Data-Driven Demand: The Rising Need for Data Scientists Across Industries
CXOToday has engaged in an exclusive interview withManish Menon, office managing principal, ZS.
- How has the ever-growing volume and complexity of data impacted the demand for data scientists across various industries?
Data science has become a crucial and highly sought-after skill in today’s job market, with companies of all sizes relying heavily on data for their operations. Initially, data was just an afterthought for many businesses, but it has now evolved into essential information that requires analysis, creativity, and expertise to transform into actionable insights. Consequently, the demand for data scientists has significantly increased. These professionals play a critical role in helping clients make informed decisions by identifying patterns and connections within data. They are a blend of statisticians, analysts, and programmers with deep subject matter expertise, capable of gathering and analyzing vast amounts of data to make industry-specific predictions. Their work involves deciphering patterns within data, uncovering hidden insights, testing novel hypotheses, and drawing inferential conclusions.
In the healthcare sector, data scientists enable early detection of risks for medical conditions such as cancer or heart disease by analyzing data from sources like Electronic Medical Records (EMR), Electronic Health Records (EHR), Electronic Patient Records (EPR), transactional and biometric data, and genetic information. Insights derived from these sources help tailor medical treatments to specific patient subgroups, moving away from a one-size-fits-all approach and focusing on genetic, epigenomic, and proteomic differences. This further aids early intervention with preventive measures, the development of more effective personalized medicines, and a reduction in adverse medication responses, all contributing to a more proactive, efficient, and patient-centered future in medicine.
- Can you elaborate on the expertise of ZS’s data science team? What are the advanced data management solutions, cross-functional technologies and analytics tools that it leverages?
We have over 3,600 professionals in our analytics and AI division. These employees handle both internal projects and client-billable tasks. While they may operate independently at times, they are primarily integrated into larger teams, particularly for client engagements. By leveraging the expertise of each team member, ZS ensures the successful delivery of high-quality solutions tailored to our clients’ needs.
Our teams utilize a wide array of industry-standard tools, applications, and frameworks, continually updated by our research-based AI labs to stay on the cutting edge. Some of these include Power BI, Tableau, Python, R, image recognition, fraud detection, speech recognition, augmented reality (AR), random forest, NumPy, XGBoost, and Pandas. We have also developed innovative experiments and solutions using GPT and LLMs. Generative AI (GenAI) is another focal point of research within our AI team. Additionally, we are actively exploring the potential of AR and virtual reality (VR) to create immersive experiences, a promising area of experimentation for us.
We apply our data science capabilities in the following areas:
- Providing clients with advanced data management solutions that leverage AI, cross-functional technologies, and analytics tools to mitigate risk and improve essential processes.
- Developing and scaling data fabric solutions using cutting-edge semantic graph and search technologies, enriched by ZS-curated domain ontologies.
- Maintaining a library of smart algorithms, infused with our pharma expertise, to enhance data service efficiencies and help clients build in-house capabilities for scaling data management functions.
- Creating enterprise data lakes, migrating data warehouses, providing skills-based training, and rethinking organizational designs.
- Planning optimal channels to increase sales and engagement, as well as guiding engagement strategies in near real-time.
- Offering comprehensive and customized solutions through our ZAIDYN Data & Analytics platform, which integrates primary, secondary, and social data, enabling clients to transition from ad hoc insights to a programmatic approach.
- Considering the evolving data landscape, how does ZS invest in upskilling its workforce to stay ahead of the curve in analytics capabilities? Can you share some specific examples?
In the fast-paced consulting and technology sectors, skills can quickly become outdated. In this dynamic environment, keeping talent relevant through continuous upskilling in areas like advanced analytics and GenAI is imperative. Ensuring the workforce stays current with technological advancements is vital for employee satisfaction and retention. With about 9,000 client-facing employees in India, ZS understands the urgency of timely upskilling. The company especially stands out with respect to GenAI, leading the way in adoption and strategic planning compared to other industry players. This is particularly crucial in sectors like pharma and healthcare, where agility is paramount.
ZS has made significant investments in learning technology infrastructure, partnering with platforms such as Skillsoft and Degreed. These collaborations provide numerous learning paths for professional development, with a strong emphasis on GenAI-related programs. Through initiatives like Rapid Skilling, ZS equips all employees with essential technical skills. Under this, we have identified skills relevant for both the present and future, defining learning pathways for each.
- As AI plays an increasingly prominent role in healthcare, ethical considerations become paramount. How can organizations ensure adherence to ethical guidelines while integrating AI technologies into their systems?
Data in a healthcare setting is a critical and sensitive asset. Effective analysis of this data can significantly improve patient outcomes, but it also involves inherent risks in handling and processing. Integrating AI into healthcare systems necessitates meticulous attention to ethical guidelines to ensure patient safety, privacy, and trust. This is also crucial for eliminating disparities in outcomes among different patient groups.
Governments and policymakers worldwide have established guardrails regarding this. It is essential to view data from a flow perspective, focusing on three key components: the data originator, the data processor, and the data consumer. Laws and regulations must ensure that users provide proper consent for data usage and understand the extent of its use. Additionally, the originator should be informed about the purpose of data usage. Many countries and regions, such as China and the EU, mandate that data be stored within their sovereign borders. These factors must be considered when handling healthcare data.
As healthcare systems and patient data evolve, regular updates and ongoing monitoring of any model’s performance in real-world situations are essential to maintain accuracy and reliability. It is critical to ensure compliance with local and federal regulatory standards. Frameworks such as GDPR, HIPAA, and HITRUST compliant platforms, tokenization technologies, and advanced features like AWS clean rooms for compliant data sharing must be considered.
- What are some of the key trends that you foresee for data science and AI in 2024?
2024 is shaping up to be a year when data is being used like never before. With GenAI implementation organizations are expected to experience productivity improvements. In healthcare, GenAI will enhance patient engagement and treatment adherence by simplifying complex information for better understanding. It will address the shortage of accessible information among multiple stakeholders, including patients, caregivers, and prescribers by ensuring the availability of relevant information at the right time. We expect natural language processing (NLP) to reform patient care by analyzing unstructured data from a variety of sources, thereby boosting clinical decision-making and patient outcomes.
In the near future, Retrieval-Augmented Generation (RAG) is expected to mature, transforming AI and NLP by integrating retrieval and generative models. RAG addresses limitations in LLMs like GPT by delivering contextually appropriate responses, enabling enterprise use cases, industry-specific applications, and advanced search apps. Multimodality will also be a priority this year, enhancing chatbot adaptability and engagement, allowing them to see, listen, and respond effectively.
We also foresee companies investing in platforms, processes, methodologies, feature stores, machine learning operations (MLOps) systems, and other tools to enhance productivity and deployment efficiency. Automated machine learning (Auto-ML) platforms are gaining popularity, automating key aspects of the data science lifecycle. These platforms manage tasks such as data sourcing, feature engineering, conducting machine learning experiments, evaluating and selecting optimal models, and deploying them into production environments.
Finally, AI will continue to be critical in cybersecurity due to its ability to predict, detect, and respond to cyber threats swiftly and accurately. AI-driven solutions will help organizations stay ahead of emerging threats, minimizing potential damage by continuously learning from attacks.
