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Traditionally, content delivery networks (CDNs) were used to cache files close to consumers, enabling media publishers to stream video and gaming software to customers as quickly as possible, and allowing high-stakes web application providers to deliver web pages equally fast.

Eventually, application and content owners found these networks had use beyond caching that enabled digital experiences to be better, safer, and more personalized and profitable. The market responded with edge platforms, an evolution of CDNs that can handle the compute and data workloads that were historically the domain of data centers and clouds. Edge platforms are now a fundamental part of every consumer-facing business’s digital stack.

How does this evolution from traditional CDN to an Edge platform help businesses and improve consumer experiences?

Benefit 1: holistic security protection

With the proliferation of on-premise, cloud, and SaaS systems, technology leaders are struggling to protect an increasingly diverse and expanding attack surface area from bad actors. Further, leaders often tend to overcompensate by implementing chains of security solutions, creating single points of failure, and adding latency and performance bottlenecks between security layers. Given that the average web page generates 65-70 requests on mobile and desktop, and latency accumulates with every page, performance, in turn, is negatively impacted.

The distributed nature of modern applications across multiple clouds, on-prem data centers, and SaaS tools means that the traditional notion of a security perimeter is no longer applicable. To ensure holistic protection, organizations must adopt an edge-enabled solution that can be present across all these environments, otherwise, routing all traffic through a central office using VPNs can cause undesirable latencies and network costs.

By moving security to the edge – in front of cloud providers, application, and storage servers – your infrastructure and data is protected wherever it lives.

Benefit 2: increased consumer experience with speed and AI-driven personalization

Generally, the closer you can move compute to the user, the faster your application will be. Edge computing enables companies to push components of web applications down to the edge of the network and even into the consumer’s device, speeding up page loads on web and mobile devices.

To do so requires intelligent predictive prefetching, which anticipates what actions, data or content the consumer will need next, and pushes the info to their browser or mobile device in anticipation of the request. This effectively makes web pages and mobile screens load instantly.

In addition to speed, the edge can be the ideal layer to implement personalization informed by first-party data or AI algorithms. Organizations can use the knowledge of their end users’ preferences, keywords searches, and geolocation to display products that are relevant to the user in real time.

Benefit 3: reduced costs

Nearly 75% of executives consider edge computing a strategic investment, in part due to the lower cost of bandwidth. Edge computing allows local data centers to crunch their own data instead of sending it to a central data center or the cloud. By processing locally, the amount of transmitted data across the network is greatly reduced, resulting in less bandwidth and connectivity.

Remote servers or data centers act independently, regardless of outages or connectivity to the central data center. Removing dependency on a central network allows digital businesses to be more available and agile in constantly changing markets.

Edgio

To realize true cost savings from the edge requires a balanced approach. Sometimes, it’s more efficient to compute a workload in the cloud and cache it to multiple edge nodes, instead of having all nodes execute redundant work. That’s why it’s important to use a holistic application platform, such that of Edgio’s, that allows you intelligently leverage the capabilities of both the cloud and edge for peak performance and cost optimization.

Conclusion

Edge platforms are taking the market share of traditional CDNs and cloud providers for their wider range of use cases and advanced capabilities. Businesses are improving their security posture, performance, and consumer experiences, while reducing overall costs from edge compute and AI capabilities, real-time responses, and intelligent migration of workloads.

Edgio is a global edge network with an integrated developer-friendly platform designed to offer the highest levels of performance and protection for digital content, boosting overall revenue and business value.

Digital Transformation

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After years of compounded digital transformation, the downsides of the cloud are starting to reveal themselves. As cloud investments increase, benefits remain elusive without also investing in optimization efforts targeted at reducing cloud waste and lowering costs, that’s according to a new study published by CIO.com.

Research reveals that while most companies are investing more in the cloud, 80% of decision makers report cost savings as the biggest issue with existing cloud deployments. In fact, the vast majority (55%) of those respondents say costs are “extremely” challenging.

This key takeaway is being labeled the “cloud cost quandary,” the predicament companies face when they need to invest more in the cloud yet struggle to recognize value on the investments they have already made. But why is capitalizing on the cloud difficult? The research also provides answers.

Data explains why cloud cost savings are problematic

The research report unpacks root causes behind the issues of cost and achieving cost savings in the cloud. The bottom line: it’s hard to follow where the money goes, seeing how, when, and where resources are used and then allocating costs across the organization.

It’s hard to follow the cloud money trail

70% say it’s challenging to account for cloud spending and usage66% of the C-suite cite tracing spend and chargebacks of particular concern

Here’s where the cloud can spur spending surprises that are difficult to chase down.

Cloud infrastructure services generally charge a fee every time data is accessed, as well as when data limits are surpassed during storage and backup. You are also charged by the second for however long a service instance runs. Thus, understanding cloud usage at a very granular level has become a key focal point in putting boundaries around spending.

Tagging capabilities enable granular tracking of service usage, but it’s not always easy to maintain accuracy, spot IaaS instances that are little used or mis-tagged or get a clear view into usage spikes and the reasons for them. Managing tags is essential yet time-consuming when performed manually. Default dashboards from cloud providers don’t offer the speed and data-crunching benefits of advanced analytics nor the cost-cutting insights of cloud expense management platforms.

Deciphering cloud costs on the backend is equally daunting, as invoices can be highly complex in part because of the number of line items for each fee or instance. However, with more investments in cloud, IT finance managers want to ensure resources are used effectively to keep costs to a minimum. Chargebacks and allocating costs are also critical moves for leaders trying to hold departments and lines of business fiscally responsible for digital innovation and the cloud services they use.  

And it’s not just cloud infrastructure fogging the view. The study shows a large majority (70%) of respondents reported Shadow IT as their top challenge with cloud applications. That’s understood after fast-moving changes from the past three years. Most companies today find they have an eye-opening number of unsanctioned tools, multiple redundant SaaS subscriptions, and a significant amount of unused application licenses that need to be identified and reassigned to new owners.

The bottom line: Making time to oversee the cloud—gaining visibility into expansive datasets for deeper investigation and managing accounting details—is an added burden on IT and finance teams.

Cloud waste offers evidence of the difficulties of cloud management.

Waste proves companies are mismanaging cloud resources

When it’s challenging to account for cloud usage and spending, it’s that much harder to optimize cloud resources and reap the benefits of cost savings. Another key takeaway from the study is how many digital transformation dollars are getting washed away in the form of cloud waste. The study shows 29% of cloud resources are wasted with 12% of cloud licenses going entirely unused. 

Those numbers should have any cloud innovator sitting up straight in their chair—not because they should fear exposing how much of the cloud gets squandered, but because they can now see exactly how much a cloud cost optimization program is worth—29% in savings. This is the type of program that could generate meaningful resource reallocations, ROI improvements, departmental recognition, or even personal promotion.

In diving deeper, the research shows the larger the company the larger the problem. This is particularly the case when it comes to cost savings. Roughly 68% of companies with more than $5 billion in revenue faced significant difficulty achieving cost savings with cloud, compared to 36% of smaller companies.

Capitalizing on the cloud: 3 capabilities simplify governance

Few organizations have yet to update their operational processes to handle the cloud effectively. But in the wake of compressed innovation, governance is now necessary in order to fully capitalize on the cloud. Being a good steward of cloud investments requires three cornerstone competencies, which the study respondents prioritized as the top critical capabilities for cloud management:

Cloud optimization and right-sizingMeasuring and tracking total cloud spendMeasuring cloud utilization

These are the most significant factors in maximizing ROI on cloud innovation.

To learn more about cloud expense management services, visit us here.  

Multi Cloud

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By Chet Kapoor, Chairman & CEO,DataStax

Mistakes: we all make them. Whether it’s screwing up a demo in front of the entire leadership team or hiring the wrong person for a role, I can’t even count how many times I’ve made mistakes throughout my career. These moments are never easy, but they are always learning experiences–and the best leaders are always learning.

Below, I share a few common mistakes leaders and organizations are making today and how they can overcome them to drive lasting success.

Mistake 1: undisciplined growth

Leaders are facing times of uncertainty, magnified recently with the collapse of Silicon Valley Bank and ongoing market turmoil. As companies continue to navigate the current economic headwinds, one thing is clear: growing responsibly is critical both in the good times and the hard times.

Kelly Battles is a seasoned CFO and board member at several Silicon Valley organizations. I recently discussed the biggest mistakes companies make with regard to execution.

“The biggest mistakes that I’ve seen are whiplash overreactions to the fear and greed cycle,” Kelly said.

When times are good, it can be easy to get greedy. Maybe your burn increases, you loosen your controls, or you don’t take discipline as seriously. As a result, you have an organization that’s floating with the tides. The problem is when a tough period hits, now you are unprepared. As we’ve seen over the last several months, irresponsible growth can lead to hiring freezes and limiting investments (at best) or mass layoffs and big risks (at worst).

In Kelly’s words, “The best companies are disciplined during the greedy times and lead with strength during the toughest.”

Growing responsibly requires thinking about where you’re investing resources, the way you’re prioritizing projects, and how you’re tracking progress. It’s a constant balance of patience and impatience–but if you stay disciplined, it will pay off.

Mistake 2: failing to implement AI across the business

The age of AI is here. Leading enterprises like Netflix and Uber have been leveraging AI to drive business outcomes for years, but today these capabilities are within reach for companies of all sizes. It’s not just about building AI products–it’s also about using AI to improve performance and efficiency across the business.

I had the chance to catch up with Hussein Mehanna, who heads up AI and ML at the self-driving car company Cruise. Hussein shared how his team is using AI to improve their operational efficiency. They have a paradigm called the “continuous learning machine,” where engineers use AI to automate their repetitive work tasks and build predictive models to help with productivity. This way, they can focus on high-value tasks and the more creative aspects of their work.

Winning companies of today and of the future will be AI-first:

It starts with data. Focus on finding quality data and building proprietary signals (i.e. unique insights that come from your data)Once you have quality signals, start searching for monetization methods. Usually, you can use the data to improve your apps and productsHave a strong AI execution framework that includes people, process and, technology–and use it across the business

Mistake #3: Treating DEI&B like a “nice-to-have”

Many companies view diversity, equality, inclusion, and belonging (DEI&B) through the numbers, and there’s a common misconception that hiring diverse individuals equates to a more diverse and inclusive culture. But that’s only a small piece of the big picture.

To build a truly inclusive and equitable workplace, leaders need to focus on driving real behavioral change. This starts with each individual looking in the mirror and asking themselves every day: How did I show up? Did I listen actively? Can I identify any implicit or unconscious biases at play? If I made a mistake, did I take accountability, and how can I do better next time?

Alana Mayo is president at Orion Pictures, a division of MGM that’s dedicated to underrepresented voices and authentic storytelling in film. I often refer back to our discussion on the Inspired Execution podcast, where Alana shared tips for holding ourselves accountable and demonstrating inclusivity.

“In meetings, don’t always speak first. Take note of when you are talking more than other people in the room. And the biggest thing to remember is that both active listening and speaking up require vulnerability,” she said. “It ultimately goes back to creating a culture where there’s really good communication and where everybody feels comfortable enough to be vulnerable.”

Today, it’s not enough to treat your DEI efforts as a box on a checklist. Real progress and impact start with each leader, each individual at your company. And in the current economic climate, now is actually the perfect time to double down on investing in your people–it will pay off.

For more insights and stories from world-class leaders, check out Inspired Execution here or wherever you listen to your podcasts. Season 5 is coming soon!

About Chet Kapoor:

Chet is Chairman and CEO of DataStax. He is a proven leader and innovator in the tech industry with more than 20 years in leadership at innovative software and cloud companies, including Google, IBM, BEA Systems, WebMethods, and NeXT. As Chairman and CEO of Apigee, he led company-wide initiatives to build Apigee into a leading technology provider for digital business. Google (Apigee) is the cross-cloud API management platform that operates in a multi- and hybrid-cloud world. Chet successfully took Apigee public before the company was acquired by Google in 2016. Chet earned his B.S. in engineering from Arizona State University.

IT Leadership

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By Bryan Kirschner, Vice President, Strategy at DataStax

Artificial intelligence is something developers are excited to work on. So much so that many enterprises give their AI systems names to better tout their innovations and aspirations to the world (Halo at Priceline or  Michelangelo at Uber, for example).

But, as the saying goes, when it comes to the typical consumer, people don’t want to buy a quarter-inch drill; they want a quarter-inch hole. What users really care about is how well your app, website, or customer service process satisfies their goals. They generally don’t care a whole lot about the “how” under the hood.

A new survey, conducted by Wakefield Research on behalf of DataStax, hammers this home. Based on responses from 1,000 U.S. adults, Wakefield found that a majority don’t realize how often they interact with AI in products, services, and experiences. 

Take fraud detection. Nearly two-thirds (65 percent) of respondents don’t identify fraud alerts from banks or credit card companies as being powered by AI. This is, in fact, a measure of how AI consistently gets its job done so well for the consumer that it fades into the background.

How does AI make your customers feel about your organization?

Imagine a world where customers are surprised that it was even possible to detect potential fraud in real time. Imagine them feeling relief every time a legitimate credit card swipe was approved or resigned to the fact that–yet again this month–there were unauthorized charges on their bill.

AI would be front page news for all the wrong reasons, with stories about the ins and outs of balky systems that are sources of constant frustration–as well as why financial institutions were subjecting customers to such constant aggravation.

This reveals one key question you can ask to accelerate your AI strategy: how will it make your customers feel? And let’s be clear: if you are doing it right, the feelings won’t be about your AI, it will be about your organization.

In the case of fraud detection, it’s a latent confidence you’d have to poke at to bring to the surface (although it would be an obvious and utter disaster in the case of a breach). But consider these sentiments:

You knew just what I was in the mood to watch.

You really saved my bacon with the alternative product recommendation I could get the same day.

You recommended the perfect gift.

Most consumers (64%) don’t give AI the credit  for a song or movie recommendation from a streaming service. But they love the results. Eighty-seven percent find relevant recommendations “highly valuable.”

Wakefield Research/DataStax

And 60% of shoppers take advantage of relevant recommendations they come across while browsing or shopping online, including 54% of millennials, who call these “a great benefit.” Nearly 3 in 4 (72%) trust a company more when they receive relevant recommendations (including 83% of millennials). And nearly 1 in 5 (21 percent) are “extremely likely to return” after receiving good recommendations (46% of millennials say as much).

Make AI a superpower for your organization

Real-time AI has crossed two critical thresholds. First, best-of-breed tools for delivering top-quality experiences are open source and available as-a-service, on demand, to anyone. But just as important: it has become a powerful determinant of satisfaction and loyalty among consumers.

Thus, turning the tools into a competitive edge requires a vision for how you align what technology can do with the qualitative and emotional brand relationship only you can build with your customers.

Is it “you expand my horizons” or “you never let me down”? Is it “I always get more for less” or “I’m never, ever late”? (Lyft smartly flips the script on this apparent dichotomy by offering a continuum of options from “Wait and Save” to “Priority Pickup.”)

Brands are built by consistently delivering on a promise to customers. Modern AI has the power to pound away at this goal by doing so millions (or billions) of times, as often as every second of every day–while getting better throughout the process. Connecting those dots is how you can make it a superpower for your organization–even though consumers might not give AI the credit for the results.

Learn more about real-time AI here.


About Bryan Kirschner:

Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.

Artificial Intelligence, IT Leadership

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In the words of J.R.R. Tolkien, “shortcuts make long delays.” I get it, we live in an age of instant gratification, with Doordash and Grubhub meals on-demand, fast-paced social media and same-day Amazon Prime deliveries. But I’ve learned that in some cases, shortcuts are just not possible.

Such is the case with comprehensive AI implementations; you cannot shortcut success. Operationalizing AI at scale mandates that your full suite of data–structured, unstructured and semi-structured get organized and architected in a way that makes it useable, readily accessible and secure. Fortunately, the journey to AI is one that is more than worth the time and effort.

AI Potential: Powering Our World and Your Business

That’s because AI promises to be one of the most transformational technologies of our time. Already, we see its impact across industries and applications. If you’ve experienced any of these, then you’re seeing AI in action:

Automated assistants such as Amazon Alexa, Microsoft Cortana and Google Assistant.COVID vaccines and/or personalized medicine used to treat an illness or disease.Smart cars that alert drivers like you, help you park and ping you when it’s time for maintenance.Shopping preferences that are tailored to your specific tastes and proactively sent to you.

Despite these AI-powered examples, businesses have only just begun to embrace AI, with an estimated 12% fully using AI technology.1 But this is changing rapidly. And that’s because AI holds massive potential. In one Forrester study and financial analysis, it was found that AI-enabled organizations can gain an ROI of 183% over three years. 2

That’s why AI is a key determinant of your future success. Businesses that lead in fully deploying AI will be able to optimize customer experiences and efficiencies that help maximize customer retention and customer acquisition and gain a distinct advantage over the competition. The growing divide between AI haves and have-nots is underway and at a certain point, that chasm will not be crossable.

For example, today airports can use AI to keep passengers and employees safer. AI working on top of a data lakehouse, can help to quickly correlate passenger and security data, enabling real-time threat analysis and advanced threat detection.

In order to move AI forward, we need to first build and fortify the foundational layer: data architecture. This architecture is important because, to reap the full benefits of AI, it must be built to scale across an enterprise versus individual AI applications. 

Constructing the right data architecture cannot be bypassed. That’s because several impeding factors are currently in play that must be resolved. All organizations need an optimized, future-proofed data architecture to move AI forward.

Complexity slows innovation

Data growth is skyrocketing. One estimate3 states that by 2024, 149 zettabytes will be created every day: that’s 1.7 MB every second. A zettabyte has 21 zeroes. What does that mean? According to the World Economic Forum4, “At the beginning of 2020, the number of bytes in the digital universe was 40 times bigger than the number of stars in the observable universe.” 

Dell

Data’s size alone creates inherent complexity. Layered on top of that are the different types of data stored in various siloes and locations throughout an organization. It all adds up to a “perfect storm” of complexity.

A complex data landscape prevents data scientists and data engineers from easily linking the right data together at the right time. Additionally, multiple systems of record create a confusing environment when those sources do not report the same answers.

Extracting value from data

Highly skilled data scientists, analysts and other users grapple with gaining ready access to data. This has become a bottleneck, hindering richer and real-time insights. For AI success, data scientists, analysts and other users need fast, concurrent access to data from all areas of the business.

Securing data as it grows

Securing mission-critical infrastructure, across all data in an enterprise, is a default task for every organization.  However, as data grows within an enterprise, more desire for access and use of that data produces an increasing amount of vulnerable security end points.   

Catalyzing AI at Scale with Data Lakehouse

The good news is that data architectures are evolving to solve these challenges and fully enable AI deployments at scale. Let’s look at the data architecture journey to understand why and how data lakehouses help to solve complexity, value and security.

Traditionally, data warehouses have stored curated, structured data to support analytics and business intelligence, with fast, easy access to data. Data warehouses, however, were not designed to support the demands of AI or semi-structured and unstructured data sources. Data lakes emerged to help solve complex data organizational challenges and store data in its natural format. Used in tandem with data warehouses, data lakes, while helpful, simultaneously create more data silos and increase cost.5

Today, the ideal solution is a data lakehouse, which combines the benefits of data warehouses and data lakes. A data lakehouse handles all types of data via a single repository, eliminating the need for separate systems. This unification of access through the lakehouse removes multiple areas of ingress/egress and simplifies security and management achieving both value extraction and security. Data lakehouses support AI and real-time data applications with streamlined, fast and effective access to data.

The benefits of a data lakehouse address complexity, value and security:

Create more value quickly and efficiently from all data sourcesSimplify the data landscape via carefully engineered design featuresSecure data and ensure data availability at the right time for the right requirements

For example, pharmacies can use a data lakehouse to help patients. By quickly matching drug availability with patient demand, pharmacies can ensure the right medication is at the right pharmacy for the correct patient.

Moving AI Forward

AI deployments at scale will change the trajectory of success around the world and across industries, company types and sizes. But first things first mandate that the right data architecture be put in place to fully enable AI. While data lake solutions help accelerate this process, the right architecture cannot be bypassed. As J.R.R. Tolkien intimated, anything worth achieving takes time.

Want to learn more?  Read this ESG paper.

*************

[1] https://www.zdnet.com/article/what-is-ai-maturity-and-why-does-it-matter/ 

[2] https://www.delltechnologies.com/asset/en-us/products/ready-solutions/industry-market/forrester-tei-dell-ai-solutions.pdf

[3] Finances Online, 53 Important Statistics About How Much Data Is Created Every Day, accessed April 2022

[4] https://www3.weforum.org/docs/WEF_Paths_Towards_Free_and_Trusted_Data%20_Flows_2020.pdf

[5] https://www.dell.com/en-us/blog/break-down-data-silos-with-a-data-lakehouse/

IT Leadership

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The retail industry is transforming rapidly. Modern retailers rely heavily on automation for managing inventory, shelf design, customer service, and logistics. Video cameras and sensors that allow for unique store design help to enhance the customer experience. Technology is truly powering retail transformation, setting modern stores apart from traditional brick-and-mortar ones.

It is no easy feat sending all these video streams and sensor data to the cloud for real-time analysis. High bandwidth is required to move heavy data streams. So is low latency for quick data processing and decision making, especially when robotics is involved. 

This is where edge computing and edge-native applications become relevant for retail stores. They allow computing to occur closer to the source of data–right inside the store. Coupled with a private 5G communication network, retailers can deploy cost-effective and high performing ‘edge-native’ applications.

At the same time, companies must maintain secure environments and prevent fraud. According to a recent Microsoft blog, organizations can use security and compliance solutions in Microsoft 365 E5 to have visibility into their threat landscape and leverage built-in AI and machine learning in Microsoft Sentinel and Microsoft Defender for Cloud to proactively manage threats and reduce alert fatigue.

Read the full blog post to learn more.

Cloud Computing, Retail Industry

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As IT leader of self-regulatory body Professional Engineers Ontario (PEO), Doria Manico-Daka continues to build on her 16 years in tech, the last five of which has seen her heavily involved in leading digital transformation and modernization. Throughout her career, industries and company sizes have varied, but there’s been one constant: environments have largely been male dominated. And as a Black woman, she’s had some unique experiences as a double minority. Against the odds, however, she’s excelled not only for herself but toward collective efforts to elevate the conversation of diversity, opportunity, and sourcing talent in, and for, the workplace.

“One needs to be resilient and determined to pursue the passion and paths they’ve chosen,” she says. “There’s very little precedent or example to rely on, and it can be both challenging and rewarding at the same time. Challenging in the sense that it can be lonely sometimes. It can feel like an uphill battle when you’re in the minority, and especially if you have conscious and unconscious bias fighting against you. But at the same time, it can be rewarding just knowing you helped change the status quo, and change minds and environments for people to consider it’s normal to have women at all levels of the tech space. Early in my career, I had a role that included helping clients over the phone. I’d take calls and after introducing myself, the person on the other end would think it was a mistake and ask to be transferred to the technical team. But I was the technical team, so I’m glad that we’re past that in 2023 for the most part.”  

It can be difficult for women to have a sense of belonging facing these challenges. Speaking of the senior tech leader at the leadership table, there’s underrepresentation of women and even more underrepresentation of Black women. So resilience, fuelled by self reliance and confidence, helps to navigate a career path.

“Being in a minority can bring self-doubt, especially if you’re in an environment that isn’t supportive or causes doubts,” she says. “So know the value you bring to the table and the difference you’re making. Some environments are going to appreciate this more than others, but it’s important you don’t let others minimize your contributions. For example, if you work hard and lead your team to launch a tech solution that positively impacts the organization’s bottom line, that is value you can quantify. Having said that, we still have a ways to go about women in tech still being overlooked and passed over for promotions. The numbers are getting better, but we’re still there.”

CIO Leadership Live’s Rennick recently spoke with Manico-Daka about elevating standards of diversity to help achieve organizational goals and win the search for talent. Watch the full video below for more insights.

On Black women in tech: Breaking the glass ceiling for women in minority groups is still a business goal every organization should strive to achieve. And for Black women, the ceiling is made of concrete, so the organizations that are going to break through are the ones with talent at all levels. We’ve seen great improvements in lowers ranks in terms of inclusiveness, but the senior leadership roles in the boardroom still have a ways to go. I think soon it’ll no longer be acceptable to have non-diverse leadership teams. And we’re already seeing mandates on this, especially from forward thinking organizations that are intentional about diversity, equity and inclusion at all levels, not just the lower ranks. This is inclusive leadership that taps into a wider pool of talent, especially as we see the shortage of talent in tech. So organizations that lead with purpose, intention and empathy, and reflect the communities they serve, are the ones that are going to retain top talent, especially regarding women. One step organizations can take to raise equity is to be aware of unconscious bias and manage it through education or training. Just acknowledging we all have it and sometimes it gets in the way of making decisions in how we treat other people is progress. And again, that purposeful, intentional, empathetic leader is the one who is going to win in this case. Another is you need to create targets for equity and ensure those targets are measured and communicating progress of those targets. We know that only what’s measured can be improved.

On a clear approach to talent: For me, clarity of vision, purpose and meaningful work is a bare minimum in today’s world of talent. Any organizational leader who wants to attract talent today must have that focus on the greater good and the difference they’re going to make. What the COVID years have shown is that people are now starting to want to find meaning; they want to rediscover themselves and ask what really matters. So it’s important that what matters in business gets tied to a mission, vision and value system, and gets clearly communicated to the entire organization. And if you do that, you’ll have a chance of winning the talent pool.

On teamwork: Throughout my career, what I’ve found is a positive environment inspires creativity, motivation and delivery. So I always try to create that same thing for my team. We’re an environment that fosters positive work where achievements are rewarded and respect is given. And if somebody drops the ball, you pick it up and score. There’s no room for blame. That’s an environment that has inspired me to be very creative and innovative. So I figure if I create the same for my team, I’ll see results. And with our rapidly changing tech landscape, you also need to promote continuous learning and upskilling. Today, the shelf life of tech skills is about three years and it keeps shrinking. So as leaders, you need to ensure your team is in continuous learning mode. One thing I’ve done for my team is to create a space where they can use new technologies to implement ideas without affecting the digital services we offer. And that’s really helped us improve the tech skills, close the gaps, and foster that sense of innovation and knowing that it’s a safe space where they can try out their ideas.

On strategic leadership: The strategy in all aspects of the organization is key. So for me, what first comes into play is you need to care and act like an owner. Business owners look at the long-term success of the whole business. So in my experience, it’s important that a clear vision, mission, and goals are articulated at every level throughout the organization. When everyone understands and owns that, and begins to act like an owner of the business, then you have a recipe for success. Second, you need to play using the whole team by encouraging women to get in the tech sector and stay in tech, but also by encouraging diversity in general. By now, everyone knows it’s been proven that diversity of thought is good for business. When you have a diverse team, you simply have more ideas to work with and a greater talent pool to tap into. And that makes for very superior business solutions. So I would encourage any leader or any organization to focus on diversity at every level. And with this war on talent, this is the recipe that’s going to make you win.

Diversity and Inclusion, IT Leadership, Women in IT

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By Bryan Kirschner, Vice President, Strategy at DataStax

It’s high time to treat HR as every bit as important to your company’s artificial intelligence strategy as IT.

Alongside all the evidence that getting your developers working on AI is good for your business, there’s mounting proof that even providing the opportunity to work on—and work with—AI has a positive effect on job satisfaction, recruitment, and retention.

Getting this right matters a lot today. In 2022, McKinsey’s State of AI Report notes that “[s]oftware engineers emerged as the AI role that survey responses show organizations hired most often in the past year, more often than data engineers and AI data scientists … another clear sign that many organizations have largely shifted from experimenting with AI to actively embedding it in enterprise applications.”

And the stakes are high. In the data gathered for the latest State of the Data Race report, the developers most tapped into next-generation technologies (these are developers who describe themselves as “the first in their organization to learn about new tools and technologies” and those upon whom others rely on for answers about new tech) describe interacting with real-time data and building AI and ML-powered apps as the most important factors in deciding where to work.

Overall, developers in organizations with both AI and ML widely deployed were 15 percentage points more likely than those in organizations where AI and ML are in “the early days” of deployment to say that “tech is more exciting than ever.” Similarly, they were 18 points more likely to say they felt “energized” about their jobs.

AI: An opportunity for your developers to make an impact

It isn’t hard to grasp why. Developers have always leaned into technologies that enabled them to increase their impact and keep their skills up to date. (In State of the Data Race data, for example, about three quarters rate opportunities to learn and to use the latest technologies as important in their work.)

And now, with many CIOs feeling pressure from corporate teams to create AI apps that could quickly cut costs, AI offers the prospect of helping to recession-proof their jobs.

So it’s vital that your people strategy keeps up with the pace at which your competitors are pushing their developers to produce, but also the ways rivals equip them to not only be happier, but also more productive.

AI: A way to help your developers be more productive

AI has a role to play in this, too. New research that details GitHub Copilot’s impact on developer productivity and happiness really hammers this home. Nearly nine out of 10 (88 percent) of 2,000 developers surveyed said that using Copilot, a real-time AI assistant that offers code suggestions, made them more productive. Sixty percent said they felt more fulfilled with their job.

The words of one software engineer illustrate why: “(With Copilot,) I have to think less, and when I have to think it’s the fun stuff. It sets off a little spark that makes coding more fun and more efficient.”

But it’s arguably the perspective of one chief technology officer that tees up the call to action best: “The engineers’ satisfaction with doing edgy things and us giving them edgy tools is a factor for me. Copilot makes things more exciting.”

You already know that the apps that will most delight your customers and win you market share or margin going forward will be AI-driven. Developers who already work for you today—and those you might be keen to hire—are eager to get to work on them, and to use AI tools themselves. That’s a clear North Star for your business, people, and IT strategy to align toward, ASAP.

Learn how DataStax enables real-time AI here.

About Bryan Kirschner:

Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.

Artificial Intelligence, IT Leadership

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Generative AI has become a top priority among businesses even though IT leaders are expressing concerns about potential ethical issues posed by the technology, according to a new Salesforce survey.

Sixty-seven percent of senior IT leaders surveyed said they will be prioritizing the technology over the next 18 months, and 33% claimed it would be their top priority, the survey said.

Despite its perceived benefits, however, respondents to the survey remain skeptical about some of the ethical challenges currently surrounding generative AI, in particular that its output could be biased or inaccurate.

Various forms of AI have been used by businesses for decades. Generative AI is the latest major development in the field. According to IDC, it is a form of artificial intelligence that uses unsupervised and semi-supervised algorithms to create new content from existing materials, such as text, audio, video, images and code.

The Salesforce survey, which asked 515 senior IT leaders in the US about their thoughts regarding generative AI, comes a week after CEO Marc Benioff told analysts that the growth of AI presents an opportunity for Salesforce. It also precedes the launch later this week of the company’s EinsteinGPT, which Benioff said will complement the company’s Einstein technology.

Launched in 2016, Salesforce Einstein is an integrated set of AI technologies that brings artificial intelligence into all Salesforce products, which the company says ultimately provides customers with more personalized and predictive experiences.

Although 33% of respondents to the Salesforce survey think the technology is already “over-hyped,” 57% said they believe that generative AI is a “game changer.” According to the report, better serving customers, helping to take advantage of data, and allowing organizations to operate more efficiently were cited as the top benefits of the technology by 87%, 80% and 79% of respondents respectively.

In addition, 79% of senior IT leaders said generative AI will help reduce team workload and thereby reduce burnout, and 77% believe that the technology will help their organization serve their customers faster. Seventy-five percent of respondents said generative AI helps their organization sell efficiently.

IT leaders express concerns about AI

However, as recent events have shown (Microsoft’s Bing chatbot recently professed its love to a New York Times reporter and told him to get a divorce), generative AI is not without its problems, a concern reflected by a significant percentage of the senior IT leaders surveyed by Salesforce. Seventy-nine percent of respondents believe that the technology has the potential to be a security risk, while 73% are concerned it could be biased, and 59% of respondents believe generative AI outputs are inaccurate.

Furthermore, 66% of respondents said their employees don’t have the skills to successfully leverage generative AI, while 60% believe the technology won’t integrate into their current tech stack, and 59% don’t have a unified data strategy to implement generative AI successfully.

Consequently, 99% of senior IT leaders surveyed believe their business must take measures to better equip themselves to successfully leverage the technology.

The importance of acknowledging the technical and ethical concerns regarding the implementation of AI was further highlighted by the fact that 83% of respondents to the survey think businesses must work together to ensure generative AI is used ethically.

In comments posted along with the announcement of the survey findings, Clara Shih, CEO of Salesforce’s Service Cloud, said that generative AI represents a change in how organizations across industries will analyze data, automate processes, and empower different departments to improve customer relationships. However, she also warned that the technology is not without new risks and challenges.

“Whether generating a tailored sales email or customer support chat response, an ethics-first approach grounded in trusted data and human-in-the-loop workflows is what will allow enterprises to safely and responsibly use generative AI to deliver against today’s growing customer expectations,” Shih said.

Artificial Intelligence, Enterprise Applications

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The onset of the COVID-19 pandemic led many organizations to further adopt public clouds, and geopolitical conflicts have demonstrated the importance and need for sovereign clouds. Today, many organizations are already embracing or are moving to multi-cloud environments, but this multi-cloud reality does not come without its challenges.

As the nature of the cloud evolves, so does the strategy in which organizations must approach these challenges. What does remain the same is the cloud concerns the organization must manage, such as cost, performance, security, and app delivery.

Establishing a Cloud Center of Excellence is one way to ensure that these concerns are continuously and consistently managed, no matter where you are in your cloud transformation and as business needs evolve. More importantly, with the cloud underpinning modern organizations’ digital businesses, a Cloud Center of Excellence ensures that your cloud management strategy is in alignment with driving business outcomes.

1. Establish a Cloud Center of Excellence

While some organizations have already begun seeing success with multi-cloud strategies, having different business apps and services across different clouds can make it difficult for organizations to ensure approaches to processes and management concerns remain consistent.

To address cloud management concerns around cost, performance, security, and app delivery, many companies have established a Cloud Center of Excellence, a cross-functional team tasked with the responsibility of supporting and governing the execution of an organization’s cloud strategy. A Cloud Center of Excellence team would be responsible for establishing policies and guardrails, driving collaboration and adoption of best practices, and supporting the implementation of new and existing cloud technologies. Thus, enabling centralized management for a decentralized IT delivery model.

The Cloud Center of Excellence team will help identify who within the organization needs to be involved to ensure cloud objectives are well-defined and aligned with business goals. This group should include those responsible for managing cloud cost, cloud operations, security, application development, and enterprise architecture. Ultimately, a Cloud Center of Excellence is pivotal to driving collaboration and setting standards, policies, and best practices that ensure cloud operations are addressing ongoing management concerns across all clouds.

2. Empower Platform Teams by Simplifying Your Cloud Management Strategy

During VMware Explore Europe in Barcelona, VMware polled its audience in order to better understand their cloud management needs. The majority of audience members shared they felt it necessary to not only converge their tools but their teams as well. These same respondents also overwhelmingly indicated that if they could simplify their cloud management approach, it would help them achieve greater cost optimization results, more relevant business insights, and better guardrails around their cloud operations. Having different teams use multiple tools to manage their public and private clouds was an obvious pain point in their cloud management strategy.

Organizations can help meet the need for integrated teams and tools through platform teams. They not only build and run the platform that developers use to create new applications to drive business revenue, but they also serve as a channel between developer teams, operations teams, and security teams. The platform team provides a route for business leaders, security personnel, and the rest of the organization to communicate business needs and meet business challenges, including management concerns.

3. Empower Your Cloud Center of Excellence with Visibility

Without visibility of your cloud applications and their dependencies, it becomes impossible for the Cloud Center of Excellence team to achieve its objectives. Teams are unable to manage what they cannot see. Teams need visibility into the infrastructure to assess spending and application efficiency.

Visibility can be mutually beneficial if you provide your team access to a unified cloud management platform. Doing so enables teams to view factors such as cloud cost, resource utilization, and application performance by business groups across all clouds. A unified cloud management solution also helps teams proactively detect and remediate misconfigurations in your cloud environment. This saves time by not having to review data for violations that may not exist, leaving more time for the team to continue building out best practices.  

The main goal of cloud management is to correlate cloud decisions to business outcomes. Organizations that embrace a Cloud Center of Excellence will be able to pursue a cloud management strategy that’s in alignment with the goals of the business, while also remaining nimble to respond to challenges as the nature of the cloud evolves.

To learn more, visit us here.

Cloud Computing