Speaking to TechGraph, Rahul Pandey, AVP of Data Analytics & Technology at SG Analytics said, “Technology has evolved to bring information and data closer; experiments being conducted in the form of “proof of concept” and run them.”
Read the complete interview:
How is SG Analytics utilizing its sectoral expertise and digitalization to provide data-centric research and contextual analytics services for its clients?
Rahul Pandey: At SG Analytics, we streamline our client’s data, analyze it and use technology to create relevant solutions. We provide data management and contextual analytics services to our clients, globally. Our global multi-disciplinary team cogently brings together the best-in-class expertise in analytical consulting, data engineering, data science, and visualization.
SG Analytics boasts a dynamic combination of domain expertise, data analytics, and contextual analytical skills, which helps organizations evolve through the entire journey of digital transformation starting from incubation to the full-fledged use of Artificial Intelligence (AI) and value-driven analytics.
With operational excellence as a way of life, we are deeply invested in enabling efficient, scalable realities for all our stakeholders – our employees, clients, vendors, and the society we live in.
We provide end-to-end support through consulting, development, and support to our clients thereby helping them identify the efficiency areas by re-engineering and implementing redesigned processes, thus making them leaner and more efficient. We augment our large-scale data management capabilities with our data analytics expertise to generate non-linear business impact for our clients. We analyze the collected data, using data science techniques and build sophisticated storyboards and automated insights that drive actions and monetizable business strategies for our clients.
For instance, our Machine Learning (ML)-enabled contextual data extraction engine has helped clients eliminate a significant percentage of manual effort, of reading through all the documents, by intelligently extracting relevant unstructured data for our research teams to generate meaningful insights and summarize the findings from the documents.
How are AI and ML helping in the revolution of business assessment towards financial inclusiveness?
Rahul Pandey: In this era of digital transformation especially within the financial sector where organizations are aggressively diversifying their offerings by leveraging new technologies to accelerate business operations and having customer-centric and user-friendly platforms. However, to streamline this, financial inclusivity is as important as digitally transforming the business.
For Example, Financial institutions have over time realized that it is not just analytical services but also AI/ML services that can drive data-led digital transformation with scalable and impactful results. Using AI/ML services, organizations are now able to monetize customer data by combining it with other sources such as social media and behavioral data to predict customer affinity towards different financial products.
SG Analytics has built robust ML models, using a combination of supervised and unsupervised techniques, to identify an optimized set of policy rules for recognizing fraudulent transactions and enhancing customer experience by reducing false positives, which otherwise was a huge manual effort with individual judgment as a factor to induce bias.
How is SG Analytics leveraging technology to enhance the customer experience?
Rahul Pandey: Since customers are day by day becoming tech-savvy and can optimize the use of technology to its full potential, organizations have been challenged to meet their expectations. Organizations have now realized that they are sitting on a goldmine of information in form of customer data and the first step towards enhancing customer experience is to extract value out of customer data and rigorously monitor and validate the data to understand the customer behavior, likes, dislikes and generate valuable insights to hyper personalize the customer experience and engagement.
The AI/ML-enabled tools and platforms are enabling organizations to unify the data acquisition and ingestion from varied sources and integrate it with the enterprise data to predict customer behavior and help companies’ devices better customer engagement models and strategies.
At SG Analytics, we develop strategy-oriented data science solutions and amalgamate them with proprietary ML-enabled data extraction processes, which enables our clients to take timely, yet informed and impactful decisions across business processes, allowing their customers, a seamless personalized experience. We use our proprietary ML models to identify target customers, predict their behaviors and personalize offerings. We leverage our expertise in scalable new-age visualization tools to help our clients triangulate the right signals and take the right actions for our customers by creating a command center that helps identify early warning indicators and triggers to predict customer behavior and personalize offerings.
How do you see technologies namely AI, ML, and Data Science, with regard to their relevance across the analytics network? What does the future look like?
Rahul Pandey: The evolution in the next frontier demands a strategic change from being “Model Centric” to “Data-Centric” where technologies always play an enabler role. AI, ML, and Data Science are together helping companies derive value from data and generate customer insights, thus helping them connect strongly with their target customers with the data at the core. SG Analytics has always kept the game up by being “start from data” and “be data-centric” in all of its offerings.
The new, evolving technologies make it easier to engage with customers and enhance their offerings as per requirements. Intelligence in applications is now being built to help companies deliver the speed, simplicity, and flexibility that customers expect through engaging business domain experts with AI/ML tools and technologies. Remember, humans, are smarter than any technology.
AI systems play a role in every part of life today for example recommendation systems of Netflix to Tesla’s autonomous driving enabled cars. However, Netflix has posted a loss of customers and Tesla still faces challenges to run cars on commercial/non-controlled roads. We, at SG Analytics, have always believed that algorithms and technologies need to align with business goals and objectives, not the reverse. We continuously apply the human-machine approach and strive to deliver value-driven growth to our clients. With businesses pivoting to online customer engagements, the combination of AI/ML and Data Science is offering a unique set of business solutions by providing accurate predictions, reduction in manual efforts, customer satisfaction, and increase in operational efficiency.
We as humans have achieved a lot but there is still a lot we can achieve. With that thought, I would say the future is when we will be able to control our need of depending on technology to do everything for us vs. defining what’s best done manually rather than needing a machine, a voice command, or a code for everything. For example – I would always like to tell my family/kid that I love them rather than Alexa, Google, or a future robot doing the job for me.
Technology has evolved to bring information and data closer; experiments are being conducted in the form of “proof of concept” and run them. The demand is now to operationalize, scale, and provide information and insights in real-time to take action. We all have understood that a model developed in a lab is not proven in the market. The focus of the future is the data-driven operationalization of digital products and solutions.
How is the response so far for your RPA Consulting Services?
Rahul Pandey: SG Analytics started its RPA services in 2020 and since then we have been continuously working with our clients combining the power of data analytics and insights, data sciences & AI with RPA. This makes rule-based engines powerful and the solutions we develop become valuable beyond automation.
For example, one of our focus areas is where there is heavy data and document transactions/movement from various sources, and we must be analytics savvy to use text analytics and NLP to automate such processes. We are excited about moving forward to grow in several geographies and domains.
