Business Lab

Modernizing IT Helps Enterprises Do More with Less

The World Bank Group has a massive mission to “help developing countries escape poverty and share prosperity,” says Vijay Yellai, program manager for enterprise resource planning transformation at the World Bank Group. For example, it provides an wide array of financial products and technical know-how in a complex and ever-changing global setting. Therefore, for an institution like the World Bank Group, which provides funding and resources to countries with low bandwidth and infrastructure, IT modernization is no small feat. “So in an ever-changing environment–complexity, risk, and security threats with a global workforce–the World Bank is under pressure to do more with less,” says Yellai. He explains that the challenge is to increase real-time business, as well as quickly respond to changing needs of customers and employees. But also, Yellai continues, “security, risk, and data are key elements. Not to mention the continuous need for business intelligence and quick decision making.” The ultimate goal is to “capitalize on technology to meet our mission and goals.” And although data collection and processing is key to IT systems, an agile and adaptive approach is needed to keep operational and financial systems current in each business. “Data is very fundamental to that. And data and research help us understand how we are addressing the needs, helps us set up priorities, helps us share knowledge, and helps us measure progress.” Yellai says. With a modernized IT system, Yellai says, there are a number of innovations that become possible. Predictive analytics, natural language processing, blockchain, and process automation are a few of the technologies emerging to allow for quicker decision-making and efficiency. “Anything we can do to reduce the work we need to do in technology, but let the technology do more for us, so we can focus our time on the strategic priorities, will be the most exciting thing for us,” says Yellai. This episode of Business Lab is produced in association with Infosys Cobalt.

Feeding the World by AI, Machine Learning, and the Cloud

Although the world population has continued to steadily increase, farming practices have largely remained the same. Amid this growth, climate change poses great challenges to the agricultural industry and its capacity to feed the world sustainably. According to the World Bank, 70% of the world’s fresh water is used in agriculture and droughts and heat waves continue to threaten crops. And that is where the challenge arises to feed the world while mitigating the environmental effects of agricultural practices. The answer to this challenge, according to Thomas Jung, head of IT Research and Development at Syngenta, is regenerative agriculture. Just as important as clean water and clean air, soil is the critical foundation of agriculture. The crux of regenerative agriculture is to grow more food with less environmental impact by enhancing the health of soil. “So not much has changed, but we need to feed more and more people,” he continues “How do we address this challenge of feeding the world in a sustainable fashion without exploiting our soils more?” Regenerative agriculture efforts look to find solutions to help plants stay healthy, find solutions to make crops more resistant to climate change-induced droughts and heatwaves, and use less water in farming. Therefore, what’s necessary is, “moving beyond the traditional agriculture and the way we've been doing this for probably 100 years or more. I mean, this is a leap,” says Jung. “This is an agricultural revolution that is ongoing, and artificial intelligence will play the decisive role in it.” Although farmers have invaluable knowledge of their own crops and fields, says Jung, AI and machine learning tools can be instrumental in cataloging greater detail, refining algorithms, and creating recommendations for solutions. As more data is collected and algorithms continue to improve to create new innovations, we’ll be even closer to understanding our planetary ecosystem, says Jung. Breakthroughs like soil regeneration, really living with sustainable agriculture across disciplines are achievable within the next three years. “There's a lot of advocacy for open source, for democratized data, for fair data, and we need to bring that to the industry,” says Jung. “This can't just be an NGO or a volunteering thing, that this is how I believe our industry needs to work. So we really want to share, we want to lead by example, we want to nurture the community, and through that, win altogether.”This episode of Business Lab is produced in association with Infosys Cobalt.

AI and Data Fuel Innovation in Clinical Trials and Beyond

The last five years have seen large innovations throughout drug development and clinical trial life cycles—from finding a target and designing the trial, to getting a drug approved and launching the drug itself. The recent use of mRNA vaccines to combat covid-19 is just one of many advances in biotech and drug development.Whether in preclinical stages or in the commercialization of a drug, AI-enabled drug development is now used by an estimated 400 companies and has reached a $50 billion market, placing AI more firmly in the life sciences mainstream.“Now, if you look at the parallel movements that are happening in technology, everyone’s in consensus that the utility of what AI can do in drug development is becoming more evident,” says senior vice president at Medidata AI, Arnaub Chatterjee.The pandemic has shown how critical and fraught the race can be to provide new treatments to patients, positioning the pharmaceutical industry at an inflection point, says Chatterjee.And that’s because drug development usually takes years. Evidence generation is the industry-standard process of collecting and analyzing data to demonstrate a drug’s safety and efficacy to stakeholders, including regulators, providers, and patients.The challenge, says Chatterjee, becomes, “How do we keep the rigor of the clinical trial and tell the entire story, and then how do we bring in the real-world data to kind of complete that picture?” To build more effective treatments faster, drug and vaccine companies are using data iteratively to improve understanding of diseases that can be used for future drug design. Bridging gaps between clinical trial and real-world data creates longitudinal records. AI models and analytics can then be used to enable feedback loops that are key for ensuring safety, efficacy, and value, says Chatterjee.“We want to create safe and expeditious access to therapy,” says Chatterjee. “So we really have to meet this moment with innovation. With all the new advances happening in drug development, there’s no reason why technology and data can’t be there.” This episode of Business Lab is produced in association with Medidata. Related resources●     Integrated evidence, Medidata●     Why artificial intelligence could speed drug discovery, Morgan Stanley

Building a Culture of Innovation in Research and Development

Memory and storage solutions for technology are built into our everyday life, from mobile applications, cars, health-care systems, and more. To meet that need and help propel innovation, Micron Technology said it would invest $150 billion into research and development to build factories for its semiconductor memory chips. This investment looks to expand not only the reach of memory chips but also to innovate new solutions to common problems, says Naga Chandrasekaran, senior vice president of technology development at Micron.“The day we stop innovating, not just in memory, but as a human race, the day we stop innovating, we stop progressing and that's not where we want to be. We want to continue to drive innovation,” says Chandrasekaran.With each iteration of new technology, from phones to cars, consumers are looking for improved performance, lower latency, more storage, and lower costs. Meeting these expectations means finding solutions at an atomic scale and making micro changes to push the boundaries of what’s possible.Since its inception 44 years ago, Micron has developed over 50,000 patents. While Chandrasekaran emphasizes that patents are just one part of fostering innovation, they do represent the strides toward greater innovations and the company culture that Micron has worked to establish.While having strong team members is important, the culture that a company fosters is just as crucial when it comes to seeing positive results. Chandrasekaran says that building successful teams that can create so many patents and build technologies with an eye on innovation requires a certain company mindset that doesn’t shy away from mistakes or failure.“So we are taking risks on a regular basis, but the key is to make sure we can fail fast and not see those failures as a mistake, but actually learn from them.” Chandrasekaran continues, “That's why failing fast is important, but not being afraid of failing.”In addition to taking risks, diversity has become a significant contributor to driving new solutions. Between 2017 and 2021, the number of women listed as inventors on Micron patent applications quadrupled. Chandrasekaran says that for any sustained success and innovation to be possible, diversity is necessary.“No matter what we say, we are all limited in our thought process in how we approach problems, in how we approach solutions. And even with a growth mindset, we have limitations, because we are who we are based on the experiences and the exposures that we have gained,” says Chandrasekaran. “So diversity brings in not just from a gender diversity or ethnic diversity, but if we look at diversity from a broader scale, it's diversity of thought process.”  This episode of Business Lab is produced in association with Micron Technology.

Maximize Data Outcomes by Investing in People and Systems

In any enterprise, digital transformation is not only a technology transformation but enables business transformation itself, driving new products, solutions and innovations. Having an efficient data strategy is critical to any successful digital transformation but requires careful investment into both people and systems. “To achieve that goal, availability of good data, of the right data, and availability of that to the right people and systems is very, very critical. So that forms the data strategy for any enterprise today,” says chief architect for data and AI services at Kyndryl, Sundar Shanmugam. Getting the most out of digital transformation investments means evaluating and optimizing agility throughout an enterprise to drive actionable insights, says Shanmugam. A strong data governance framework also goes a long way in keeping data high-quality. Often data governance primarily serves regulatory requirements. But truly effective data governance is holistic, he adds. Data usage, regulations, and the data itself are constantly evolving within an enterprise, effectively making data governance a continuous process. Although tech teams are often dictating how data should be managed and used, Shanmugam says everyone across the enterprise, including leaders and decision makers, should be data literate. “End of the day, the people are the ones who design the systems and who develop the systems that consume the data, so the right investment on literacy is paramount in that aspect,” says Shanmugam. Another key component to digital transformation lies in maximizing investments across business units. The combination of software development and operations to form devOps, AI and machine learning to form MLOps, and finance and operations to form finOps all fall under the broader umbrella of XOps that categorize these merging of IT disciplines with business operations. XOps all come together to deliver value in the most efficient way with each combination focused on maximizing automation, reusability, and agility, says Shanmugam. “As we say, necessity is the mother of innovation, so that necessity can continuously change,” says Shanmugam. “At the core of that, if we keep that data proper [condition], then it can expand the horizons, not just internally, but even for the other external requirements and use cases.” This episode of Business Lab is produced in association with Kyndryl. Related readingTop Trends in Data and Analytics for 2021, Gartner, February 16, 2021