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Artificial intelligence will benefit business in myriad ways. But AI that writes for us should be rejected completely. By Mike Elgan

Machines are getting better at writing. They can finish our sentences. They can reply to our emails. They can write news reports and even novels. But just because they can doesn’t mean they should.

Artificial intelligence (AI) is launching a technology revolution that will transform business over the next decade. The most powerful use for AI may be in the area of decision support, where algorithms feed us streams of knowledge and advice as we go about our work. Gartner says AI augmentation alone will create $2.9 trillion (with a “t”) in business value in 2021.

AI evolution is necessary for enterprise security, too, if only because the cybercriminals will use it to build better malware. We’ll benefit from AI in manufacturing, design, transportation and in countless other areas.

In short, AI will prove to be a huge boost to business.

But as we embark on this partnership with artificial intelligence, it’s important that we safeguard human intelligence. And the biggest threat to human intelligence is software that writes.

The creeping takeover of business writing

The mainstreaming of AI business writing began with Google Smart Reply four years ago. Google Inbox users were offered a few colorless options for a reply to most emails. The feature still exists in Gmail, and with a single click you can respond with “Thanks!” or “I’ll send it to you” or “Let’s do Friday!”

Last year Google added Smart Compose, which finishes the sentences you start. You can choose Google’s words by pressing the tab key.

Using Smart Reply and Smart Compose saves time but makes replies dull. They’re dull because Google makes sure the replies are generic and designed to not annoy or offend anyone (for example, Google’s AI never uses gendered pronouns like “he” or “she”), and also because millions of other Gmail users are using the exact same wording for their replies. We all sound the same in our replies.

Google is not alone. Lightkey makes a Windows application that works like Google’s Smart Compose.

Quillbot is a cloud-based tool that can rephrase what you write (or what you copy and paste from others’ writing). It typically produces awkward prose. Machines have no ear for language.

StoryAI is a tool based on OpenAI that will write a whole story if you write the beginning. I tried it by pasting in the opening paragraph of this column. You can read the column StoryAI wrote and decide who did a better job.

We find ourselves in the tragicomic place where AI writes financial news stories mainly for human consumption, but other AI also reads those stories to provide input for automated trading systems. AI does the writing. AI does the reading. And at some point AI is just going to cut humans out of the trade and keep all the money.

Automated writing will not only get better, it will be increasingly built into the tools we use to write things. The temptation to just let the machines do the writing will only grow. What’s wrong with that?

Here’s what’s wrong with that

The main problem with letting AI write for us is that writing isn’t just writing. Writing is one component of literacy, which includes reading, writing and thinking.

Writing involves revision, which clarifies thinking. We think. We write what we think. Then by reading what we write we realize the errors in our thinking, or at least in the way we have expressed our thinking. We rewrite until our thoughts are clearly and accurately and fully expressed. This practice is at the core of our ability to analyze, create, make good decisions and make progress in our lives and in our work.

Literacy and thinking are connected. This was the point of George Orwell’s Newspeak idea in the novel 1984. The totalitarian government in that book used restrictions on language to make complex thought impossible. Its purpose was “to diminish the range of thought” in order to pacify and enervate the public.

Writing, even writing business emails, forces us to confront our own thoughts in black and white. And this makes us cultivate our ability to think clearly.

It’s also the foundation of our ability to talk with logic and coherence. You’ll notice that good writers tend to speak well.

And writing aids memory. Just letting AI communicate for us, even if we choose from a menu of options, makes it easier to forget what “we” said.

More to the point, writing ability is a use-it-or-lose-it proposition, and AI systems that write for us could make us gradually lose it.

By allowing writing tools to do the writing for us, our literacy fades, and we begin to base our decisions on superficial impressions, rather than critical or analytical thinking.

The critical faculty is already under siege with conventions like emoji. By using cartoons in place of words, we communicate vague impressions rather than specific thoughts. As such, it’s not necessary to think specific thoughts in the first place. Textspeak, SMS abbreviations, autocorrect, emojis —- we’re slouching toward idiocracy. AI that writes our business communication for us is the professional version of all that.

Our relationship with literacy exists on a spectrum.

At one end, we’re fully realized humans with language and literacy to think and communicate better. At the other end we’re less human. We’re “echoborgs” — meat puppets who mindlessly regurgitate the words fed to us by AI.

We should be trying to move toward the good end of that spectrum, not the bad one.

Fear mongering over AI is common nowadays — AI, we’re told, will take our jobs and ultimately have no use for us, other than to keep us as pets. This AI techno panic is based on the knowledge that the machines will just keep getting smarter. We should be more worried that AI will make us all dumber.

The most efficient way for AI to make us dumber is to take the task of writing away from us. Our critical and creative faculties will atrophy. Our minds will become dull. And we’ll all become so boring that the machines may not even want us around as pets.

If you’re concerned about AI making us all redundant, you can do something about it today and every day: Don’t let AI put words in your mouth. Reject automated writing in all its forms. Do your own writing. Think for yourself.

The risk isn’t that machines will get smarter. It’s that humans will get dumber.

The blockchain industry is still something of a wild west, with many cloud service offerings and a large universe of platforms that can vary greatly in their capabilities. So enterprises should beware jumping to conclusions about the technology. By Lucas Mearian

Companies are still cautiously dipping their toes into the blockchain trough, hoping to discover where the distributed ledger technology can create efficiencies in their business processes. But for those who are ready to take the plunge, there are common missteps to avoid.

Based on its research of blockchain implementations, Gartner this week published a guide to the seven most common mistakes companies should avoid. Gartner gauges the maturation of new technology through a “Hype Cycle,” a graphic-based lifecycle that follows five phases: from the Technology Trigger, when proof-of-concept stories and media interest emerges, to the Plateau of Productivity, when mainstream adoption occurs – if the technology is more than niche.

Adrian Leow, senior research director at Gartner, said blockchain is currently sliding down toward the “Trough of Disillusionment.” That’s where interest wanes as pilots and proofs-of-concept fail to deliver forcing tech providers to either work out the kinks or allow a technology to fail and die out.

“The blockchain platforms and technologies market is still nascent and there is no industry consensus on key components such as product concept, feature set and core application requirements,” Leow said in a statement. “We do not expect that there will be a single dominant platform within the next five years.”

In fact, last week, Gartner in a separate study claimed that by 2021, 90% of current enterprise blockchain platform implementations will require replacement to remain competitive, secure and relevant.

“Many CIOs overestimate the capabilities and short-term benefits of blockchain as a technology to help them achieve their business goals, thus creating unrealistic expectations when assessing offerings from blockchain platform vendors and service providers,” Adrian Lee, a senior research director at Gartner, said in that study.

By 2025, the business value added by blockchain is expected to grow to slightly more than $176 billion – then surge past $3.1 trillion by 2030, according to Gartner. “Product managers should prepare for rapid evolution, early obsolescence, a shifting competitive landscape, future consolidation of offerings and the potential failure of early stage technologies/functionality in the blockchain platform market,” Lee said.

Consulting firms working in blockchain were surveyed by Gartner earlier this year and confirmed that CIOs aren’t even using the technology for its most valuable features. Mainly, companies are using blockchain as a database for shared record keeping and asset tracking while ignoring a key attribute: it’s an immutable audit trail.

“That no one is using those innovative features calls into question why they’re using blockchain. Just go use a database,”

Avivah Litan, a Gartner vice president and distinguished analyst, said in an earlier interview.

The perception that the technology is not meeting business expectations has resulted in a palpable disillusionment among IT leaders because of the misalignment of expectations and the real-world requirements of enterprise projects. The problem also stems from a simple fact: blockchain is not yet mature enough for all enterprise use cases.

Here’s a rundown on the mistakes companies are making.

1. Not using blockchain to create immutable data audit trails

IT leaders who’ve taken the plunge into blockchain are mainly deploying it in proofs-of-concept tests, often to address the same problems a conventional database could handle, according to Gartner and other research agencies such as ABI Research.

“They’re not using it as a decentralized ledger able to support immutable data audit trails for exchanging a single version of transactional truth – the core mission at the heart of blockchain. For many, blockchain remains a technology in search of a problem,” according to Gartner.

2. Assuming the technology is mature

A second mistake enterprises make is assuming blockchain technology is ready for production use when the market remains largely composed of fragmented platform offerings that try to differentiate themselves in various ways.

Some blockchain platforms are developed more for confidentiality, others for tokenization or the digital representation of fiat currency or goods; still others are created for universal transactions. Most, Gartner said, are too immature for large-scale production work that comes with the accompanying and requisite systems, security and network management services. However, this will likely change within the next few years.

“CIOs should monitor the evolving capabilities of blockchain platforms and align their blockchain project timeline accordingly,” the report said.

3. Confusing protocol with complete business solution

A third misstep is confusing protocol with business solution, as blockchain is a foundational-level technology that requires applications on top of it to fulfill specific business needs.

While blockchain can and is being used in a variety of scenarios ranging from supply chain management to sharing data across medical information systems, it must also include features such as user interface, business logic and interoperability.

“When it comes to blockchain, there is the implicit assumption that the foundation-level technology is not far removed from a complete application solution. This is not the case,” Leow said. “It helps to view blockchain as a protocol to perform a certain task within a full application. No one would assume a protocol can be the sole base for a whole e-commerce system or a social network.”

4. Misconceptions about scale

A fourth misconception is that blockchain should be considered as purely a database or data storage system. The technology does not yet scale well, as each node in the peer-to-peer network receives a full copy of the distributed ledger each time it’s updated; as it grows, performance slows.

Blockchain, Gartner said, was designed to provide an authoritative, immutable, trusted record of events arising out of a dynamic collection of untrusted parties. That architecture comes at the price of database management capabilities.

In its current form, the technology does not implement the full “create, read, update, delete” model found in conventional database management technology. Instead, it should be seen as a write-once, append-many electronic ledger. “A conventional data management solution might be the better option in some cases,” Leow said.

5. Expecting interoperability too soon

A fifth mistake businesses make is assuming the blockchain universe includes interoperability standards. While some vendors of blockchain platforms talk about interoperability with other blockchains, it is difficult to envision interoperability when most platforms and their underlying protocols are still being designed or developed, Gartner said.

Currently, any vendor conversations about platform interoperability should be seen by CIOs and others as a marketing.

“Never select a blockchain platform with the expectation that it will interoperate with next year’s technology from a different vendor,” Leow said.

6. Assuming smart contracts are fully baked

A sixth mistake some make is assuming that smart contract technology is mature. While smart contracts arguably represent the most powerful aspect of blockchain because they are business automation applications that add dynamic behavior to transactions, problems remain.

Conceptually, smart contracts are software scripts that store procedures associated with specific transaction records. For example, a smart contract could ensure that when cargo reaches a point of entry, a manufacturer awaiting the parts is notified. Unlike a stored procedure in a centralized system, smart contracts are executed by all nodes in the peer-to-peer network, resulting in scalability and manageability challenges that haven’t been fully addressed.

Smart contract technology will undergo significant changes. So CIOs should not plan for full adoption yet, opting instead to run small experiments first. That area of blockchain will continue to mature over the next two or three years, Gartner said.

7. Misunderstanding governance

In a private or permissioned blockchain, governance of the network is typically handed by the owner of the blockchain. So while a supply chain consortium may have dozens of members, the originating company is typically in charge of onboarding, verifying identifiable and financial information, and resolving any disputes that may arise. That’s not the case with public blockchains.

“Governance in public blockchains such as Ethereum and Bitcoin is mostly aimed at technical issues. Human behaviors or motivation are rarely addressed,” Leow said. “CIOs must be aware of the risk that blockchain governance issues might pose for the success of their project. Especially larger organizations should think about joining or forming consortia to help define governance models for the public blockchain.”

CIOs have an opportunity to shrink the distance between technology and line of business as IT takes a leadership role with digital business initiatives. By Beth Stackpole

With IT leading digital business initiatives, the longstanding delta between technology and line of business (LOB) is shrinking as collaboration and cross pollination of strategic expertise becomes the epicentre for enterprise advancement.

The CIO’s rising stature and expanding portfolio of responsibilities are among the key drivers for movement on the ever-elusive goal of IT/LOB alignment. CIOs’ traditional role as technologist with oversight of back-office operations has significantly broadened to a strategist charter, increasing exposure and clout among business counterparts. According to the 2019 State of the CIO, which surveyed 683 IT leaders, 67 percent of respondents said a significant portion of their time is now spent on business strategist activities in pursuit of innovation and advancing go-to-market plans.

“The evolution of technology is exponentially increasing and shaping how consumers think about engaging with different companies, and the technology function is now a strategic leadership role,” notes Angela Yochem, executive vice president and chief digital and technology officer for Novant Health. “CIOs are the digital product and services leads.”

Even with the shift, CIOs are not abdicating functional duties in areas like security management or improving IT operations and systems performance. On the contrary, the 2019 State of the CIO research found CIOs are still wearing the traditional hat in addition to picking up responsibility and oversight of areas far outside of their typical domain.

Respondents to the survey confirmed they are devoting a higher percentage of time to functional activities (85 percent) in addition to transformational efforts (88 percent) like aligning IT initiatives with business goals and cultivating the IT business partnership. At the same time, the strategist portion of the CIO agenda has taken off this year, illustrating an increasing reliance on IT to steer technology-driven business initiatives and digital transformation. In comparison, only 53 percent of respondents to the prior Year State of the CIO spent any significant time on business strategist duties.

As part of the new emphasis, CIOs are channelling energy to efforts they haven’t spent much time on in the past. For example, 35 percent of respondents to the 2019 State of the CIO are actively driving business innovation compared to only 28 percent last year, and 23 percent are developing and refining business strategy, a 2 percent hike over 2018.

Other activities newly heaped on the CIO’s plate include identifying opportunities for competitive differentiation (21 percent), crafting new go-to-market strategies and technologies (19 percent), and studying up on market trends and customer requirements in order to identify new commercial opportunities (16 percent). CIOs are also taking ownership of new domains and responsibilities, among them: Data analytics (64 percent), operations (43 percent), business development (38 percent) and customer service (32 percent).

CIOs new role transcends tradition

As CIOs embrace a portfolio that transcends traditional IT boundaries, there’s no question a tighter partnership with LOB and closer alignment on shared priorities is a foundational element for success. “Everything we do, we do as a collaboration with business,” says Shannon Gath, vice president and head of technology at AMAG Pharmaceuticals. “We co-create the value proposition together and there is alignment around delivering scalable, secure innovative solutions that drive patients’ health.”

Even when LOB spearheads the purchase of their own technology products and services, there remains a desire to work in concert with IT. The rise of cloud services has made it easier for business users to circumvent official IT channels, yet LOB continues to lean on the technology organization for both advisory and oversight assistance. Eighty-five percent of IT heads responding to the 2019 State of the CIO said the department stays involved in some capacity, most (43 percent) maintaining shared project oversight. At 67 percent of responding companies, IT and business are cooperating more frequently on collaborative projects where IT shares responsibility and oversight, another indicator of greater IT/LOB synergy.

While IT is being recognized for stepping up to the strategic advisor role, there remains a gap in perception between CIOs and their LOB counterparts. In this year’s survey, more than half of CIOs interviewed (59 percent) characterized IT as a strategic advisor for technology initiatives (up from 52 percent last year), spending time identifying business opportunities and recommending technology and provider selections. Far fewer LOB respondents characterized IT in that capacity, at only 41 percent; 18 percent still viewed IT as a cautious voice of reason compared to a tiny fraction (4 percent) of CIOs.

To empower the transition, IT organizations are shoring up their ranks with talent versed in business-oriented skills, which helps improve communication between the two functional areas. Technology roles like cloud architects and data scientists are in hot demand, but IT organizations are also seeking out candidates with proficiency in business and soft skills to foster more effective interaction with LOB counterparts and to ensure collaborative efforts don’t get waylaid by miscommunication or competing goals. Forty percent of respondents to the 2019 State of the CIO were on the hunt for employees with change management and strategy building skills while 32 percent were targeting project management acumen and 25 percent business relationship management skills.

Some disconnect remains

While enterprises have made significant strides to align IT and LOB goals, some longstanding perceptions remain a barrier. For example, despite recognition of IT’s contribution to business strategy, there is still a disconnect between how CIOs view their role in digital transformation and how their business counterparts see them. The vast majority of IT leader respondents to the 2019 State of the CIO survey (88 percent) believe CIOs are driving digital transformation efforts more aggressively than any of their business counterparts. Yet that view is only shared by 47 percent of LOB respondents.

There are also differences in both business and technology priorities among the different groups. CIOs are putting emphasis on increasing operational efficiency (40 percent), bolstering cybersecurity protections (40 percent), and improving customer experience (35 percent) as the key areas driving IT investments—a view mostly shared by LOB. However, instead of a focus on cybersecurity protections, which fell lower on LOB’s list, that segment placed a greater priority on growing the business (32 percent).

In terms of the technology agenda, IT leader respondents to the 2019 State of the CIO put the highest priority on data/business analytics (30 percent) followed by cloud computing (27 percent), and enterprise applications and security/risk management (both cited by 26 percent of respondents). LOB, on the other hand, is championing cloud computing (25 percent) followed by security/risk management (23 percent), business process management (19 percent), and data/business analytics (18 percent).

LOB and IT leaders also have slightly diverging views on the challenges CIOs face as they settle into the strategist role. With more on their plate, CIOs continue to struggle with balancing responsibilities that span operational excellence and innovation—an uphill battle for 80 percent of IT leaders responding to the 2019 State of the CIO compared to only 73 percent last year. In comparison, only half of LOB respondents believe CIOs are being stretched too thin by having to juggle simultaneous objectives and agendas.

With digital transformation, CIOs have a real opportunity to take a front-seat leadership role. Yet their success hinges on forging a tight partnership with LOB and creating synergies that put the age-old alignment issue permanently in the rear-view mirror.

Most enterprises now use two or more cloud service providers, and 35% use up to five monitoring tools to keep tabs on hybrid cloud and multi-cloud environments. What’s the best approach to full network visibility? By Stacy Collett

Network monitoring in the enterprise has never been easy. Even before organizations began moving software and infrastructure to the cloud, a typical enterprise used four to 10 tools just to monitor and troubleshoot their own networks, according to analyst and consulting firm Enterprise Management Associates.

The public cloud adds another complex wrinkle to network visibility. Traditional monitoring tools center around the health and performance of individual network elements. Today’s digital business era requires a more holistic view of networks with the ability to glean and correlate data from diverse cloud environments using big data analytics and machine learning.

Today 40% of organizations consider themselves multi-cloud users, having two or more cloud service providers for their organization, according to a survey by Kentik. One-third of organizations have a hybrid cloud environment, with at least one cloud service provider and some type of traditional infrastructure that is company-owned, co-located, or a third-party data center.

“There’s a lot of different types of data that people collect and analyze on the network – everything from device metrics to NetFlow to packets to logs to active synthetic monitoring, and no one vendor does it all very well. Most of them don’t even try to do all of it,” says Shamus McGillicuddy, research director at EMA.

As a result, 35% of multi-cloud users have three to five monitoring tools, including log management tools (48%), application performance management tools (40%), open-source tools (34%), and network performance management tools (25%).

“Network people tell me they just can’t find end-to-end tools. They have a really good view of the data center, a good view of AWS, a good view of Azure, but they can’t pull it all together,” McGillicuddy says.

“The environment is getting far more complex,” says Bob Laliberte, senior analyst at Enterprise Strategy Group. “So it will be critical for them to find very sophisticated tools that will allow that complex environment to become simple to manage.”

Easier said than done. Network professionals often complain that existing device-centric network monitoring does not scale or provide the needed visibility for cloud and digital-business-era applications. Cloud-native monitoring tools, such as Amazon CloudWatch, Azure Monitor or GCP Stackdriver, are less piecemeal and can observe all infrastructure and application layers, but some users find cloud tools often lacking in features and visibility, not to mention that they don’t integrate well with on-premises tools.

No vendor has come up with a “big picture” monitoring solution, and one shouldn’t be expected anytime soon because of the vast differences between the networks you own and those you rent, analysts say. But there are ways to close the gap just a little and achieve better visibility across the networks.

Connecting islands of network insight

In a hybrid cloud environment, “you’re always going to have islands of visibility. The important thing is to look for opportunities to integrate those islands,” McGillicuddy says.

One of the most valuable sources of data for a network monitoring tool is a management system API used to pull data from other platforms, whether it’s from AWS, or an IT service management platform like ServiceNow that’s sending ticket data, or a security monitoring tool.

“If you’re going to try to pull these things together, you need a network monitoring vendor that has a very modern API on the tool that gives you access to things like custom data collection, tool customization, and the ability to build new dashboards that allow you to see the cloud in the way you want to see it,” McGillicuddy says.

Most new vendors will have a good API, he adds. Older ones might be slower to open up APIs to customers because they consider the data they produce with their analytics to be proprietary.

“Infrastructure teams may have an advantage with some of the legacy tools that they currently have that are expanding into cloud-native environments,” Laliberte says. Tool sets like Riverbed, which integrates SNMP polling, flow and packet capture to get an enterprise network view of performance in hybrid cloud environments, and SolarWinds advanced network monitoring for on-premises, hybrid, and cloud, “give the opportunity to tie in both the legacy and cloud” monitoring, he adds.

Many traditional network monitoring tools, however, have been slow to adopt a roadmap for the cloud. Some 74% of network managers surveyed by EMA say they had a network management tool fail to meet their public cloud requirements. Among those network managers, 28% said this failure was due to vendor inaction or lack of a cloud support roadmap.

“I think we’ll get to the place where all the vendors are ‘good’ at incorporating some insight into the cloud with their tool – but I don’t think you’ll ever see a time where there is true parity,” McGillicuddy says.

Cloud service providers make some progress

For cloud native and multi-cloud environments, “the cloud providers are starting to provide a little more consistent access to tools to monitor the networks that cross their perimeters,” says Gregg Siegfried, research director, cloud and IT operations at Gartner.

Microsoft Azure, for example, has added Virtual Network TAP (currently in preview in all Azure regions), which allows you to continuously stream your virtual machine network traffic to a network packet collector or analytics tool. The collector or analytics tool is provided by a network virtual appliance partner.

“Those are much more akin to what network engineers are seeing from their onsite [systems] – so the ability to integrate things like that will certainly assist in those hybrid environments,” Siegfried says.

Despite the progress being made, many cloud users don’t know about or take advantage of some of the monitoring features already in place, according to Kentik. For instance, more than half of AWS users surveyed say they are using the cloud-specific monitoring tools offered by AWS, such as flow logs.

“I generally recommend that clients try the cloud providers’ tools first and cloud-native tools first before spending time and money on third parties,” Siegfried says, “but there is absolutely a delta between the visibility you get from a cloud provider and the visibility you can get with one of these [add-on] products.”

Crossing the lines: multi-cloud monitoring

New tools have emerged that combine monitoring across multi-cloud environments.

The types of features that are important in these tools are their ability to be adaptable, their ability to support collaboration with product development and other infrastructure teams, and their ability to integrate data from multiple sources. It should also be able to factor in things like cost and capacity management that are important in a hybrid network, Siegfried says.

Some of these tools include ThousandEyes, Kentik and APM tools, such as New Relic and Dynatrace, to name a few, Siegfried adds.

Kentik in April announced integrated support for Microsoft Azure. Kentik began using flow data from AWS and Google Cloud Platform late last year. The platform also integrates with other cloud infrastructure data sources, such as host-level instrumentation, virtual network appliances, and container orchestration or service mesh.

Kentik primarily collects NetFlow or real traffic data between two points. It’s telling you what happened on your network and enhances it with other data sources.

Internet monitoring vendor ThousandEyes last year extended its Network Intelligence product to multi-cloud environments. The company has pre-provisioned IaaS vantage points, including 15 AWS, 25 Azure, and 15 GCP regions, giving them visibility into how specific cloud providers are performing in various geographies. It can also do agent-to-agent testing between clouds, giving IT the ability to measure inter-region, hybrid, inter and intra-cloud performance.

While Kentik monitors live traffic, ThousandEyes generates synthetic traffic that emulates a user, and then tells you what could be happening with a hypothetical network transaction.

Interest in active synthetic monitoring solutions has grown over the last three years, McGillicuddy says. “It’s two different ways of looking at things. Synthetic is good because some transactions you can’t see from both sides.”

AIOps and advanced analytics platforms

As network monitoring becomes as much about data acquisition and ingestion as it does about troubleshooting, analysts see the emergence of artificial intelligence for IT Operations (AIOps) and advanced analytics platforms that perform big data analysis and machine learning to correlate insights across tools.

“You see some vendors like CA doing that with their big data stack they’ve built called Jarvis that plugs into different parts of their tool portfolio to correlate insights across them,” McGillicuddy says. “They’ve also tried to make it easier to pull data from third-party vendors to correlate insights. Some specialist vendors can also plug into all your monitoring stuff and correlate everything for you in a way that’s easy to see,” he adds. “We see some indication in our research so far that that’s actually a good approach.”

Looking ahead: Distributed network monitoring

Gartner recommends a data-driven network monitoring framework for boosting network visibility across multiple cloud services, rather than rely on traditional infrastructure tools.

“Use cloud native tools, as opposed to trying to bring your internal tools in the cloud, and wait to see how the market [develops],” says Simon Richard, senior director and analyst at Gartner. “I suspect that the better tools to do multi-cloud monitoring will be coming from the cloud [providers].”

“Whether we call it hybrid, cloud or SD networking, the future of networking is software defined – with distributed rather than centralized intelligence or control,” Siegfried says.  “The same automation philosophy, infrastructure and code techniques that have disrupted other areas of infrastructure management are applying to networking as well. Consider a data-centric network monitor framework … for building the types of network monitoring platforms that will survive or support some of these evolving architectures.”

Developments in cloud services, security, containers and white-box switches can make a positive impact for software-defined data-center networks in the coming year. By Lee Doyle  

To help IT pros attain top performance for their software-defined data-center networks (SDDCN), we have identified 10 crucial technology areas to watch and evaluate during 2019.

SDDCN performance requires advanced network software to provision, manage and secure high-speed traffic flows, and network administrators need automated solutions to monitor and deliver reliable quality of service to critical applications.

The SDDCN combines with compute resources (virtual machines and containers) and storage (disc and flash) to deliver specified performance for private-cloud applications. Via software abstraction, data-center resources can be easily reallocated to address changing application requirements without changing the underlying physical compute, storage or network elements.

Here are 10 factors to keep an eye in during the coming year in order to optimize the performance of SDDNCs.

Multi-cloud migration

Most organizations now live in a multi-cloud world with applications running on a wide variety of IaaS, SaaS and internal data-center platforms. IT organizations need the ability to track application performance independent of the platform and to be able to migrate applications/data to the most appropriate service based on latency, security/compliance and cost concerns. Network software (both in the data center and SD-WAN) will increasingly be used to track data flows and to facilitate the migration of applications/data to the appropriate platform. During 2019, IT will be challenged to enable multi-cloud support by integrating network security, cloud-based security and internal IT security systems. 

Customer experience

The latest IT buzzwords relate to digital transformation and providing excellent customer experience. Business leaders now expect IT to transform their internal data-center resources to deliver the responsiveness and flexibility of public-cloud services. Data-center managers will continue to leverage network software to identify and prioritize key applications, especially those that impact customers. This includes the ability to scale up or scale down capacity as web traffic spikes and ebbs and the tools to fix performance problems rapidly. For 2019, network professionals will increasingly be judged by their ability to deliver expected customer experience. 

New tools for DevOps, containers, and server less environments

To enable digital transformation, IT is employing new ways of developing applications rapidly. These DevOps principals leverage microservices running on containers and soon in server less environments. The data-center network must adapt to the new requirements that containers place on the network, including scalability, predictable performance, multi-tenancy and security. The old manual methods of data-center network provisioning with their weeks of wait time have no place in the DevOps world. During 2019, intent-based networking principles will increasingly be deployed to speed automation.White-box switching and related NOSes

Growth in data-center network traffic both north-south and east-west continue to drive deployment of high-speed Ethernet. Many organizations are considering cost-effective white-box Ethernet switches to grow their data-center network capacity. Customers can choose from a variety of suppliers of network operating systems to power white-box switches. 2019 will see broader adoption of white box switches – beyond the hyperscale cloud entities.

 White-box switching and related NOSes

Growth in data-center network traffic both north-south and east-west continue to drive deployment of high-speed Ethernet. Many organizations are considering cost-effective white-box Ethernet switches to grow their data-center network capacity. Customers can choose from a variety of suppliers of network operating systems to power white-box switches. 2019 will see broader adoption of white box switches – beyond the hyperscale cloud entities.
Intent-based networking improvements

Intent-based networking (IBN) abstracts network complexity and improves automation by reducing or eliminating the need for human intervention by using natural-language requests for network resources. Current SDDCN technologies can automatically assign IP addresses, configure vLANs and provide insights to security anomalies and applications slowdowns. In 2019, IBN will and gain the ability to leverage machine-learning techniques to further automate data-center network orchestration and management.

Architecture options

IT professionals with plans to implement a private cloud or SDDC will continue to be confronted with a plethora of architectural options. No clear blueprints exist to deploy a fully automated SDDCN, and the design options are much more like building a custom home than selecting from three feature levels for a new car. Vendors offer architectural options, but IT professional can also choose to build an SDDCN with white-box switches and independent NOS suppliers. In 2019, system integrators and large IT suppliers will become more prescriptive in their SDDCN offerings.

Better integrated network security

As we reach news saturation about the breach of the week, to say nothing about the thousands of breaches that we never hear about, network security remains the key unsolved problem in the data center. Extensive use of the internet, IaaS and SaaS means companies must defend in depth and can no long rely on a secure perimeter. IT organizations will continue to use Firewall/UTM and ADC appliances in the data center, but they must also enable internal data-center security due to the risk that a breach of one VM or container will allow the attacker to access other data-center applications via “trusted” east-west traffic flows.

Network software provides for segmentation/isolation of critical data-center assets and the ability to monitor data traffic to identify attacks and to alter the network to remediate specific threats. In 2019, network software suppliers must build a broad-based security ecosystem that integrates SDDCN elements with the wide range of network and IT security systems already deployed in most organizations.

SD-WAN to SDDCN frameworks

To ensure a high-level user experience, many SD-WAN providers are enhancing their public-cloud application-acceleration capabilities. They are also expanding the depth of their connections with private-cloud networks and security features. During 2019, these SD-WAN to SDDCN integrations will expand with improved management/orchestration, performance monitoring and security capabilities.

 Virtual application delivery controllers

Traditionally, the application-delivery controller (ADC) appliance sits between the firewall and the application servers. It can see, route and analyze much of the inbound and outbound traffic. Software networking and virtualization has enabled the disaggregation of ADC feature with increasing use of microservice-based features, such as a service offerings and flexible licensing. During 2019, virtual ADCs will be distributed throughout the data center to enable container-to-container communications, enhance security and provide quality of service by application and or by customer.

 Storage integration with SDDCN

The increases in compute capacity and the demands of new applications like big data, video, and IoT continue to drive massive increases in storage capacity. Decreases in prices have enabled flash to become the leading technology for new storage capacity. SDDCN suppliers have focused on the data side of the network and have typically minimized their storage-handling capabilities.

For 2019, SDDCN suppliers will develop new features to enable rapid provisioning of storage capacity, scalable performance and ability to support any type of storage with any type of compute and application. IT organizations will increasingly converge data and storage traffic on one Ethernet network because the SDDCN supports a virtual path for storage traffic with low-latency requirements.