Becoming an Event-Driven Enterprise
By Kevin Petrie
Events drive modern business. A website click, a credit card swipe, the turn of a gear – almost any event now can be digitized to create business value.
Enterprises that analyze events as they happen are more agile and compete more effectively than those that do not. Event-driven enterprises can increase revenue, reduce cost, and control risk.
This huge opportunity spurs action across the organization. IT teams build event streams, often using Kafka or Kafka-like commercial offerings, to reduce full-load, batch processing and improve efficiency. Business teams, meanwhile, seek to analyze those event streams. They want to capitalize on events more rapidly and devise new strategies.
But to become event-driven, enterprises face key architectural tradeoffs. Standard or custom software? Commercial or open source? Suite or best of breed? Automated or scripted? Truly event-driven enterprises will base their choices on a realistic assessment of their objectives, capabilities, and organizational maturity.
First let’s explore what event streaming looks like, in the form of real-world use cases.
- Contextualization: A meteorologist interprets various event streams from satellites, covering temperature, humidity, pressure, wind speed, and precipitation. Each event stream adds a bit of context to his weather prediction.
- Analysis: A gas pipeline manager monitors and automatically correlates streaming sensor signals about pipeline pressure and flow rates. Threshold-based alerts help her keep things running smoothly.
(Son-Hoang-Nguyen/Shutterstock)
- Automated action: A credit card company’s machine learning software automatically identifies an unusual transaction, compares it to historical behavior, and alerts the card owner of potential fraud.
Adoption Trends
Most organizations have adopted event streaming, and of them a significant and growing proportion are piloting or deploying streaming analytics. Let’s dig deeper into what your peers are doing.
Data modernization. Event streaming is not an island unto itself. Organizations frequently budget for and execute event streaming projects as part of larger data modernization initiatives. They adapt legacy architectures to become faster, more efficient, more scalable, and more flexible than was previously possible. Data modernization initiatives might also include the migration of analytics workloads from mainframe systems to data lakes, NoSQL stores, and/or event streaming systems. Whatever the mix of projects, business teams and data teams that adopt event streaming usually need to manage a lot of inter-dependencies.
Shift to cloud. Each year data teams base a higher portion of their event streaming workloads on the cloud. They publish on-premises data to cloud-based event streaming platforms, migrate on-premises streaming workloads to the cloud, and launch new projects entirely on the cloud. As with other data initiatives, cloud platforms can improve economics by converting capital expenditure to operating expense. The cloud also improves operational flexibility, enabling IT teams to easily scale up or wind down infrastructure resources based on changing business requirements.
Machine learning (ML). Many organizations use ML software to automatically learn from and adapt to event streams. Data scientists use ML to design a stream processor before applying it to an event stream. They also use ML to have a stream processor learn from and adapt to event streams on the fly. If managed carefully, ML improves the accuracy of streaming analytics, and helps address entirely new streaming analytics use cases.
IoT edge processing. Organizations in industries such as manufacturing frequently analyze data streams from physical sensors that are attached to “things” in the so-called “Internet of Things” (IoT). By processing events near these sensors, at the “edge,” they can speed up results and address new IoT use cases.
The Journey to the Event Driven Enterprise
The event driven enterprise is a journey, not a destination. The key is to set realistic goals and achieve them – not necessarily to reach for the stars.
Eckerson Group research has found that event streaming initiatives fall into one of two types, opportunistic or transformational, according to their level of customization. Business and data leaders can choose either opportunistic or transformational initiatives based on the scope of their use case, their organizational maturity and their readiness to incur risk.
Opportunistic Initiatives: Start with simpler projects that have lower cost and lower risk.
With an Opportunistic initiative, business and/or data leaders scope a relatively narrow, basic use case. These initiatives, often led by data teams within IT, target growing pains: is the organization struggling to meet demand for an existing analytics service? Perhaps that service is hobbled by legacy batch or SOA technology, and would benefit from a conversion to Kafka-based streaming analytics. Then they set a quantifiable business objective: increasing product revenue 5% through cross-selling, reducing production cost 2% via preventive maintenance, and so forth.
Comparison of Event Streaming Initiatives (Image Source: Eckerson Group)
With an Opportunistic initiative, data teams seek to minimize inhouse development, maintenance and troubleshooting. They use standard features of commercial solutions, or potentially vertical-specific commercial packages, to avoid customization. They take advantage of open source code, but only as part of commercially supported offerings. Opportunistic initiatives use suite offerings and fewer component types, to keep architectures somewhat homogeneous and therefore easier to manage. Both data teams and business users leverage automation to reduce manual scripting wherever possible.
Transformational Initiatives: Incur higher cost and higher risk in return for higher value.
Business teams typically lead transformational initiatives. They set strategic objectives, such as the creation of a new revenue-generating service, or a company-wide effort to reduce product defects by 5%. They commit resources to crafting sustainable competitive advantage. They define more complex streaming analytics use cases, often spanning multiple departments or the full enterprise. Alternatively, they focus on one highly specialized use case that offers strategic advantages.
With Transformational initiatives, organizations customize. They try new types of event sources, stream processors and visualization offerings, favoring best of breed over the suite. They implement commercial offerings, but also favor specialized open source offerings even if they lack commercial support. They hire and train developer teams. They create custom self-service processors and visualization interfaces for business users. They embrace heterogeneous architectures for the sake of specialization.
Set Your Course Based on Your Starting Point
Larger enterprises with legacy technologies often start with opportunistic initiatives, then mature to transformational as they modernize their environments and acquire additional developer skills. If instead they leapfrog to transformational, they will need to consider significant upfront investments in specialized tools and staff.
Younger, “born in the cloud” organizations potentially can start with transformational initiatives more cost-effectively than large enterprises. They often have fewer legacy constraints, are more familiar with open source, and have more developer skills.
Either way, the organizations that first assess their capabilities and risk tolerance are most likely to move in the right direction on their event-driven journey.
About the author: Kevin Petrie is vice president of research at Eckerson Group. His report, “Streaming Analytics: Architecting for Real-Time Insights,” will be published in March.
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How Disney Built a Pipeline for Streaming Analytics
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Driver stranded after connected rental car can’t call home
Enlarge / One of GIG Car Share’s Toyota Priuses on a street in Berkley, California. (credit: Smith Collection/Gado/Getty Images)
Over the weekend, a trip to the Californian boonies by Guardian journalist Kari Paul turned into a cautionary tale about the perils of the connected car and the Internet of Things. Paul had rented a car through a local car-sharing service called GIG Car Share, which offers a fleet of hybrid Toyota Priuses and electric Chevrolet Bolt EVs in the Bay Area and Sacramento, with plans to spend the weekend in a more rural part of the state about three hours north of Oakland. But on Sunday, she was left stranded on an unpaved road when the car’s telematics system lost its cell signal. Without being able to call home, the rented Prius refused to move.
today in sharing economy struggles: our app powered car rental lost cell service on the side of a mountain in rural California and now I live here I guess pic.twitter.com/XoqqMpEwdN
— Kari Paul (@kari_paul) February 17, 2020
Adding insult to injury, Paul’s cellphone was not similarly troubled by the remote location, allowing her to express her frustration, but also to talk to GIG’s customer service to try and get the car back in motion. At first, their plan was to send a tow truck to tow the Prius a few miles closer to civilization, but that would be too easy. Unhelpfully, it appears GIG’s customer service suggested Paul and her companion spend the night sleeping in the car and trying to start the car again the next morning. Instead, after a six-hour wait and not one but two tow trucks—the second of which Paul called herself—plus 20 (!) calls to GIG, the problem was finally solved in the early hours of Monday morning.
six hours, two tow trucks, and 20 calls to customer service later apparently it was a software issue and the car needed to be rebooted before we could use it @internetofshit pic.twitter.com/LZBZQwRJk8
— Kari Paul (@kari_paul) February 17, 2020
In fairness to GIG, on its website the company does explain that users can order an RFID card to use to lock or unlock the car in areas of poor cell service, although that isn’t entirely compatible with the ability to “sign up instantly” and rent a car on the spot. It also appears to be a different approach than that taken by Car2Go (now known as Share Now), the now-defunct car-sharing service from Daimler that filled cities like Seattle and Washington, DC with blue-and-white Smart cars. Those vehicles were geofenced to particular cities and also needed cellular reception to start a trip, but had the option to turn the car off while still keeping the rental running, therefore only requiring the key to unlock them and turn them back on.
Read on Ars Technica | Comments
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How to manage IoT data
In a video interview recorded before the cancellation of Mobile World Congress 2020, Omdia associate director Liz Cruz talks about the challenge of delivering IoT data from the edge to the cloud.
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/slash: A Next-Generation Digital Hotel Experience
Popular Japanese real-estate developer Global Agents announced the much-anticipated February 2020 opening of their new hotel brand, /slash. This new brand seeks to revolutionize the traditional hotel business in Japan and offer what could be called the next generation of digital hotels. The first branch will open a mere five-minute walk from bustling Kawasaki Station […]
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Clovity Brings Its End-to-End CSensorNet IoT Platform to the Qualcomm…
Silicon Valley-based Smart City IoT Platform Provider, Clovity, joins the Qualcomm® Smart Cities Accelerator Program to Provide Cities, Schools and Campuses Across the US with Connected Solutions…
(PRWeb February 18, 2020)
Read the full story at https://www.prweb.com/releases/clovity_brings_its_end_to_end_csensornet_iot_platform_to_the_qualcomm_smart_cities_accelerator_program_to_develop_the_next_generation_campus/prweb16917740.htm
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