Developers are hired for their coding skills, but often spend too much time on information-finding, setup tasks, and manual processes. To combat wasted time and effort, Discover® Financial Services championed a few initiatives to help developers get back to what they do best: developing. The result? More than 100,000 hours of developer toil have been automated or eliminated.

“A happy developer is one who’s writing code,” said Joe Mills, Director of Transformation Strategy and Automation at Discover. “So, we strive to create an inspiring culture and an exciting place to build your career. We want it to be easy to deliver value with the skillsets you have and to harness opportunities to refine your craft.”

Streamlining development through tools, knowledge, community

DevWorx is a program that simplifies the developer experience, streamlines work, and frees up time to innovate. Specifically, DevWorx is an online hub where developers across Discover can access prescriptive guidance for repetitive setup or deployment tasks, developer environments, self-service or automation tools, and a community of other developers to collaborate with.

“It’s basically a developer-driven community where we remove barriers to getting work done, focus on efficiency, and really enjoy coding as opposed to it feeling like a slog,” said Jonathan Stoyko, senior manager of strategic projects.

Developers can use DevWorx to standardize duplicate processes and reduce manual tasks. “If there’s a code structure that has to be reused every time you’re creating an application, that structure can be standardized as a template,” said Stoyko. “And we can store it in a common location so everybody has access to it and can contribute to it.”

Increasing productivity with step-by-step tutorials

Golden Paths are a key element of DevWorx. Golden Paths provide step-by-step tutorials for accomplishing specific development tasks within Discover. From making submissions, gathering approvals, and filling out prerequisite forms, Golden Paths covers the entire production lifecycle.

“If someone gets dropped into a new team, they can start coding within minutes and skip months of playing catchup,” said Andrew Duckett, senior principal application engineer and architect. “With Golden Paths, these processes are all codified and readily accessible.”

Developers are encouraged to contribute to existing paths and build new ones based on their own experiences.

Duckett continues: “We believe that it’s better to let the engineering community determine what works best for them, not to put a bunch of people in an ivory tower and dictate what is right. These developers are hired to innovate and solve problems, so we let them do that.”

Reducing manual tasks through automation

Automating manual tasks and repetitive processes is crucial for increasing developer efficiency. “Employing automation for tasks that many engineers face throughout their SDLC helps to shift focus towards human value-add activities. This also increases overall delivery throughput, with higher confidence in our development lifecycle, and produces consistent processes across teams that would otherwise be handled one-off and uniquely” said Joe Mills.

Developers can engage a team of automation experts to assess certain processes and tasks and help uncover automation opportunities. The team uses a hub-and-spoke model to scale their efforts across development teams at Discover and can help teams with robotic process automation, business automation, or code automation.

Reducing friction through consistent development practices

In addition to these initiatives, engineers at Discover adhere to a set of practices, internally called CraftWorx, that define and direct the agile development process. Aligning engineers across these practices reduces friction because engineers and developers are following the same development practices.

“If you’re trying to solve a problem and you think, ‘where’s the answer?’ CraftWorx aims to be that answer,” said Colin Petford, director of technology capability enablement at Discover. “It’s also constantly evolving along with our craft. It will never be finished because technology doesn’t sit still.”

Learn how Discover developers are using automation, Golden Paths, CraftWorx, and more.

Digital Transformation

If you’ve been reading a lot about quantum computing recently, you likely have a few questions.

Some of those questions may be about how quantum computing works. After all, it is very different from other kinds of computing. (You can learn a little about the basics in the recent CIO article Are you ready for quantum computing?)

You probably have one other very important question: What can quantum computing do for my business?

Until recently, most of the conversation about quantum computing has been academic. Researchers have been focused on getting the technology to work and engineers have been building systems with more qubits.

Now the emphasis is starting to shift to actual use cases as organizations take a closer look at quantum.

In a recent conversation, Victor Fong, Distinguished Engineer at Dell, and Michael Robillard, Sr. Distinguished Engineer at Dell, offered their thoughts on what quantum computing can do for businesses. The short answer is that quantum acts as an accelerator, allowing computers to complete some kinds of processing much more quickly than has ever been possible before.

To understand what that means, Fong and Robillard recommend that organizations get started by examining the technology. They laid out three steps they believe companies should be taking today:

1. Discover the potential of quantum computing

You may not have quantum computing experts on your staff today. That’s okay because your competitors almost certainly don’t have any quantum experts either. Only a small group of people currently have the expertise to be considered true experts in quantum computing.

Fortunately, you don’t have to be an expert to get started.

The first stage of preparing your organization for quantum computing is to do some foundational research. Look up some introductory guides. Read some articles. If you don’t know where to begin, Dell has a Quantum Computing Resource Center with white papers, analyst reports and recent news about quantum.

Be prepared to be a little confused at first.

Quantum computing is fundamentally different from the classical computers you use every day. Quantum computers rely on the principles of quantum mechanics, which Albert Einstein once described as “spooky action at a distance.” Quantum computing might seem strange — maybe even spooky — at first. But it operates by some basic rules that you can understand.

The computer hardware is also quite a bit different than what you’re used to. Quantum computers store information in qubits. Qubits operate at atomic scale, which means they are very, very small. They are also quite delicate. Small changes in the environment, referred to as “noise,” can easily disrupt the system enough that it cannot function as intended.

Developing and deploying systems built around these minuscule, sensitive parts is both difficult and expensive. But engineers are also developing simulators that mimic how quantum computers work. These simulators can be used to experiment with quantum.

2. Identify some quantum use cases for your organization

Once you understand the basics of quantum computing, you’ll be ready to start brainstorming ways that your organization can use the technology. 

Not every computing problem is well-suited to quantum processing. You wouldn’t want to use a quantum computer to do any kind of calculation that has one right answer. For example, you shouldn’t use a quantum computer to calculate your tax bill or process your payroll.

On the other hand, quantum computers can be very good at solving optimization problems. If you need to choose the best answer from a group of possible right answers, quantum computing may be ideal.

Some organizations are already experimenting with quantum computing for a variety of use cases:

Logistics and transportation firms are trying quantum computing as a way to find the most fuel-efficient, fastest and safest routes to deliver cargo and passengers while accounting for weather and traffic.

Financial firms want quantum computing to optimize their portfolios and maximize returns while mitigating risk.

Chemical and materials manufacturers seek to harness the power of quantum computing to come up with new formulas and model how a material’s properties will change under various conditions.

Drug companies want quantum computing to develop new treatments and vaccines for debilitating illnesses.

Auto manufacturers are testing quantum computing to help optimize the large batteries necessary for electric vehicles.

Technology companies of all kinds are experimenting to help develop new products and services or optimize those they already offer.

Even if you aren’t in one of these industries, you probably have similar use cases where quantum computing would be helpful. The key is to look for situations that are difficult to model because of a large number of variables. You also want use cases that are intrinsic to your business, where improving operations would have a large impact on your bottom line.

3. Deploy a test case.

Believe it or not, it’s not too early to start experimenting with quantum computing.

Anyone can download the open source Qiskit software development kit (SDK) that allows you to write code that will run on quantum systems.

A few vendors already offer access to quantum systems, although using these systems for experimentation can become expensive quickly.

Many people find it more affordable to begin by testing on a simulator. Quantum simulators use classical computing hardware to simulate the operation of quantum systems. They allow engineers to keep costs low while perfecting the code that they want to run on the quantum system. Simulators can also alleviate some data privacy concerns, and they eliminate the previously mentioned problem of quantum noise.

Finding the right tool for the job

Different kinds of computers are a little like the different kinds of saws you might use for woodworking. You can do most kinds of cutting with a standard circular saw. In the same way, a classical computer can do most kinds of calculations.

But some kinds of woodworking — like intricate scrollwork — are almost impossible to do with a circular saw. For that, you would want a jigsaw or even a scroll saw. And while you can do miter cuts with a circular saw, it’s a lot easier with a miter saw. A quantum computer should be thought of as a specialized tool. It won’t ever replace classic computing, but it makes some specialized tasks a whole lot faster and easier.

While engineers have made a lot of progress designing and building quantum computers, we’re still in the early days of the quantum era. Right now, quantum computing isn’t right for a lot of situations.

But as time goes on and the technology improves, quantum computing will become a better choice more often. And organizations that have already begun experimenting with the technology will have a head start.

That’s why now is the time to get started — learn more about quantum computing, identify test cases and begin to experiment.


Read more about Dell Technologies Quantum Computing here.

Read more about Intel Quantum Computing here.

Digital Transformation

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

Digital transformation has embedded IT at the center of business strategy, making all organizations technology enterprises today, irrespective of their industry. Business processes, culture, workflow, and systems are all necessarily impacted by digital transformation efforts, which by definition overhaul how business gets done, expediting efficiencies, modernizing the enterprise, and — when executed well — enhancing profitability.

It’s little wonder then that CEOs across the world are attaching high importance to digital transformation as a means for achieving their future goals. According to KPMG’s latest 2022 CEO Outlook survey released in January 2023, 72% of the 1,325 CEOs across 11 markets have an “aggressive digital investment strategy, intended to secure first-mover or fast-follower status.”

But driving radical change in any enterprise without a well thought out strategy is a recipe for disaster. Before embarking on digital journeys, IT leaders must address several key areas that could otherwise stymie the entire process.

Unfortunately, the following planning, or ‘phase 0,’ mistakes are too often made by IT leaders looking to move forward with digital implementations before they are truly ready to make good on investments.  

Failing to secure LOB bandwidth

IT leaders must first gauge the readiness of their organizational engine to drive digital transformation. Without adequate resources, intentional change can quickly become chaotic. Resource assessment is a vital phase 0 or even phase -1 process of any digital initiative.

Most digital transformation initiatives fail because of lack of resources. While IT leaders often focus on planning, evaluation, partnerships, and platforms, they often forget to assess the human resource bandwidth required to implement, execute, and make good on the completed program — in particular within the lines of business (LOBs) impacted by by digital transformation.

No digital initiative today can succeed without LOB sponsorship and involvement. For instance, if a company intends to overhaul its recruitment strategy with a new digital solution, there must be complete involvement of the human resource department. However, in most cases, the HR team already has its hands full with its day-to-day workload and is unable to take out time to work alongside IT on the project.

With low HR involvement in meetings and feedback phases related to the project, IT will struggle to hit goals and timelines, jeopardizing the initiative’s outcome.

While it is relatively easy for an IT leader to put his or her team in place before embarking on a digital transformation journey, it may not be as easy for LOB leaders to identify the right team members to be involved. Therefore, it is on CIOs to ensure that their LOB counterparts set aside the right talent from their departments to be involved in the process and prioritize their participation alongside their daily work. This must be done right at the start, not after the project has launched, else the CIO will have to continue to re-baseline the timing and requirements of the initiative. Cross-functional teams are vital to digital success, and CIOs should insist on them.

Misunderstanding the organization’s digital maturity

Another major reason digital transformations stumble is the lack of visibility business and technology leaders often have into their organization’s digital maturity before they begin. To become digitally mature, an enterprise must know its capabilities. This is an imperative precursor before deciding to go digital.

Each company has a different level of digital maturity at the enterprise, technology, and functional levels, and how it complements business. If business and technology leaders understand where they stand on this digital maturity curve, it gets easier to know where they intend to go and how long will it take to reach that destination.

The onus lies on the CIO to apprise top management on the status of the company’s digital maturity, so they know where they stand. For instance, if a company is in growth mode, it may need to align resources, scale up its technology platforms, and hire more employees. By getting to know the digital maturity in each of these functions, technology investments can be prioritized and aligned in relevant areas accordingly. In the absence of this, companies can make investments in wrong areas without realizing larger value.

Some questions CIOs can ask as part of the digital maturity discovery process could be: Does the company have a clear strategic vision, objectives, and direction? How does the company rank (laggard, mediocre, or leader) against its competition? How consistent is the organization’s digital experience across various channels?

To get more visibility into digital maturity, CIOs would be wise to create digital maturity indexes and link them to various facets of the business.

Launching without a clear mission

Any digital journey kicks off with a problem that is worth solving. It would, therefore, help if there is a single, clear statement that throws light on the problem at hand, those experiencing it, and the reasons to solve it. IT leaders may come up with lengthy project briefs and comprehensive RFPs but without a clear, precise problem statement they are all no good.

A well-honed problem statement provides clarity for all involved. Deep into the complexity of a transformation, team members can return to this document for guidance, using it to help address drift or any additional issues or questions that may arise along the way to ensure they stay on course and on mission. A clear problem statement can also be helpful if a technology leader has taken up three or four projects simultaneously, as it can help with prioritization issues and any overlapping complexities that might arise to help ensure each project is successful.

A simple exercise such as a drawing board session can go a long way in understanding the pain points of the relevant stakeholders and coming up with a refined problem statement. A couple of weeks of such a collaborative process, prior to getting into a long-drawn digital initiative, can help business and IT to get on the same page and ensure they stay focused on delivering the optimal outcome regardless of what they encounter along the way.

Digital Transformation

You might think that senior-level IT leaders have a lock on the art of landing jobs. After all, that’s partly how they reached such lofty heights, right?

But you’d be wrong. CIOs, vice presidents, directors — all make similar mistakes when they are on a job prowl, executive recruiters say. The two most common, and most fatal, are talking too much during an interview and resumes that are either too braggadocious or that go on and on and on.

“Our record was a 55-page resume,” says Judy Kirby, CEO of Kirby Partners, an executive search firm. “Not even their mother is going to read that.”

Here is a look at a dozen common mistakes even seasoned IT leaders make when looking to land new jobs, according to experts who can help.

Going grandiose

Charley Betzig, managing director of Heller Search Associates, has seen two candidates in the past year lose out on opportunities because of too-grandiose resumes. “These were great candidates, and we did our darndest to try to work with them to rewrite their resumes.” Both refused.

Betzig suggests instead sticking to the facts and keeping your resume “clean” by eschewing trendy design and offbeat type faces.

Finally, save your patents and published papers for the end of your resume and don’t lead with this information, Betzig says. “Employers don’t really care about that stuff,” he adds.

Failing to back up claims

In addition to holding your resume to a reasonable length, make sure it notes specific accomplishments. “I am a visionary innovator” doesn’t mean much to anyone wanting to learn about your credentials. (What did you innovate? In what way was that visionary?) Instead, talk about what your team produced and how, exactly, this helped your company create a new product, save money or time, generate revenue, or enter a new market.

Show, don’t just tell, how you’ve met specific challenges, whether strategic or operational.

Choosing ‘me’ over ‘we’

Similar to going grandiose, too many IT leaders forget that leadership is often more about team accomplishments than personal accolades.

Both on your resume and during interviews, recruiters emphasize focusing on ‘we,’ not ‘me.’ Nobody wants to hire someone who sucks all the oxygen out of the room or doesn’t play well with others. Make sure to share the credit with others on your team, and don’t talk trash about any company or person you’ve worked for.

Misunderstanding what makes a good interview

While you’re interviewing, answer the questions as succinctly as possible. Remember you’re not driving here; the interviewer is. “Be a listener first,” Betzig says. “Make sure it’s a conversation; listen and react.” He says that candidates are often so excited about landing an interview — or want to convey all their experience during the time allotted — that “they’re just bursting.”

Resist that impulse, and keep each of your answers to five minutes, maximum. Recently “we had a guy we thought was a great fit,” Betzig says. He had the qualifications and was a local candidate for the role. But the hiring company reported back that during the hour-plus interview, they were able to ask him only three questions because he talked so much.

“It was tough for them to imagine putting this person in front of their executives,” Betzig explains, and they wouldn’t consider doing another interview with him.

Overlooking the power of practice

If you’re working with an executive recruiter, that firm will likely do at least one mock interview with you and will video you in the process. “That can be a very sobering experience, to see yourself in action,” Kirby says. The recruiter will give you tips about how to improve your interviewing skills and resume because, after all, he or she gets paid if you do land the role, from the company that posted the job. It’s wise to take their advice.

And if you flame out after the first interview, you can often get feedback from the recruiter that you wouldn’t be able to get directly from the hiring manager because of perceived or real legal constraints.

If you’re looking for a job without the help of a recruiter, Kirby suggests you still enlist a trusted friend or peer to do a mock interview — and video it. Check to ensure you show enthusiasm for the job without being over the top, and make sure you answer the questions succinctly and without grandstanding.

Not seeing yourself clearly

Spending 20 years or longer at the same company isn’t necessarily viewed as favorably as it used to be, says Shawn Bannerji, managing partner for the data, digital, and technology leaders practice at Caldwell. Back in the day, it was considered a sign of loyalty to stick it out that long. But these days, staying at the same place for decades can be a negative.

The question is whether a person who’s been immersed in the same culture for so long “can be successful outside the norms of that specific organization,” Bannerji says. Many of the traditional leaders in their respective industries — such as GE, IBM, Morgan Stanley, and P&G — have multiple systems and processes set up to ensure their employees’ success, he explains.

After spending so long in one place, IT leaders can perhaps successfully transfer their expertise and skills to another organization or industry. But some hiring managers feel this category of candidate should “go somewhere else and prove it first, and then I’ll hire them,” Bannerji says.

If you do find yourself wanting to move on after a long stint in one company — anything over seven years — spend time thinking through exactly how your skills are transferrable. And make sure that is reflected on both your resume and in interviews.

Failing to have foresight

IT leaders seeking to build their careers further need to take the approach of successful pool players and think at least two moves ahead. Where are you in your career, and where do you want to be? How do your pay and benefits compare to those of your peers? That’s another strike against staying at one place too long; company lifers tend to miss out on the same pay jumps that more nimble IT leaders generally receive.

Career paths used to be more straight-line; “you’d work hard, get good reviews, and assume that path would lead to recognition, rewards, and promotions,” Bannerji explains. “But we’ve seen a departure of this path,” he says. People who want to rise in their careers need to acquire new skills and competencies, and “develop a portfolio that’s a professional calling card” or else “opportunities can pass them by.”

He advises you to find a mentor who can act as a career sherpa to “advise you how to invest your professional capital” and to help you determine which skills you should be focusing on at any point in time. If, say, you’ve spent a decade in infrastructure, try to develop more direct business acumen and broader management or strategy expertise.

Getting rusty on tech

Conversely, a business degree and strategy proficiency alone won’t cut it as a CIO in today’s world. “The role is evolving to have more substantive technical dimensions,” Bannerji explains. “Cybersecurity, AI, machine learning, the journey to the cloud” are all important on a resume today. Digital supply chains and other areas also require technical chops.

It’s also important to understand product development because IT is expected to help or sometimes even lead in that regard.

Not honoring the job description

It can be tempting, and sometimes okay, to ignore some things on a job description’s checklist that don’t fit. But if you apply for a position that specifies an advanced college degree as a minimum requirement, and you have a bachelor’s, don’t expect to land the interview no matter how much experience you may have.

Also make sure the job is something you really can handle. If the organization wants an implementer, and you’ve been mostly a strategist, “that’s not the same thing,” Kirby says. Even if you force-fit things and you’re lucky enough to be hired, chances are good that the position won’t be sustainable for very long and you’ll be job-hunting again before you know it.

And, if you don’t calculate all the key elements correctly — position, company, pay, and location — you can “throw off the entire equation,” Bannerji adds.

Losing sight of the social-media details

Particularly at the senior or executive level, you and your entire family are on view. Hiring managers routinely check social-media accounts for inappropriate photos or posts, especially regarding you and your spouse, for a clue about how you both might conduct yourselves at corporate events and how you represent yourselves in the broader world.

If you don’t want people snooping, adjust your social accounts’ privacy settings while you’re job hunting — and suggest that all the members of your immediate family do the same. Something that’s ‘cool’ or ‘cute’ or ‘funny’ might not translate the same to anyone who doesn’t already know you.

You might survive an Instagram photo of yourself barbequing in your Speedo, if you insist on keeping that visible online, but make sure your LinkedIn account and other more professional venues don’t show you in sweatpants or risqué clothing, or looking (or acting) inebriated. Vet your videos and invest in some professional photos.

Kirby recalls a situation when one company’s internal candidate was determined to sabotage his closest rival, an external candidate. The internal person found photos of the external guy at a party with drinks in both hands and acting goofy, all while standing next to an X-rated cardboard cutout. Internal Guy emailed the photos to hiring managers, and in the process both candidates were thrown out of contention.

You can still be you, of course; just don’t leave any potentially damaging documentation of your wildest moments in places where recruiters or hiring managers can find it.

Failing to read the room

To survive executive-level interviews, you must hone your emotional quotient (EQ) skills, Kirby advises.

“One of our candidates was showing off his deep knowledge of baseball and failed to notice that one of the other people in the room had her eyes glazed over.” It cost him the job.

Forgoing leveraging your network

Job hunters “often don’t want to be a bother to their contacts,” Heller’s Betzig says. “But that’s a big mistake. Your contacts want to be there for you, to be the person to help you find your next job.” Make time to network; he advises reaching out to five to 10 contacts each day.

Get in touch with everyone you know from your former jobs and those you’ve met in various professional organizations, explain what you’re looking to do and ask if they’ve heard of anything related to that and to let you know if they do. “Chances are that IT leaders’ next jobs will come from their network,” Betzig adds.

Careers, IT Leadership, Resumes

Enterprises worldwide are not tapping the potential of their data when making critical business decisions and navigating uncertain macroeconomic conditions, according to a Salesforce survey.

Nearly 67% of 10,000 business leaders polled globally are not using data to set pricing in line with economic conditions such as inflation, according to the Untapped Data Research survey.

Only 29% of these leaders are using data to set strategy when launching products or services in new markets, and just 17% are using data to achieve their climate goals, according to the survey. Just 21% of the survey respondents said they are using data to make decisions about their company’s diversity goals.

The lack of data utilization is happening even though 80% of the leaders said that data is critical to decision making and 73% said that data reduces uncertainties.

The business leaders who were polled also believe that data can help generate more efficiency and trust in their organizations if leveraged correctly, according to the survey. Nearly 72% of these leaders said that data keeps people focused on the things that matter and that are relevant to the business.

In addition, more than 66% of the executives surveyed said that they think data can help minimize the influence of personal opinions or egos in a business conversation.

Data deluge sparks operational challenges

The volume of data generated and the lack of knowledge to operationalize or utilize it in the most effective way are impediments to tapping the potential of enterprises’ data reserves, according to survey respondents.

“While 80% of business leaders say data is critical in decision-making, 41% cite a lack of understanding of data because it is too complex or not accessible enough. What’s more, one-third of leaders said they lack the ability to generate insights from data,” Francois Ajenstat, chief product officer at Tableau, wrote in a blog post.

Salesforce acquired visual analytics software provider Tableau in August 2019.

In addition to the impediments cited by Ajenstat, the volume of data generated globally is expected to more than double by 2026, adding to more complexities for enterprises, according to the study.

Investing in data literacy skills could be the solution

Enterprise leadership teams can work to eliminate these impediments by investing in data literacy programs for employees and weaving a data culture into the fabric of the enterprise, according to Ajenstat.

“If a company doesn’t yet have a data culture, then they need to invest in platforms that allow them to turn repeatable processes into core capabilities,” Ajenstat said, adding that data literacy programs should be offered to all employees.

The proliferation of generative AI and natural language processing will break down learning barriers for employees, Ajenstat said.

“These innovations are giving non-data people the confidence to make an informed decision and act on it,” Ajenstat wrote.

Data Management

As CIO of United Airlines, Jason Birnbaum is laser focused on using technology and data to enable the company’s 86,000 employees to create as seamless a customer travel experience as possible. “Our goal is to improve the entire travel process from when you plan a trip to when you plan the next trip,” says Birnbaum, who joined the airline in 2015 and became CIO last July.

One opportunity for improvement was with customers who are frustrated about arriving at the gate after boarding time and unable to board because the doors are shut, while the plane is sitting on the ground. “The situation is not only frustrating to our customers, but also to our employees,” Birnbaum says. “We are in the business of getting people to where they want to go. If we can’t help them do that, it drives us crazy.”

So, Birnbaum and his team built ConnectionSaver, an analytics-driven engine that assesses arriving connections, calculates a customer’s distance from the gate, looks at all other passenger itineraries, where the plane is going, and whether the winds will allow the flight to make up time, and then makes a real-time determination about waiting for the connecting passenger. ConnectionSaver communicates directly with the customer that the agents are holding the plane.

ConnectionSaver is a great example of how a “simple” solution resulted from a tremendous amount of cultural, organizational, and process transformation, so I asked Birnbaum to describe the transformation “chapters” behind this kind of innovation.

Chapter 1: IT trust and credibility

“For years, it was common for technology organizations to have too little credibility to drive transformation,” says Birnbaum. “That was our story, and we worked very diligently to change the narrative.”

Key to changing the narrative was giving senior IT leaders end-to-end business process ownership responsibilities. “We started moving toward a process ownership model several years ago, and since then, we’ve made significant improvements in technology reliability, user satisfaction, and our employees’ trust in the tools,” Birnbaum says. “This is important because every transformation chapter depends on use of the technology. If our employees don’t trust the tools, we will never get to transformation.”

A process could be gate management, buying a ticket, managing baggage, or boarding a plane, each of which runs on multiple systems. “Before we moved from systems to process ownership, people would see that their system is up, so they would assume the problem belonged to someone else,” says Birnbaum. “In that model, no one was looking out for the end user. Now, we have collaborative conversations about accountability for business outcomes, not system performance.”

Chapter 2: Improving the employee experience

Like every company, United Airlines has been working to improve the customer experience for years, but more recently has expanded its “design thinking” energies to tools for employees. To facilitate this expansion, Birnbaum grew the Digital Technology employee user experience team from three people to 60, all acutely focused on integrating the employee experience into the customer experience.

The employee user experience team spends time with gate agents, contact centers, and airplane technicians to identify technology to help employees help customers. “The goal of the employee user experience team is to provide tools that are intuitive enough for the employee to create a great customer experience, which in turn, creates a great employee experience,” says Birnbaum. “It is important for companies to invest in change management, but you need less change management if you give employees tools that they really want to use.”

For example, the user experience team learned that flight attendants felt ill equipped to improve the experience of a customer once the customer is on the plane. If a customer agreed to change seats or check a bag, for example, there was little a flight attendant could do to improve the experience in real-time. “All they had was a book of discount coupons, but the customer had to call a contact center with a code to get the discount,” says Birnbaum. “The reward required five more steps for the customer; it did not feel immediate.”

So, the team developed a tool called “In the Moment Care,” which uses an AI engine to make reward recommendations to the flight attendant who can offer compensation, miles, or discounts in any situation. The customer can see the reward on his or her phone right away, which immediately improves both the customer and employee experience. “We knew the customers would be happier with having their problem solved in real-time, but we were surprised by how much the flight attendants loved the tool,” says Birnbaum.  “They said, ‘I get to be the hero. I get to save the day.’”

The employee user experience team then turned its attention to the process of “turning the plane,” which includes every task that happens from the minute a plane lands to when it takes off again. It involves at least 35 employees in a 30-minute window.  

Take baggage, for example. Traditionally, during the boarding process, if the overhead bins were starting to fill up at the back of the plane, that flight attendant had no way to communicate to the flight attendant in the front of the plane that it is time to start checking bags. Their only option was to call the captain to call the network center to call the gate to get them to start checking bags.

To create a better communication channel, the employee user experience team worked with the developers to create a new tool, Easy Chat, that puts every employee responsible for a turn activity into one chat room for the length of the turn. “Whether the bins are filling up, or they need more orange juice, or they are waiting for two more customers to come down the ramp, the team can communicate directly to digitally coordinate the turn,” says Birnbaum. “Once the flight is gone, each employee will be connected to another group in another time and place.”

Again, Birnbaum sees that the value of Easy Chat is well beyond the customer experience. “I was just talking to a few flight attendants the other day, who told me that Easy Chat makes them feel like they are a part of a team, rather than a bunch of people with individual roles,” says Birnbaum. “United has a lot of employees, and they don’t work with the same people every day. The new tool allows us them to work as a team and to feel connected to each other.”

Chapter 3: Data at scale

To improve the analytics capabilities of the company, Birnbaum and his team built a hub and spoke model with a central advanced analytics team in IT that collaborates with each operational area to develop the right data models. 

“The operating teams live and breathe the analytics — they are the people scheduling the planes — so they are key to unlocking the value of the analytics,” says Birnbaum. “Digital Technology’s job is to collect, structure, and secure the data, and help our operational groups exploit it. We want the data scientists in the operating areas to take the lead on how to make the data valuable at scale.”

For example, United has always worked to understand the cause of a flight delay. Was it a mechanical problem? Did the crew show up late? “The teams would spend hours figuring out whose fault it was, which was a huge distraction from running the operation,” says Birnbaum. To solve this problem, the analytics team, in partnership with the operations team, created a “Root Cause Analyzer” that collects operational data about the flight.

“Now, instead of spending time debating why the flight was delayed, we can quickly see exactly what happened and spend all of our time on process improvement,” says Birnbaum.

With the foundational, employee experience, and data chapters now under way, Birnbaum is thinking about the next chapter: Using technology and analytics to integrate and personalize a customer’s entire travel experience.

“If you have a rough time getting to the airport, but the flight attendant greets you by your name and knows what you ordered, you will still have a good trip,” says Birnbaum.  “It is our job to use technology to help our employees deliver that great customer experience.”

Digital Transformation, Employee Experience, Travel and Hospitality Industry

Topping the list of executive priorities for 2023—a year heralded by escalating economic woes and climate risks—is the need for data driven insights to propel efficiency, resiliency, and other key initiatives. Many companies have been experimenting with advanced analytics and artificial intelligence (AI) to fill this need. Now, they must turn their proof of concept into a return on investment. But, how? 

Organizations are making great strides, putting into place the right talent and software. Yet many are struggling to move into production because they don’t have the right foundational technologies to support AI and advanced analytics workloads. Some are relying on outmoded legacy hardware systems. Others are stymied by the cost and control issues that come with leveraging a public cloud. Most have been so drawn to the excitement of AI software tools that they missed out on selecting the right hardware. 

As the pace of innovation in these areas accelerates, now is the time for technology leaders to take stock of everything they need to successfully leverage AI and analytics.

Look at Enterprise Infrastructure

An IDC survey[1] of more than 2,000 business leaders found a growing realization that AI needs to reside on purpose-built infrastructure to be able to deliver real value. In fact, respondents cited the lack of proper infrastructure as a primary culprit for failed AI projects. Blocking the move to a more AI-centric infrastructure, the survey noted, are concerns about cost and strategy plus overly complex existing data environments and infrastructure.

Though experts agree on the difficulty of deploying new platforms across an enterprise, there are options for optimizing the value of AI and analytics projects.[2] Foundational considerations include compute power, memory architecture as well as data processing, storage, and security. 

It’s About the Data

For companies that have succeeded in an AI and analytics deployment, data availability is a key performance indicator, according to a Harvard Business Review report.[3] In short, the report’s successful leaders have democratized their company’s data—making it accessible to staff, acquiring it from customers and suppliers, and sharing it back. Dealing with data is where core technologies and hardware prove essential. Here’s what to consider:

Ingesting the data: To be able to analyze more data at greater speeds, organizations need faster processing via high-powered servers and the right chips for AI—whether CPUs or GPUs. Modern compute infrastructures are designed to enhance business agility and time to market by supporting workloads for databases and analytics, AI and machine learning (ML), high performance computing (HPC) and more. Storing the data: Many organizations have plenty of data to glean actionable insights from, but they need a secure and flexible place to store it. The most innovative unstructured data storage solutions are flexible and designed to be reliable at any scale without sacrificing performance. And modern object storage solutions, offer performance, scalability, resilience, and compatibility on a globally distributed architecture to support enterprise workloads such as cloud-native, archive, IoT, AI, and big data analytics.Protecting the data: Cyber threats are everywhere—at the edge, on-premises and across cloud providers. An organization’s data, applications and critical systems must be protected. Many leaders are seeking a trusted infrastructure that can operate with maximum flexibility and business agility without compromising security. They are looking to adopt a zero-trust architecture, embedding security capabilities across an enterprise-wide line of storage, servers, hyperconverged, networking, and data protection solutions. Moving the data: As the landscape of data generation shifts and data traffic patterns grow more complex, surging demands require a network reevaluation in most organizations. For data to travel seamlessly, they must have the right networking system. However, traditional proprietary networks often lack scalability, proven cloud-based solutions, and automation, while open-source solutions can be expensive and inflexible. Open networking answers the challenge by accommodating software choice, ecosystem integration, and automation for the modern enterprise from edge to core to cloud.Accessing the data: Increasingly, AI development and deployment is taking place on powerful yet efficient workstations. These purpose-built systems enable teams to do AI and analytics work smarter and faster during all stages of AI development, and increasingly during deployment as they support inferencing at the edge. And to give employees access to the data they need, organizations will need to move away from legacy systems that are siloed, rigid and costly to new solutions that enable analytics and AI with speed, scalability, and confidence. A data lakehouse supports business intelligence (BI), analytics, real-time data applications, data science and ML in one place. It provides rapid, direct access to trusted data for data scientists, business analysts, and others who need data to drive business value. 

Focus on Outcomes

Analytics and AI hold the promise of driving better business insights from data warehouses, streams, and lakes. But first, enterprises will need to honestly assess their ability to not just develop but successfully deploy an AI or analytics project. Most will need to modernize critical infrastructure and hardware to be able to support AI development and deployment from edge to data center to cloud. Those that do so will find their data and applications to be force multipliers. Along the way, they will have implemented upgrades that keep data secure and accessible—imperatives for meeting IT and business objectives in the months and years to come. 

To learn more about Creating an End-to-End Infrastructure for AI Successread the IDC white paperand visit


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IT Leadership

The effects of such an unpredictable environment are profound, and no organization in any industry is immune. Looking across our client base, we expect to see varying degrees of impact as the turbulence continues. The common thread? In almost every case, there’s an increased need for data insight and technology-enabled agility to reaffirm technology’s position at the center of investment strategy in order to achieve organizational growth.

So when it comes to securing funding and resources from the board, is the CIO put in the box seat if technology is at the center of investment strategy? Not necessarily. While investing in technology is key—and becoming more so—this doesn’t mean that CIO budgets won’t come under pressure, both for capital spend as well as for operations and maintenance (O&M). That’s why forward-thinking CIOs are taking action today to strengthen their position. And no matter the industry, we believe there are four smart moves that any CIO can make now to help them weather any economic storm.

1. Optimize cloud spend

It’s a good time for CIOs to conduct a financial health check on their technology budget. This includes running a benchmarking spend analysis on all categories relative to industry peers, as well as leading technology companies. Then, identify opportunities to reduce run costs and free up funds to invest in transformation and new technology capabilities. Specifically, look at your organization’s newer areas of technology spend, especially since the last economic downturn. What’s the biggest change you’ll find? Almost invariably, spending on cloud has leapt from low or even non-existent to high. However, in many cases, that money could be spent more effectively; we often see clients using cloud in a capital-intensive way that mimics how they used to use datacenters. Remember, you don’t own cloud servers, you just “rent” them. So your usage and costs should be elastic, expanding and contracting with workload. That’s a core benefit of cloud.

That’s why one of the first moves to consider is optimizing your cloud spend. An easy example? Shut down the testing environment when you’re not using it. And consider different types of storage for different classes of data: highly-available and responsive storage for transactional data, and higher-latency and lower-cost for data not needed immediately. You should also scrutinize the bills from your cloud providers. These are often extremely complicated, running into millions or hundreds of millions of line items. FinOps for cloud can help track and optimize this spending while reaping major benefits on top. For instance, a robust FinOps capability can prevent spend commitment mistakes, and help you switch from a “lift-and-shift” approach founded on a datacenter mentality to a true cloud-centric model that realizes cloud’s full potential.

2. Double down on automation

If your IT budget, and maybe your business as a whole, is under pressure in the current environment, then automating more business processes is a natural step. But it’s important to implement automation for the right reasons, looking beyond the obvious cost savings to consider how it contributes to broader enterprise strategy. Of course, automating procedural, repeatable tasks via robotic process automation (RPA) not only cuts cost but frees up talent for higher-value, more strategic activities, enabling the business to do more with fewer people and address talent supply issues. The results? Higher efficiency and better outcomes. While many organizations are already implementing RPA, few are doing it at scale, and most haven’t yet fully embraced the more advanced “intelligent” automation opportunities via artificial intelligence and machine learning that can unlock true end-to-end automation. Given this, the CIO should become the driver of enterprise automation. 

3. Be open with suppliers on budget constraints

Try talking to your suppliers about the cost squeeze you’re facing, and you might be pleasantly surprised at their response. If you treat them as true partners and give them the opportunity to make suggestions for ways to save costs, they’ll probably come back with creative ideas. This reflects our own experience: we’ve worked with clients through downturns in industries like steel and utilities, and we know they expect us to offer creative ways to do things more cost-effectively. Whether it involves outsourcing, insourcing or something else, your suppliers or partners will often have great ideas.

4. Review software licenses and subscriptions

Many organizations are over-licensed and oversubscribed on software, pushing costs higher than they need to be. There are several ways to tackle this problem. One is to take steps to optimize subscription fees on expensive licenses by verifying the user base uses a software product or even separately licensed/subscribed features. Another is to identify savings opportunities from using open-source components instead of commercial software. Further, most software license agreements include annual processes to reset maintenance costs when consumption patterns change. Then of course there’s rationalization of products that are functionally redundant or can be archived/retired. While CIOs can carry out this license management themselves, a more effective approach could be to use a partner with specific expertise, who can detect in real time where an application is being used, and help recommend approaches to reduce spend.

With those four moves in mind, and in the drive to reduce costs amid ongoing uncertainty, CIOs may be tempted to cancel a project in its final stages to stop spend. But if that project involves retiring an asset or getting rid of a datacenter, companies should press on for multiple reasons. One is that by stopping, they’ll prolong technical debt into the future for a short-term benefit. Another is that once finished, maintenance costs, like on on-premise servers, will go away. So don’t stop short of the finish line and neglect to collect the savings.

Agile Development, Budgeting, CIO, Cloud Management, Data Center Management, IT Leadership

Companies today face disruptions and business risks the likes of which haven’t been seen in decades. The enterprises that ultimately succeed are the ones that have built up resilience.

To be truly resilient, an organization must be able to continuously gather data from diverse sources, correlate it, draw accurate conclusions, and in near-real time trigger appropriate actions. This requires continuous monitoring of events both within and outside an enterprise to detect, diagnose, and resolve issues before they can cause any damage.  

This is especially true when it comes to enterprise procurement. Upwards of 70% of an organization’s revenue can flow through procurement. This highlights the critical need to detect potential business disruptions, spend leakages (purchases made at sub-optimal prices by deviating from established contracts, catalogs, or procurement policies), non-compliance, and fraud. Large organizations can have a dizzying array of data related to thousands of suppliers and accompanying contracts.

Yet amassing and extracting value from these large amounts of data is difficult for humans to keep up with, as the number of data sources and volume of data only continues to grow exponentially. Current data monitoring and analysis methods are no longer sufficient.

“While periodic spend analysis was okay up until a few years ago, today it’s essential that you do this kind of data analysis continuously, on a daily basis, to spot issues and address them quicker,” says Shouvik Banerjee, product owner for ignio Cognitive Procurement at Digitate.

Enterprises need a tool that continuously monitors data so they can use their funds more effectively. Companies across industries have found success with ignio Cognitive Procurement, an AI-based analytics solution for procure-to-pay. The solution screens purchase transactions to detect and predict anomalies that increase risk, spend leakage, cycle time, and non-compliance.

For example, the product flags purchase requests with suppliers who have a poor track record of compliance with local labor laws. Likewise, it flags urgent purchases whose fulfillment is likely to be delayed based on patterns observed in similar transactions in the past.  It also flags invoices that need to be prioritized to take advantage of early payment discounts.

“It’s a system of intelligence versus other products in the market, which are systems of record,” says Banerjee. Not only does ignio Cognitive Procurement analyze an organization’s array of transactions, it also takes into account relevant market data on suppliers and categories on a daily basis.

ignio Cognitive Procurement is unique for its ability to correlate what’s currently happening in the market with what’s going on inside an organization, and it makes specific recommendations to stakeholders. For example, the solution can simplify category managers’ work, helping them source the best deals for their company, or make decisions such as whether to place an order now or hold off for a month.

Charged with finding the best suppliers and monitoring their success within the context of the market, category managers work better and smarter when they can tap into ignio Cognitive Procurement.

ignio Cognitive Procurement also identifies other opportunities to save money and improve the effectiveness of procurement. For instance, the solution proactively makes business recommendations that seamlessly take into account not only price, but also a variety of key factors like timeliness, popularity, external market indicators, suppliers’ market reputation, and their legal, compliance, and sustainability records.

“Companies also use the software to analyze that part of spend that’s not happening through contracts,” says Banerjee, “and they’ve been able to identify items which have significant price variance.”

To avoid irreversible damage or missed opportunities and to keep a competitive advantage, organizations across industries urgently need an AI-based analytics solution for procure-to-pay that can augment their human capabilities.

To learn more about Digitate’signio Cognitive Procurement, click here.

Analytics, IT Leadership