Beyond one’s own personal relationships, opinions on how others conduct theirs are usually none of anyone’s business. But when it comes to actual business, George Al Koura, CISO of online dating company Ruby Life, has built a career on how long-term success depends on building team cohesion within the organization, and elevating the relationship with partners outside it.

“We’re effectively a software company, but we have to humanize one another,” he says. “When we look at today’s hot resource market, competing for talent on traditional lines has been a bit of an archaic and sometimes toxic game where personnel leave organizations within months of joining due to offers of substantially greater compensation or benefits. This situation isn’t strategically feasible at the industry level.”

Also unsustainable are interactions with vendors that are only there to make their quarterly quota and no sense of loyalty. “That’s not the best way of doing business nor the best career fostering real improvement opportunities,” he says.

Turning a vendor into a partner, he says, takes an understanding of business outcomes and anticipating change in the sector that need a pivot or reaction, and then help you understand that. “There’s still work to do in that area of collaboration but there are positive signs,” he says.

For Al Koura, it’s a constant learning process inherent to a leadership journey that was never straightforward or predetermined from the start, with a non-traditional path to entry for a tech career.

“I actually don’t have a formal STEM education,” he says. “I studied political science psychology at military college and served in the Army regular forces until about 2016. I had a technical job as a communications research operator, but after a while, I wanted a new challenge. I started a couple of businesses but ultimately nothing stuck. Yet there’s something to be said about failing fast and failing often. Looking back at those times, I was learning a lot of great lessons that would serve me later in life. But those lessons were definitely learned the hard way.”

CIO Leadership Live’s Rennick recently spoke with Al Koura about the importance of collaboration with colleagues and forging reliable, long-lasting relationships with business partners. Watch the full video below for more insights.

On learning on the job: I was a junior analyst doing shift work at a 24/7 global operation. While I enjoyed my time in software, I knew I was capable of more. So I spent a bunch of my overnight shifts reviewing all the SLAs for the company’s entire 80 plus clients to understand the business of cyber and what the organization actually did. In doing so, I found they sold some managed threat intelligence services that we weren’t delivering on. It was a light-bulb moment and I realized I had an opportunity to build those services and advance my career further. At the time, one of my VPs was John Proctor, who’s now CEO of Martello Technologies. He and I go back over 10 years serving in the Army together, and he was a bit of a trade mentor for me then. We always had a good relationship and I told him about what I saw and he gave me an opportunity to build that capability out. What’s interesting is I had no formal education or training on threat intelligence, and I was learning security operations in the cyber role on the fly at the time. So my version of a CTI service was built around something different from anything else you’d find in the market because I was leaning on a decade of military intelligence training and converting that knowledge into customer value within a CTI context. The success of that service company was promoted out of the SOC and into a senior consultant role where I had my first commercial team. A lot of good and bad times in those days, but most importantly I was learning and getting better every day.

On the CISO-CIO relationship: At both my current and previous employer, I had the privilege of working with two outstanding CIOs. Our infrastructure was handled by IT veteran Tim Farrington, who’s been doing this for over 20 years in SMEs throughout Ontario. He was very resourceful and organized in his approach to infrastructure management. Together we got the organization ISO 27001 certified, which took about two years. So a lot of important leadership lessons were learned through that process. Now I work with our current CIO, Srdjan Milutinovic, who’s also very highly experienced. He’s been an empowering mentor and believes in hiring the right people in the right roles and letting them drive what needs to happen in their respective areas. He’s personally driven the transition of our entire company into an agile and safe methodology of software development, meaning he understands and expects empathetic, results-driven leadership out of all his respective department heads. I consider him a mentor and I enjoy the opportunity to soak up as much knowledge and trade experience I can with him. And if I look at the qualities and people he’s brought in, you can see the sense of loyalty and respect he commands. That’s what you’re looking for in a CIO.

On collaboration: I can’t get too deep into our own tech stuff specifically, but an example of a great collaborative partner or vendor partner I have is my relationship with Record Future. They have the best CTI platform, but they also have talented account and technical support. I’ve worked with them and their platform across every one of my employers throughout my entire InfoSec career. A lot of vendor relationships are very transactional and I find that’s not very genuine in terms of the care they give you. But my discussions with RF are never driven around whatever new widget or service they’re pushing. Rather, they continually assess where they can provide additional value to the state of my operations by having sales growth and development conversations focused on improving our current level of maturity. It’s a committed collaboration partner with a stake in seeing us succeed, and not simply in making their quarterly quotas. And I think that’s what it takes.

On team building: My approach has been to lean on my network, to scout, develop and capture talent by creating my own social pipeline. When someone is in school or a new grad or mid-career, the key thing I focus on is building genuine relationships with them. That investment in time, effort and care is the differentiating factor that makes them want to work with me, even if I can’t pay the same as a Silicon Valley company. In a market where employers and employees are all playing the numbers game against one another to untenable levels, the focus should be on real human relationships and looking at employment as a vehicle to a better quality of life for your people. That’s what makes it worth the time to actually pursue and fill that new head count with that individual. Once you build a pool of known, hopefully trusted talent already waiting for the opportunity to work directly with you, it’s just a matter of making sure the opportunity is right for them and working together to achieve that.

C-Suite, CIO, IT Leadership, Relationship Building, Vendor Management

As one of the world’s largest biopharmaceutical companies, AstraZeneca pushes the boundaries of science to deliver life-changing medicines that create enduring value for patients and society. To accelerate growth through innovation, the company is expanding its use of data science and artificial intelligence (AI) across the business to improve patient outcomes. 

AstraZeneca has been on a multiyear journey to transform its scientific capabilities to enhance its understanding of disease, design next-generation therapeutics, pioneer new clinical approaches, and better predict clinical success. For example, as part of its efforts to unlock different human genomes, AstraZeneca is working toward the analysis of up to 2 million individual genomes by 2026. This initiative alone has generated an explosion in the quantity and complexity of data the company collects, stores, and analyzes for insights. 

“We needed a new approach to manage and analyze that data to accelerate the delivery of life-changing medicines for patients,” said Gurinder Kaur, Vice President of Operations IT at AstraZeneca. 

Gurinder Kaur, Vice President of Operations IT,  AstraZeneca

AstraZeneca

The new approach involved federating its vast and globally dispersed data repositories in the cloud with Amazon Web Services (AWS).  Unifying its data within a centralized architecture allows AstraZeneca’s researchers to easily tag, search, share, transform, analyze, and govern petabytes of information at a scale unthinkable a decade ago. 

What began as an initiative focused on R&D now has extended to the company’s three other major business units: Commercial, Operations, and Clinical, according to Kaur. The goal, she explained, is to knock down data silos between those groups, using multiple data lakes supported by strong security and governance, to drive positive impact across the supply chain, manufacturing, and the clinical trials of new drugs. 

“Our ambition is finding a way to take these amazing capabilities we’ve built in different areas and connect them, using AI and machine learning, to drive huge scale across the ecosystem,” Kaur said. “Beyond R&D, we see value in extracting insights from data sources to improve patient outcomes and deliver personalized medicines.”

The cloud-based platform allows AstraZeneca scientists to move from ideas to insights faster, accelerating both drug discovery and clinical trials, to improve patient outcomes.

Moving from ideas to insights faster

AWS’s expertise with scaling cloud services was invaluable in helping AstraZeneca build an end-to-end machine learning platform, called AI Bench, to make it easier to apply machine learning across the enterprise. “AI Bench is a set of automated tools and guardrails that help us spin up the right environments in an automated fashion, so our data scientists can quickly begin working in a safe, secure, environment while ensuring regulatory compliance,” said Brian Dummann, AstraZeneca’s Vice President of Insights & Technology Excellence. “Before AI Bench, every data science project was like a separate IT project. We would spend weeks getting the right environment in place.”

Brian Dummann, Vice President of Insights & Technology Excellence, AstraZeneca

AstraZeneca

Built on Amazon SageMaker, a service to build, train, and deploy ML models, AI Bench has accelerated the pace of innovation and reduced the barrier of entry for machine learning across AstraZeneca.  

“We have reduced the lead time to start a machine learning project from months to hours,” Kaur said. “This allows for engineers and data scientists to go from idea to insight quickly, delivering meaningful impact. Modern technology solutions provide our data science teams with fingertip access to synchronized information and data sets, allowing rapid re-use of models to ultimately accelerate outcomes and delivery for our patients.”

Accelerating drug discovery and clinical trials

More quickly moving from ideas to insights has aided new drug development and the clinical trials used for testing new products. AstraZeneca’s ability to quickly spin up new analytics capabilities using AI Bench was put to the ultimate test in early 2020 as the global pandemic took hold. 

“When Covid first appeared, we knew we had to step up quickly with our pandemic response,” Dummann said. “We were able to establish validated environments within 24 hours to begin working on evaluating Covid. This would have taken weeks or even months without the work we had already done to build out AI Bench.”

AstraZeneca’s increased investment in the cloud and AI capabilities offers the potential for a similar impact on clinical trials.  “Clinical trials currently account for 60% of the cost and 70% of the time it takes to bring a potential new drug to market[1],” said Kaur. “AI and machine learning are helping us optimize that process and reduce the time it takes. The quicker we can complete clinical trials, the quicker we can get new medicines to patients.”

Four ways to improve data-driven business transformation 

Kaur and Dummann offered four pieces of advice to other IT leaders looking to get more value from their data transformation activities: 

Start small, think big, and scale fast. “You always need to have the big picture and vision in mind, but you don’t have to develop that picture right out of the gate,” Kaur said. Instead, focus on getting solutions out quickly, testing and improving them, and then scale them out across the company. The ability to scale also means promoting the re-use of data products where possible. “We want to maximize our investment in AI,” said Kaur. “We don’t want to keep reinventing the wheel, and we want our data scientists to be able to re-use AI assets across the enterprise.” AstraZeneca’s data scientists have launched more than 100 AI projects, and the number continues to grow.

Build internal expertise and understanding. Data-driven transformation is as much about people and process as it is about data and technology. To succeed, you need to get people to believe in the value of the transformation and show them a clear path to get there. “Attracting and retaining some of the best data scientists in the world has been critical to unlocking the value of data,” said Dummann. “So a big part for us is focusing on improving the experience of the data scientists. We’re keen to democratize data projects so that data scientists can get on with their daily tasks without reliance on IT. We don’t want to make them wait for weeks or days to get their work done.”

Modernize your approach to data and technology. “Data is an asset, and it needs to be treated as such,” said Dummann. It’s critical to ensure the integrity of the data for AI and machine learning models to work effectively. For the broader technology architecture, Dummann suggests moving away from best-of-breed point solutions. Instead, “invest in a few big, critical capabilities to really get the scale and speed you need.”

Don’t be afraid to fail. “There are multiple ways to solve a problem,” said Kaur. “Adjust as you go.”

Through its commitment to the cloud, data, AI, and machine learning, AstraZeneca is seeing its pace of innovation increase – and is eager to see where the journey leads. 

“Our data science community is moving faster than ever before, harnessing the power of data and AI to help discover new drugs, accelerate clinical studies and regulatory approvals, and maximize impact on patient lives,” says Dummann. “It’s an exciting time to be at AstraZeneca!” 

Learn more about ways to put your data to work on the most scalable, trusted, and secure cloud. 

[1] Clinical Development Success Rates, 2006-2015. BIO, BioMed tracker, Amplion, 2016

Artificial Intelligence

Technology is changing how healthcare and life sciences organizations operate.  With more information and analytics to find the “meaning” from the data resource, these organizations are making breakthroughs in therapies, discoveries, and patient outcomes.  This blog details the key points from a recent podcast with Richard Kramer, Chief Strategist Healthcare and Life Sciences at Informatica.  The podcast detailed best practices and strategies for building a data layer in these vertical industries.

First and foremost, organizations are making substantial investments in managing data as an asset. Executives are focused on ensuring that the business has trustworthy fit-for-purpose data, and employees can make it useful. In addition, master data management and governance are necessary to reduce data friction. The idea of master data governance is all about getting accurate and useful data in place so that it can be used across the enterprise.

Some examples provide excellent illustrations of these goals.  Anthem is investing in an enterprise data catalog. They understand it is very difficult to manage data as an asset if they don’t know where it is, where it’s going, or what happens in between. Transparency and trust are mandatory.   Eli Lilly is investing in a data marketplace, a central place for data assets to be discoverable and consumable across a large, complex enterprise.

The benefits of these initiatives are substantial. The goal is to use data to break down silos.  Data becomes the common “language” in a global company, and it has tremendous value by providing consistency and coordination across the business.  Data is also essential to smoothing the processes between different entities, like providers and insurance carriers. With trustworthy, fit-for-purpose data, federated business processes work more effectively.  The data platform is an essential resource for every healthcare and life sciences organization. It will provide the foundation for modern operations that run on facts, not guesswork.

Data Engineering