers interested in all things Cloud, AWS and SnapLogic.

Ravi Dharnikota, SnapLogic’s Head of Enterprise Architecture and Big Data Practice, took time out to stop by and visit with John Furrier, co-founder of the live video interview show theCUBE.  Ravi was joined by Matt Glickman, VP of Products with our partner Snowflake Computing, for a wide-ranging discussion on the changing customer requirements for effective data integration, SaaS integration, warehousing and analytics in the cloud.  

The roundtable all agreed — organizations need fast and easy access to all data, no matter the source, format or location — and legacy solutions built for a bygone era simply aren’t cutting it.  Enter SnapLogic and Snowflake, each with a modern solution designed from the ground-up to be cloud-first, self-service, fully scalable and capable of handling all data. Customers using these solutions together — like Kraft Group, owners of the New England Patriots and Gillette Stadium — enjoy dramatic acceleration in time-to-value at a fraction of the cost by eliminating manual configuration, coding and tuning while bringing together diverse data and taking full advantage of the flexibility and scalability of the cloud.

To make it even easier for customers, SnapLogic and Snowflake recently announced tighter technology integration and joint go-to-market programs to help organizations harness all data for new insights, smarter decisions and better business outcomes.

To watch the full video interview on theCUBE, click here.

Connect with SnapLogic at AWS re:Invent

This week, the SnapLogic team will be supporting one of our partners, Amazon Web Services, in Las Vegas for the annual AWS re:Invent conference. This gathering of the global AWS community will feature hands-on labs and bootcamps and cover topics such as infrastructure maintenance, and improving developer productivity, network security and application performance.

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SnapLogic CTO James Markarian on DisrupTV

SnapLogic CTO James Markarian recently appeared as a guest on DisrupTV, a weekly live-interview web-series produced by analyst firm Constellation Research and hosted by R “Ray” Wang and Vala Afshar. The trio discussed a variety of enterprise topics including modern data management, data lake strategy considerations and big data analytics.

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SnapLogic Live: Tableau Integration

Data visualization is a hot topic, but is only useful when the most up-to-date data is made available. This SnapLogic Live session features Tableau Integration for customers seeking rapid integration to connect Tableau with other cloud and on-premises applications and data sources.

SL-Live-Tableau

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SnapLogic Roadshow – Is The Data Warehouse Dead?

Short answer to that provocative question: no – it’s just changing. Our data integration road show hit four U.S. cities over the past two weeks. The keynote presentation was delivered by James Markarian, SnapLogic’s CTO. He shared his perspective on modern data management, the role of the data warehouse in a hybrid cloud environment, and important considerations for an enterprise data lake strategy.

James Markarian presents at the SnapLogic roadshow July 2016.
James Markarian presents at the SnapLogic roadshow July 2016.

 

We were also joined by our partner, Amazon Web Services, who highlighted their cloud data management platform, with an emphasis on Amazon Redshift. Together we highlighted some of the joint SnapLogic/AWS success stories around hybrid cloud data integration, including GameStop, Box, CapitalOne, and eero.

The bulk of James’ keynote focused on the changes to the data landscape that are affecting the role and structure of the data warehouse.  He described “data warehousing 1.0” from the 1980’s which introduced a convenient, single place to warehouse all data, but which was expensive and reliant on scripting to integrate sources. He contrasted that with “data warehousing 2.0” of the 1990’s which saw the rise of ETL processes and data marts, but which was still rigid and typically on-premises. Since that era, however, data warehousing has remained generally static. Now, with the dramatic increase in unstructured/polystructured data, plus cloudification of data sources, data warehousing 2.0 has fallen a bit short. Enter the data lake. James cautioned the audience not to think of a data lake as an amorphous, no-rules dumping ground for unstructured data. Instead, he identified multiple “zones” within the data lake, each of which has certain requirements, rules and uses.

Finally, James illustrated how lakeshore data marts and cloud-based data warehouses – connected using SnapLogic – address some of the risk and high labor costs that can be associated with Hadoop-centric data lakes.

Attendees – some of whom were already far along in their data lake adoption journey and some still in a “data warehouse 2.0” environment – certainly left with food for thought. Watch this space for the next time SnapLogic comes to a city near you.

In the mean time, don’t forget to subscribe to our data management podcast series called SnapTalk.

4 iPaaS Use Cases: Accelerating Hybrid Cloud and Big Data Integration

With the recent publication of the Gartner Magic Quadrant for Enterprise Integration Platform as a Service (iPaaS), the awareness of a new approach for connecting data, applications, APIs and the Internet of Things is on the rise. A good example is the recent feature in NetworkWorld on the category: iPaaS: What this cloud technology is and why it’s important, which includes an overview of SnapLogic customer GameStop.

3_As_integrationThe right iPaaS solution should be built to connect the 3 A’s of enterprise integrationAnything (data, apps, APIs, things), Anytime (real-time, batch, streaming), Anywhere (cloud, on prem, hybrid). I’m often asked what are some of the most common iPaaS use cases today.

Here are four:

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