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The modern hybrid workforce is composed of employees working in a variety of settings, from home, on the road, in the office, and just about everywhere in between. The help desk teams who support these dispersed employees are often in mixed working environments themselves and require enhanced contact center software with robust features to adequately serve their customers’ needs. Many companies are now exploring omnichannel solutions that combine contact center platforms, IT support systems, and video communications to address these new working environments.

With 83% of workers preferring a hybrid work model, and 63% of high-growth companies adopting a “productivity anywhere” workforce model, employees need to be as productive as possible no matter their location. IT professionals tasked with supporting these employees must be confident they can get all employees up and running as quickly as possible to avoid long stretches of downtime.

Getting assistance from IT help desks can include submitting tickets via email, online portals, calling them on the phone or using an on-site kiosk. While many enterprises have geographically dispersed IT help desk teams to support a global workforce, it’s now more likely these staffers are working from home or have their own hybrid schedule – which can make it difficult to deliver a more personalized help-desk experience.

Now, with an omnichannel, video-optimized contact center, companies can equip their IT Help Desk teams to deliver the same empathetic and supportive experience internally that is offered to external customers. IT help desk functions are just as important as external customer support, because an employee’s productivity and uptime depend on reliable technology that works. Adding visual engagements to your contact center allows for face-to-face communication that enhances support between an employee and the IT help desk team member.

Having relied heavily on tools such as Zoom for the past few years, many employees are quite comfortable with being on camera, which provides a human element that makes IT support more effective. For example, a video call allows help desk staff to provide a more personalized and caring experience for a frustrated employee who can’t get their work done. Screen and file sharing during the interaction lets IT staff solve problems faster and more efficiently. Video also can be used to quickly verify an employee’s identity, which can add another level of security with employees working from home or remote locations.

Many interactions require expertise beyond the contact center, so it’s important that IT staff have easy, real-time access to back-office experts while helping an employee. A modern platform that combines the contact center and unified communications creates a more seamless experience for help desk staff to interact with fellow employees and reduces costs while improving operational efficiency for IT staff. This also reduces research time, helps in locating answers to solve a given problem, and leads to higher employee satisfaction. What’s more, the department now has one less product to install, train on, administer, and maintain. Additionally, companies save money by eliminating the need to work with multiple vendors for various tasks.

An open platform that can integrate with the critical business applications that IT help desk staff use, such as Zendesk and ServiceNow, is essential to improving efficiency and streamlining the interactions with employees. Through integration, help desk staffers no longer need to switch between their contact center app and service ticket application, and instead, work directly within one application. This enables more automation capabilities and saves time from manually entering in the ticket and employee information.

Learn more about how Zoom Contact Center can assist IT help desk professionals with faster and more personalized support for a hybrid workforce.

Data Center Management

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In advertising, getting the right message to the right audience is easier said than done. For Dana McGraw, vice president of audience modeling and data science at Disney Advertising Sales, the key is data — and how it can be shared with advertisers to enhance its value without compromising privacy and anonymity.

“It’s about how to deliver the right ad to the right consumer at the right time so we improve their ad experience as well as the experience for the brands who buy ads from us,” she says. “Our guiding light is how we use data to improve the experience for our guests.”

With distribution channels multiplying in an industry that is becoming increasingly fragmented, audience data acquisition and use remains McGraw’s biggest challenge. But, as she says, it’s also the most interesting part. Now Disney Advertising Sales has taken a data clean room approach to data governance to help advertisers leverage Disney’s audience graph — a new frontier for data sharing that protects data and privacy while generating synergistic benefits.

McGraw spoke with CIO.com’s Thor Olavsrud at Foundry’s recent Future of Data Summit to discuss Disney Advertising Sales’ approach to data sharing, as well as the importance of staying nimble as consumer habits change and maximizing the potential of data-driven products.

Here are some edited excerpts of that conversation. Watch the full video below for more insights.

On the future of data:

Dana McGraw: The future of data is particularly interesting in the advertising space because the ecosystem changes so quickly, whether through regulation, policies, distribution channels, or platforms. So we’re constantly iterating and evolving. We joke that we always work on long-range plans, but ultimately in the data space, things can get completely uprooted in six months’ time. So it’s really interesting to just think about ‘anonymity with precision.’ In advertising, you certainly want to be precise, but also have thoughts about how to anonymize things to be consistent with what consumers expect of us.

On collecting first-party data:

I think some of the challenges are industry challenges, and opportunities are obvious in the advertising space. They lend themselves to more addressability of inventory, so on a digital platform or a streaming platform, the ability to deliver the right ad at the right time is so much higher in these kinds of environments. It’s a very fragmented business and relatively nascent compared to linear television. So that fragmentation of different distribution channels, what that means to data and advertising, and how it all works is the challenging but really interesting part we get to figure out. We’re lucky we started building an audience graph years ago, and as platforms and content consumption patterns change, we’re able to be nimble. It’s not just adding more frequency across multiple platforms but thinking incrementality, which is huge for brands that advertise because they look for incremental and new audiences. So all of these different channels and our ability to look across them is a big opportunity for us.

On data clean rooms:

The clean room is really the next step as we think about how data is matched. Previously, we would call it data onboarding from a third party or from an advertiser. So if we think about data matching, data collaboration, the clean room is next to fully respect consumer privacy as we innovate. A partner we work with on the clean room side said that a clean room is the “non-movement of data.” I think that captures what it is. We’re able to leverage our proprietary audience graph, and if an advertiser or an agency is bringing data to the table and they want to understand this is a high-value audience, then we want to match it to their audiences to understand where to advertise. We’re able to do that, but the data doesn’t move or exchange hands. So we’re able to maintain that anonymity and maintain the separation of data while still being really precise in how we can find high-value audiences on behalf of advertisers.

On hiring, training, and culture:

Over the past 10 years, we’ve really changed the way we think about how we hire, structure a team, and what our team culture is like. Some of it has to do with the innovation in the data space and some with the proliferation of data. So even those who aren’t particularly adept with data or had a lot of training are using data in their day-to-day business. From an ad sales perspective, our sales team has to be well equipped to speak to data in the marketplace and to understand what we can offer. As we’ve gone through that process, we had to think about EQ [emotional intelligence] skills in addition to quant skills so we haven’t structured our team with all PhDs and quantitative folks. We have folks from very different backgrounds. But that ability to communicate and simplify has really changed the way we think about how we hire. And we pride ourselves on having a diverse team and having a culture of empathy. There’s also been a lot of investment in how we train, both on an individual level and mass trainings. On our team, for instance, we range from someone who was an analyst in the FBI to someone who was a social media manager. So it’s interesting how they interact with one another and train each other. When you’re hiring, there’s a level of aptitude that has to be there, but when you’re hiring really intellectually curious people who have a high EQ and are confident, you can start to leverage all of those resources and get them trained in the things you can teach.

Analytics, Data Science

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At the Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, scientists, mathematicians, and software developers conduct manufacturing research, working together to gain new insights from machine, product, and manufacturing data. Manufacturers partner with the team at WZL to refine solutions before putting them into production in their own factories. 

Recently, WZL has been looking for ways to help manufacturers analyze changes in processes, monitor output and process quality, then adjust in real-time. Processing data at the point of inception, or the edge, would allow them to modify processes as required while managing large data volumes and IT infrastructure at scale.

Connected devices generate huge volumes of data

According to IDC, the amount of digital data worldwide will grow by 23% through 2025, driven in large part by the rising number of connected devices. Juniper Research found that the total number of IoT connections will reach 83 billion by 2024. This represents a projected 130% growth rate from 35 billion connections in 2020.

WZL is no stranger to this rise in data volume. As part of their manufacturing processes, fine blanking incubators generate massive amounts of data that must first be recorded at the sharp end and processed extremely quickly. Their specialized sensors for vibrations, acoustics and other manufacturing conditions can generate more than 1 million data points per second.

Traditionally, WZL’s engineers have processed small batches of this data in the data center. But this method could take days to weeks to gain insights. They wanted a solution that would enable them to implement and use extremely low-latency streaming models to garner insights in real-time without much in-house development.

Data-driven automation at the edge 

WZL implemented a platform which could ingest, store, and analyze their continuously streaming data as it was created. This system gives organizations access to a single solution for all their data (whether streaming or not) that provides out-of-the box functionality and support for high-speed data ingestion with an open-source and auto-scaling streaming storage solution. 

Now, up to 1,000 characteristic values are recorded every 0.4 milliseconds – nearly 80TB of data every 24 hours. This data is immediately stored and pre-analyzed in real-time at the edge on powerful compact servers, enabling further evaluation using artificial intelligence and machine learning. These characteristic values leverage huge amounts of streaming image, X-ray and IoT data to detect and predict abnormalities throughout the metal stamping process. 

The WZL team found that once the system was implemented, it could be scaled without constraint. “No matter how many sensors we use, once we set up the analytics pipeline and the data streams, we don’t have to address any load-balancing issues,” said Philipp Niemietz, Head of Digital Technologies at WZL. 

With conditions like speed and temperature under constant AI supervision, the machinery is now able to automatically adjust itself to prevent any interruptions. By monitoring the machines in this way, WZL have also enhanced their predictive maintenance capabilities. Learn more about how you can leverage Dell Technologies edge solutions.

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