From the Blogosphere
Bringing #DevOps to #DataScience | @DevOpsSummit @CAinc #AI #Analytics
DevOps can transform fractured data science into a repeatable business art form
By: Pete Waterhouse
Jul. 17, 2017 02:00 PM
Bringing DevOps to Analytics and Data Science
There's no shortage of material about DevOps on the Interwebs. But as you sift through mountains of information, it appears mostly skewed toward better and faster web and mobile app development. Look for details on how the thinking can be applied to the wicked world of data science or analytics and you'll be hard pressed to find much at all.
This seems odd, because a sustainable business isn't just carved out on the back of web and mobile apps. It's also dependent on intimately understanding customer data and pivoting towards the opportunities revealed - with yes, web and mobile apps.
It's not rocket science; it's data science, and web-scale companies understand it's fundamental for mega efficient business operations. It explains why Netflix has gone from stuffing DVDs through the mailbox to streaming their own shows, and how Amazon continues to redefine retail.
For most businesses the reality is quite different. Even though many consider themselves data-centric, they often struggle to get value from their data science initiatives. In part this might be explained by the ad-hoc nature of efforts, with new models only developed to support one-off projects. It might also be due to the massive amounts of effort needed to maintain and update these types of systems if they ever get deployed into the wild.
Blaming the tech, data or propeller heads is wrong. Today many businesses have mountains of data along with the scientists skilled in doing the math and building the analytical models.
They can even back this up with coding skills and have Big Data engineering teams laying down Kafka, Apache Spark and Hadoop-like technologies to deliver the number crunching grunt.
Yet problems still persist, with many businesses questioning the value of fragile analytics systems that need to be rebuilt every time a model changes. Worse still, any analysis that gets performed might deliver inconsistent results. If this leads to bad decision making, then data science can quickly become the organizational equivalent of snake oil.
The real reason behind these problems isn't tech or teams, it's the isolation of the data science and analytics function from IT development and engineering. Working alone, data science teams can find the right signal in all the data (even codify it), but lacking consistent access to production-like systems and test data means any results only get verified when a product goes live. When this eventually happens, these teams aren't set up to maintain or support them and can encounter stiff resistance from IT operations when systems are shown to be unreliable and fragile.
Faced with IT push-back, business units can of course be tempted to go it alone. This might be fine for a small one-off analytics project, but the cost and risk is prohibitive for the business as a whole if the result is application and code base disparity, overlapping or polluted datasets and complex infrastructure sprawl.
Worse than all the technology is the lost business opportunity. For fast and accurate decision making, businesses need teams, processes and technologies to become capable of supporting the continuous development, delivery, testing and refinement of analytical models that benefit the entire organization.
Of course this is all familiar to proponents of DevOps, where disparate application development teams and IT operations collaborate toward accelerating high-quality software services through a single high-speed process pipeline. Indeed, and as research such as Coleman Parkes' Accelerating Velocity and Customer Value with Agile and DevOps illustrates, advanced adopters use it to consistently outperform their competition.
This same kind of agility will be essential if data science and analytics is not only to become a viable function, but a powerful accelerant of digital value. To achieve this, companies should stop treating analytics as a piecemeal science experiment and start using it as a fully integrated business discipline. To this end, DevOps cross-functional processes and software can help, enabling data scientists, product teams, software development and IT operations to collaborate toward ensuring analytics fully deliver. One immediate beneficiary could be software engineering itself, where integrated customer-experience analytics and predictive modeling can provide valuable business insights into code effectiveness.
Data scientists will also benefit greatly from DevOps. By using automated test frameworks, test data management and service virtualization, analysts can prove or disprove their hypotheses well before production. Just as software developers can use these technologies to catch and remove code defects early, data scientists can use them to catch faulty models before they do any irreparable damage. They can also take advantage of proven agile development practices to release analytics software code and services in shorter cycles.
Benefits accrue when systems move to production. Here the domain knowledge, automation and scalable monitoring IT operations teams already provide can be leveraged. This includes automated release processing to rapidly deploy and split-test different versions of models into production, with monitoring services capturing functional and operability information and feeding it back into the development process.
These types of services are vital, because while a data science team might have many ‘eureka moments,' it's the automation that'll progress them beyond academic meanderings into a powerful and sustainable force for the business.
Ultimately, the success of a business will depend on how quickly and reliably business gains analytical insights from data. This won't happen in the lab, but when data science as a practice becomes industrialized and embraced by the entire organization.
By providing organizations a powerful framework upon which to build, test, deploy and monitor analytics as one continuous stream of value, DevOps can transform fractured data science into a repeatable business art form.
@DevOpsSummit at Cloud Expo taking place June 6-8, 2017, at Javits Center, New York City, and is co-located with the 20th International Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world.
DevOps at Cloud Expo / @ThingsExpo 2017 New York
DevOps at Cloud Expo / @ThingsExpo 2017 Silicon Valley
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The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time to wait for long development cycles that produce software that is obsolete at launch. DevOps may be disruptive, but it is essential.
@DevOpsSummit will expand the DevOps community, enable a wide sharing of knowledge, and educate delegates and technology providers alike. Recent research has shown that DevOps dramatically reduces development time, the amount of enterprise IT professionals put out fires, and support time generally. Time spent on infrastructure development is significantly increased, and DevOps practitioners report more software releases and higher quality. Sponsors of @DevOpsSummit will benefit from unmatched branding, profile building and lead generation opportunities through:
For more information on sponsorship, exhibit, and keynote opportunities, contact Carmen Gonzalez by email at events (at) sys-con.com, or by phone 201 802-3021.
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For more information on sponsorship, exhibit, and keynote opportunities, contact Carmen Gonzalez (@GonzalezCarmen) today by email at events (at) sys-con.com, or by phone 201 802-3021.
All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.
With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo | @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA. Learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.
Track 1. FinTech
Delegates to Cloud Expo / @ThingsExpo will be able to attend 8 simultaneous, information-packed education tracks.
There are over 120 breakout sessions in all, with Keynotes, General Sessions, and Power Panels adding to three days of incredibly rich presentations and content.
Join Cloud Expo / @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA for three days of intense Enterprise Cloud and 'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets.
Financial Technology - or FinTech - Is Now Part of the @CloudExpo Program!
Accordingly, attendees at the upcoming 20th Cloud Expo / @ThingsExpo June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.
Financial enterprises in New York City, London, Singapore, and other world financial capitals are embracing a new generation of smart, automated FinTech that eliminates many cumbersome, slow, and expensive intermediate processes from their businesses.
FinTech brings efficiency as well as the ability to deliver new services and a much improved customer experience throughout the global financial services industry. FinTech is a natural fit with cloud computing, as new services are quickly developed, deployed, and scaled on public, private, and hybrid clouds.
More than US$20 billion in venture capital is being invested in FinTech this year. @CloudExpo is pleased to bring you the latest FinTech developments as an integral part of our program, starting at the 20th International Cloud Expo June 6-8, 2017 in New York City and October 31 - November 2, 2017 in Silicon Valley.
The upcoming 20th International @CloudExpo | @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA announces that its Call For Papers for speaking opportunities is open.
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