Jino Noel is a data science and technology leader with extensive experience in building data teams and practices across different organizations. His experience ranges from working in startups to large conglomerates across both Australia and the Philippines. At the time of this interview, he was the Chief Data Officer at Data Analytics Ventures, Inc. (DAVI). Currently, he is the Chief Data Officer at Angkas.
What are the key skills that a Chief Data Officer should possess nowadays?
A Chief Data Officer should have both data-related technical expertise as well as people leadership skills. Leading will always be part of the job, particularly for highly specialized technical people such as data engineers and data scientists. To be able to lead them properly, I believe it is better to be a technical person myself, so I can discuss technical matters fluently, which helps me gain their trust.
What data-related challenges have you faced as the Chief Data Officer of DAVI? How did you overcome these challenges?
Our data-related challenges are the same as any company. Being able to trust our data, cleaning up data from our sources, data latencies, and other related issues. DAVI overcame these by investing in people—hiring high-quality experts in our data engineering, data governance, and analytics teams to help us make sense of the data coming in—and building robust data pipelines that have increased the standard of quality of the data in our data lake.
How does DAVI make use of advancements in artificial intelligence (AI) and machine learning to help its clients understand their customers’ needs and buying patterns?
DAVI has recently started using machine learning to model our users’ propensity to buy certain products. This helps us create more accurate target audiences for our precision marketing campaigns. We are also moving forward with a recommendation engine project, with the goal of improving user engagement with our retail partners and with our promos and campaigns. On top of this, we are improving our machine learning operations expertise to make our model deployments repeatable and robust.
In the digital marketplace, data analytics acts as a guiding compass for app developers, enabling the creation of personalized, high-performing applications that align with user preferences. By leveraging data, developers can understand nuanced user behaviors and preferences, allowing them to tailor apps to meet specific user needs and aspirations.
Dive deeper into these discussions by reading Jino Noel’s full interview with The KPI Institute. Download the free digital copy of PERFORMANCE Magazine Issue No. 26, 2023 – Data Analytics on the TKI Marketplace. You can also purchase a physical copy via Amazon.
Alfonso Medela is the Chief Artificial Intelligence (AI) Officer at Legit.Health, where he oversees the use of advanced computer vision algorithms. A renowned expert in few-shot learning and medical imaging, his contributions include developing an algorithm capable of diagnosing over 232 skin conditions.
What are the key skills that a Chief AI Officer should possess in the context of your role at Legit.Health?
A Chief AI Officer at a medical organization like Legit.Health needs strong AI expertise, including extensive knowledge of machine and deep learning, and a profound understanding of medical data and healthcare to ensure precise algorithm development. Besides technical skills, strategic thinking and leadership are vital for guiding the AI team and aligning with company goals. Great communication and collaboration skills are also crucial for working effectively with different teams.
Can you describe your experience in developing and implementing AI strategies for computer vision applications, specifically in the context of diagnosing and treating skin pathologies? How have you leveraged AI to improve diagnosis accuracy and enable life-saving therapies?
Heading a team of specialists, we’ve developed advanced algorithms that accurately identify over 232 skin conditions and automate follow-ups for chronic skin conditions. Using deep learning techniques, our platform provides real-time diagnostic support to healthcare professionals, improving their accuracy and enabling early intervention. By collaborating with medical experts and continuously refining our algorithms, we are able to offer a powerful tool that empowers clinicians, transforming healthcare and improving patient outcomes.
What approaches or methodologies do you use to ensure the accuracy and reliability of computer vision algorithms in the context of skin pathology diagnosis? Can you share examples of how you have validated the performance of AI models and ensured their safety and effectiveness in real-world clinical settings?
To guarantee accuracy and reliability, our computer vision algorithms undergo a multi-stage validation process that encompasses retrospective and prospective clinical validations. Rigorous testing is performed on diverse, representative datasets, employing cross-validation to assess model performance. We collaborate closely with medical professionals, reviewing AI model outputs and gathering feedback to iteratively refine our algorithms. Furthermore, we conduct clinical trials and pilot studies to evaluate safety and efficacy. This ensures that our models adhere to real-world requirements and actively contribute to enhancing patient outcomes.
AI stands as one of the most transformative technologies of the modern era, revolutionizing the way people approach complex problems across various fields. From enhancing healthcare diagnostics to driving advancements in autonomous vehicles, AI’s potential is vast and continually expanding.
To explore the full spectrum of Alfonso Medela’s pioneering work in AI and to stay updated with the latest industry insights, read his full interview exclusively featured in the PERFORMANCE Magazine Issue No 26, 2023 – Data Analytics edition. Download your free copy now through the TKI Marketplace or purchase a printed copy from Amazon.
Harry Patria, the CEO of Patria & Co., is a data strategist and lecturer who founded a company that serves over 100 corporate clients, 200 analytical platforms, and 500 professionals. He is a Data Hackathon winner in the UK and graduated with distinction from his master’s degree to a PhD program with a fully-funded scholarship. Harry is a subject matter expert in several fields.
Editor’s Note: This piece was first published in the 22nd PERFORMANCE Magazine – Printed Edition. The KPI Institute’s Business Research Analyst Aida Manea discusses in this article how AI supports decision-making by eliminating biases and diminishing the number of human errors.
Through a variety of ways, artificial intelligence (AI) can help organizations enable and focus on better decision-making. AI could take over administrative roles and allow humans to prioritize more valuable things that require more time. The intelligent agent can take over manual tasks and enable process automation. AI can also plan decisions or predict results based on historical data.
That holds true even at the departmental level. Freeing managers from worries related to repetitive, administrative, and employee compliance tasks gives them more time for performance management activities. This is reflected in the use of AI as a behavioral assessment tool, data-driven processes where teams are coordinated through feedback, and more opportunities for meaningful human interaction.
AI in Performance Management
According to a study conducted by the University of Twente, there are two ways to implement AI in an organization. On a small scale, AI can assist a manager in improving small parts of the system, like inventory optimization. On a larger scale, AI could play a role in redesigning core processes at the organizational level.
One thing to pay attention to is knowing which type of implementation to choose. In an environment where human interaction and feedback are essential, it would not be the wisest choice to go for the second option as it could affect human connections.
The best-case scenario is to benefit from an assisting AI as it would help the manager make decisions while the assistant processes a vast amount of data. This will not only speed up the decision-making process but also guarantee the data veracity.
AI makes its mark on performance management systems through digitalization. Real-time feedback is important now more than ever due to the changes within performance management. The traditional yearly review is now being replaced by more frequent and informal check-ins as this would enable the shift from talking about people to talking with people. The 360-degree feedback practice focuses on asking colleagues for feedback on an employee’s performance.
Another strong point of AI is that it eliminates the biases toward individuals by assessing patterns and historical data with no opinion that might dilute decisions. While the line managers or HR may have their personal opinions about employees coincide with their responsibilities, AI supports decision-making by eliminating biases and diminishing the number of human errors.
AI for HR
At the HR department, the implementation of an AI system will not only process the data faster but will also deliver robust data collection, frequent fact-based performance, and improvement discussions. HR managers are responsible for their teams’ attitudes and behavior so that they can truly contribute to organizational goals.
In 2018, IBM realized the need for AI in mitigating biases and improving departmental performance. This is why IBM Smarter Workforce Institute wrote the paper “The role of AI in mitigating bias to enhance diversity and inclusion,” in which practical recommendations are offered for organizations that are looking to adopt AI in their HR daily activities.
Efficient and effective recruitment – A recruiter’s main challenges are prioritizing all the roles they are responsible for and finding a way to differentiate among candidates that applied for the same position. Deploying AI determines how long a job requisition will take to fill based on past data so that recruiters can prioritize the roles available.
Moreover, AI can predict future performance by determining the match between a resume and the job requisition and filtering candidates. The challenge in IBM regarding effective recruitment is to help HR managers surface the top candidates for the open positions and prioritize the most important requisitions. Their solution is IBM Watson Recruitment, an AI system that assesses information about the job market and past experiences of potential candidates in order to predict the necessary time to fill in positions and spot the most suitable candidates.
The huge advantage for recruiters is that they can focus on building and nurturing relationships with applicants. At the same time, AI collects the demanded skills from job requisitions and generates a score against skills mentioned in resumes. Finally, IWR watches over the hiring decisions to make sure they are free from bias and turns the candidate and recruiter’s experiences into better ones.
Enhancing motivation – At IBM, the individual needs of employees are essential, and managers get alerts about it. For example, the manager is alerted when there is an employee with years of experience in the company, has skills, and is ready for a promotion. The same applies to the case of employees with a higher propensity to leave or when employees from a specific department are at risk of missing their targets.
Through this alarm signal, managers are able to make decisions over the organization’s talent management approach. Another AI implication is the chatter analysis used to capture the top three internal issues from social media sources. Leaders can receive personalized recommendations to increase the team’s engagement. Other benefits brought by AI can be smarter compensation planning and career development.
The drawbacks of AI systems can be avoided by making sure the data is never used as a sole determinator in decisions.AI initiatives can barely break organizational barriers, based on a survey conducted by Harvard Business Review in which only 8% of firms engage in core practices that support the adoption of Artificial Intelligence. The shift towards AI should start by aligning the organizational culture and the internal operating ways to support digital transformation. Here are the three main actions to scale up AI:
Replace siloed work with cross-functional teams collaboration. The mix of perspectives increases the impact AI has over the processes as it ensures that projects address broad organizational concerns and not just isolated ones. Moreover, if end users are required to test what development teams work on, the chances of adoption increase.
Abandon the top-down approach. Integrating AI into processes will increase the trust of employees in algorithms. They are the ones who will ultimately make a decision based on the algorithm result and their experience. Once they feel empowered to make decisions without having to consult a higher-up, they will get a taste of what AI can offer: freedom of action.
Embrace an agile, experimental, and adaptable mindset. The idea of having an idea baked before it is deployed must be replaced with a test and learn vision. This reduces the fear of failure and allows companies to correct minor mistakes before they become costly ones by receiving early feedback from users.
AI’s ability to promote automated processes, analyze data, predicts trends, and even build frameworks helps the organization in its strategy and business planning. In order to maximize the product and effects of AI, it is essential to establish a strong strategy mindset.
The KPI Institute offers a program that would help you design an organization’s strategy and plan your business using a strategic framework. Enroll now in the Certified Strategy and Business Planning Professional Live Online course! For more details, visit The KPI Institute’s website HERE.
“The world’s most valuable resource is no longer oil, but data.”
That statement from The Economist in 2017 cannot be overstated. Businesses in all shapes and sizes must realize that adapting to an already data-driven world is the only way to survive, connect, and thrive.
Artificial Intelligence (AI) was introduced in the 1950s by a computer researcher named John McCarthy. He defined AI as “the science and engineering of making intelligent machines.”
Nowadays, innovation pioneers like Microsoft, Google, and IBM have made strides in AI advancement to back cloud analytics, client engagement, and more. AI has become a program outlined to complete tasks that would regularly require human capabilities or input. AI is considered an innovation that takes after or mirrors human insights and actions, including speech, reviewing pictures, or making a conversation. To a great extent, AI can do those things by recognizing designs inside the information and reacting based on pre-defined rationale.
On the other hand, big data is an extensive, fast, and diverse information resource that requires advanced forms of processing to improve decision making, knowledge generation, and process optimization.
Big data describes sets of information created in different formats and through different sources, such as software applications, IoT sensors, customer feedback surveys, videos, and images..
Big datasets are developed by collecting large amounts of information from real-time data streams, established databases, or legacy datasets. As the environment constantly changes and grows, we need powerful software to protect, classify, and explain information for both short-term and long-term use.
Organizations often use a combination of cloud-based applications and data warehousing tools to develop analytic architectures that collect, organize, and visualize data. AI-powered tools are central to tailoring many of these moving parts to consistent insights that support decision-making.
Linking Up Big Data and AI for Business
Implementing big data with AI has already been vital for many businesses that aim to have a competitive edge. It doesn’t really matter whether it is a new company or an established leader in the market. They use data-driven strategies to turn information into perceptible value. It is common to find big data in almost every industry, from IT and banking to agriculture and healthcare.
Business experts acknowledge that big data and AI can create new ideas for growth and expansion. There is even a possibility that a new type of business will become popular soon: data analysis and aggregation companies for particular industries. The purpose of those organizations is to process enormous flows of data and generate insights. Before this happens, businesses should empower their big data capabilities intensively. In the past, estimations were made based on the retroactive point of view. Leveraging real-time analysis, big data can empower predictions and allow strategists to test assumptions and theories faster.
Data and AI are typically applied to analytics and automation, helping businesses transform their operations in the process.
Analytics tools like Microsoft, Azure, and Synapse help organizations predict or identify trends that inform decision-making around product development, service delivery, workflows, and more. Additionally, your data will be organized into dashboard visualizations, reports, charts, and graphs for readability.
Big data and AI in Health
The global market for AI-driven health care is expected to register a CAGR of 40 percent through 2021 and toup from USD 600 million in 2014. Further advances in AI and big data provide developing countries with opportunities to solve existing challenges in the health care access of their populations. AI combined with robotics and IoMT could also help developing countries address healthcare problems and meet SDG 3 on good health and well-being. AI can be deployed in health training, keeping well, early disease detection, diagnosis, decision-making, treatment, end-of-life care, and health research. For instance, AI can outperform radiologists in cancer screening, particularly in patients with lung cancer. Results suggest that the use of AI can cut false positives by 11 percent.
Big data and AI in Agriculture
Today’s global population of 7.6 billion is expected to rise to 9.8 billion by 2050, with half of the world’s population growth concentrated in nine countries, such as India, Nigeria, the Democratic Republic of the Congo, Pakistan, Ethiopia, the United Republic of Tanzania, the United States of America, Uganda, and Indonesia.
The growing demand for food will put massive pressure on the use of water and soil. All of this will be exacerbated by climate change and global warming.
Big data and AI in Education
AI can reshape high-quality education and learning through precisely targeted and individually customized human capital investments. Incorporating AI into online courses enhances access to affordable education and improves learning and employment in emerging markets. Also, AI technologies can ensure equitable and inclusive access to education, providing marginalized people and communities, such as persons with disabilities, refugees, and those out of school or living in isolated communities, with access to appropriate learning opportunities.
Expected Economic Gains from AI Worldwide
AI could contribute up to USD 15.7 trillion to the global economy in 2030, more than the current GDP of China and India combined. Of this, USD 6.6 trillion will be derived from increased productivity and USD 9.1 trillion from the knock-on effects of consumption. The total projected impact for Africa and Asia-Pacific markets would be USD 1.2 trillion. For comparison, the combined 2019 GDP for all countries in sub-Saharan Africa was USD 1.8 trillion. Thus, the successful deployment of AI and big data would open up a world of opportunities for developing countries.
The big data market is expected to grow tremendously over the projected years. One of the important reasons is the rapid increase in the amount of structured and unstructured data. Factors include the increasing penetration of technology and the proliferation of smartphones in all areas of life. This leads to a large amount of data.
Other industries such as healthcare, utilities, and banking make extensive use of online platforms to provide enhanced services to their customers.
Intelligent use of big data in day-to-day operations enables you to make data-driven decisions and respond quickly to market trends that have a direct impact on business performance.
If you would like to learn more about the best practices for analyzing data, sign up for The KPI Institute’s Data Analysis Certification.
Jino Noel is a data science and technology leader with extensive experience in building data teams and practices across different organizations. His experience ranges from working in startups to large conglomerates across both Australia and the Philippines. At the time of this interview, he was the Chief Data Officer at Data Analytics Ventures, Inc. (DAVI). Currently, he is the Chief Data Officer at Angkas.
Alfonso Medela
