Faisal Alam, an emerging technology leader is a visionary leader steering innovation in leading technologies such as IoT, AI, and advanced cloud data/data fabric solutions. As a team leader, he believes in deriving the best out of his team members, which ultimately culminates in differentiated client service and social goodwill as well. Analytics Insight has engaged in an exclusive interview with Faisal Alam, EY Americas Consulting’s emerging technology leader.
1. Kindly brief us about the company, its specialization, and the services that your company offers.
At EY, our purpose is to build a better working world. The insights and services we provide help to create long-term value for clients, people, and society, and build trust in the capital markets. We offer services in consulting, assurance, tax, and transactions. Within consulting, we are focused on helping our clients realize their business transformation goals through the intelligent application of people, technology, and innovation. By placing humans at the center, leveraging technology at speed, and enabling innovation at scale, our clients are transforming to realize long-term value for people, businesses, and society as a whole.
We offer specialized consulting services in the following areas:
- Data, analytics, and AI
- Strategy
- Technology transformation
- Climate change and sustainability
- Supply chain and operations
- Customer experience
- Finance transformation
- Cyber, risk, GRC, and business resiliency
- Organization and workforce transformation
2. How is IoT/Big Data/AI/Robotics evolving today in the industry as a whole? What are the most important trends that you see emerging across the globe?
The rise of connected vehicles, products, and people will continue to fuel the explosion of data over the coming years. The need to process this data with low latency at or near the point of capture will drive the need for robust asymmetric analytics, workflow, and autonomous decision engines that can operate without the immediate need for backend server-based compute resources. This decentralized processing paradigm will drive the increased ubiquity of computing infrastructure. New technologies like the metaverse and Web 3.0 are predicated upon this decentralized processing paradigm.
- The other important trend is data privacy. The unrelenting drumbeat of data breaches has given rise to several new control regimes including GDPR in the EU, CCPA in California, and the phasing out of 3rd party cookies. In areas such as healthcare, existing data sets will be increasingly used as templates for generating synthetic data for testing and training machine learning models to alleviate privacy concerns. Synthetic data will also be used for scenario modeling to simulate yet-to-be encountered conditions. In the area of advertising, AI and Natural Language Processing (NLP) will be used to support contextual marketing. Contextual marketing exposes users to ads based on the content they view rather than the data contained in a cookie – it’s one possible substitute for the use of third-party cookies. AI and NLP will be used to analyze videos to identify entities, languages, etc. to retain the ability to serve targeted ads without the use of third-party cookies.
3. How is Machine Learning (ML) shaping the IT/Big Data/Robotics industry today? How is it changing the role of CIOs and Leaders?
Up until the mid-2010s, many CIOs were focused on IT operations and managing on-premise infrastructure. This “keeping the lights on” imperative led to a focus on the technology fiefdom and a disconnect with business stakeholders. However, many technologies such as cloud, Big Data, serverless architectures, and the like, don’t require the level of support from IT that past systems required. This is driving a shift in focus from operations to innovation and partnering with the business. So, the CIO is now focusing on establishing IT as a trusted advisor to the business. In addition, given the demands of ML and Big Data, the CIO must also evolve into an ecosystem integrator – unifying data and driving collaboration from disparate functions of the enterprise as well as from outside the enterprise.
4. How are disruptive technologies like IoT/Big Data analytics/AI/Machine Learning/Cloud Computing impacting today’s innovation?
There are several ways that these technologies can be used to push the boundaries of innovation. For example, one of our HVAC manufacturer clients is gathering telemetry information from connected HVAC units to not only predict failure and proactively schedule service, but also to better inform future product enhancements and new designs. In another example, EY is working with a payroll processor to determine how it can monetize its valuable data through sharing the data itself or sharing insights from this data with other parties that are willing to pay for it. These are both great examples of how companies are leveraging disruptive technologies to drive innovation that contributes to the net new top and/or bottom-line growth.
In addition, as these technologies get cheaper, more powerful, and more ubiquitous, the speed of innovation can be accelerated, and the sources of innovation can be democratized. For example, the ability to conduct trials of new products, such as autonomous vehicles, in simulated virtual environments can accelerate the machine learning process. This ability to use at-scale processing power in simulated environments also lowers the cost of building a real-world testing environment. This, in turn, can democratize innovation by facilitating individual tinkerers and inventors to bring out the ideas and products to market that benefit society.
5. How C-suite executives can leverage data to deliver business value to their organizations?
The number of ways that data can be leveraged by C-Suite executives to deliver business value is almost innumerable. In one instance, EY worked with a sports apparel manufacturer to develop a solution using a knowledge graph to model and link customers, products, stores, and other data. This enabled their organization to visually interact with its data in an intuitive way to see which customers had affinities to which products. This data was then run through a machine-learning-based recommender system to make additional product recommendations. These recommendations were then fed into its marketing systems to be served up to its customers via multiple channels. The sales lifted from these types of solutions can range from 25 – 35%. Cases like this can enhance both the top-line and bottom-line performance of most organizations.
6. What are some of the challenges faced by the industry today?
There are several challenges, on the data and AI sides as well, in today’s analytics industry. On the data side, the primary challenge is data quality. Data quality problems can range from incomplete to inaccurate to untimely to insufficient to missing data. Imagine trying to execute a targeted marketing campaign when you can’t determine whether the customer entities in your disparate systems (CRM, ERP, POS, etc.,) are referring to the same or different customers. And also, when a machine learning model is trained with insufficient or inaccurate data, the insights derived from these models will be flawed. Many of these data challenges can be addressed through techniques such as the implementation of modern AI-based MDM systems, better validation rules in the source systems at the point of data capture, and the use of synthetic data to increase data volumes where needed.
On the AI side, several challenges need to be addressed, which range from technical to ethical. On the technical end of the spectrum, challenges can include selecting ML models that are not fit for purpose – over or underfitting models; and scaling models into production. Any of these issues will produce unreliable outputs. On the ethical side, AI and ML models are fed by data. If that data includes the biases of the humans that generated it, the insights generated by the AI models will also be biased. This can open up legal exposure to organizations relying on AI to make important decisions (e.g. credit, hiring, housing, etc.). Looking further into the future, societies will also have to grapple with the ethics around things like autonomous warfare. Should machines be allowed to take human life? Does a human need to be in the loop? If so, does that defeat the speed advantage of using AI for these purposes in the first place? Like most challenges faced by industries, the technical challenges are usually easier to solve than the human challenges.
The post Exclusive Interview with Faisal Alam, EY Americas Consulting Emerging Technology Leader appeared first on .
Faisal Alam, an emerging technology leader is a visionary leader steering innovation in leading technologies such as IoT, AI, and advanced cloud data/data fabric solutions. As a team leader, he believes in deriving the best out of his team members, which ultimately culminates in differentiated client service and social goodwill as well. Analytics Insight has engaged in an exclusive interview with Faisal Alam, EY Americas Consulting’s emerging technology leader.
1. Kindly brief us about the company, its specialization, and the services that your company offers.
At EY, our purpose is to build a better working world. The insights and services we provide help to create long-term value for clients, people, and society, and build trust in the capital markets. We offer services in consulting, assurance, tax, and transactions. Within consulting, we are focused on helping our clients realize their business transformation goals through the intelligent application of people, technology, and innovation. By placing humans at the center, leveraging technology at speed, and enabling innovation at scale, our clients are transforming to realize long-term value for people, businesses, and society as a whole.
We offer specialized consulting services in the following areas:
- Data, analytics, and AI
- Strategy
- Technology transformation
- Climate change and sustainability
- Supply chain and operations
- Customer experience
- Finance transformation
- Cyber, risk, GRC, and business resiliency
- Organization and workforce transformation
2. How is IoT/Big Data/AI/Robotics evolving today in the industry as a whole? What are the most important trends that you see emerging across the globe?
The rise of connected vehicles, products, and people will continue to fuel the explosion of data over the coming years. The need to process this data with low latency at or near the point of capture will drive the need for robust asymmetric analytics, workflow, and autonomous decision engines that can operate without the immediate need for backend server-based compute resources. This decentralized processing paradigm will drive the increased ubiquity of computing infrastructure. New technologies like the metaverse and Web 3.0 are predicated upon this decentralized processing paradigm.
- The other important trend is data privacy. The unrelenting drumbeat of data breaches has given rise to several new control regimes including GDPR in the EU, CCPA in California, and the phasing out of 3rd party cookies. In areas such as healthcare, existing data sets will be increasingly used as templates for generating synthetic data for testing and training machine learning models to alleviate privacy concerns. Synthetic data will also be used for scenario modeling to simulate yet-to-be encountered conditions. In the area of advertising, AI and Natural Language Processing (NLP) will be used to support contextual marketing. Contextual marketing exposes users to ads based on the content they view rather than the data contained in a cookie – it’s one possible substitute for the use of third-party cookies. AI and NLP will be used to analyze videos to identify entities, languages, etc. to retain the ability to serve targeted ads without the use of third-party cookies.
3. How is Machine Learning (ML) shaping the IT/Big Data/Robotics industry today? How is it changing the role of CIOs and Leaders?
Up until the mid-2010s, many CIOs were focused on IT operations and managing on-premise infrastructure. This “keeping the lights on” imperative led to a focus on the technology fiefdom and a disconnect with business stakeholders. However, many technologies such as cloud, Big Data, serverless architectures, and the like, don’t require the level of support from IT that past systems required. This is driving a shift in focus from operations to innovation and partnering with the business. So, the CIO is now focusing on establishing IT as a trusted advisor to the business. In addition, given the demands of ML and Big Data, the CIO must also evolve into an ecosystem integrator – unifying data and driving collaboration from disparate functions of the enterprise as well as from outside the enterprise.
4. How are disruptive technologies like IoT/Big Data analytics/AI/Machine Learning/Cloud Computing impacting today’s innovation?
There are several ways that these technologies can be used to push the boundaries of innovation. For example, one of our HVAC manufacturer clients is gathering telemetry information from connected HVAC units to not only predict failure and proactively schedule service, but also to better inform future product enhancements and new designs. In another example, EY is working with a payroll processor to determine how it can monetize its valuable data through sharing the data itself or sharing insights from this data with other parties that are willing to pay for it. These are both great examples of how companies are leveraging disruptive technologies to drive innovation that contributes to the net new top and/or bottom-line growth.
In addition, as these technologies get cheaper, more powerful, and more ubiquitous, the speed of innovation can be accelerated, and the sources of innovation can be democratized. For example, the ability to conduct trials of new products, such as autonomous vehicles, in simulated virtual environments can accelerate the machine learning process. This ability to use at-scale processing power in simulated environments also lowers the cost of building a real-world testing environment. This, in turn, can democratize innovation by facilitating individual tinkerers and inventors to bring out the ideas and products to market that benefit society.
5. How C-suite executives can leverage data to deliver business value to their organizations?
The number of ways that data can be leveraged by C-Suite executives to deliver business value is almost innumerable. In one instance, EY worked with a sports apparel manufacturer to develop a solution using a knowledge graph to model and link customers, products, stores, and other data. This enabled their organization to visually interact with its data in an intuitive way to see which customers had affinities to which products. This data was then run through a machine-learning-based recommender system to make additional product recommendations. These recommendations were then fed into its marketing systems to be served up to its customers via multiple channels. The sales lifted from these types of solutions can range from 25 – 35%. Cases like this can enhance both the top-line and bottom-line performance of most organizations.
6. What are some of the challenges faced by the industry today?
There are several challenges, on the data and AI sides as well, in today’s analytics industry. On the data side, the primary challenge is data quality. Data quality problems can range from incomplete to inaccurate to untimely to insufficient to missing data. Imagine trying to execute a targeted marketing campaign when you can’t determine whether the customer entities in your disparate systems (CRM, ERP, POS, etc.,) are referring to the same or different customers. And also, when a machine learning model is trained with insufficient or inaccurate data, the insights derived from these models will be flawed. Many of these data challenges can be addressed through techniques such as the implementation of modern AI-based MDM systems, better validation rules in the source systems at the point of data capture, and the use of synthetic data to increase data volumes where needed.
On the AI side, several challenges need to be addressed, which range from technical to ethical. On the technical end of the spectrum, challenges can include selecting ML models that are not fit for purpose – over or underfitting models; and scaling models into production. Any of these issues will produce unreliable outputs. On the ethical side, AI and ML models are fed by data. If that data includes the biases of the humans that generated it, the insights generated by the AI models will also be biased. This can open up legal exposure to organizations relying on AI to make important decisions (e.g. credit, hiring, housing, etc.). Looking further into the future, societies will also have to grapple with the ethics around things like autonomous warfare. Should machines be allowed to take human life? Does a human need to be in the loop? If so, does that defeat the speed advantage of using AI for these purposes in the first place? Like most challenges faced by industries, the technical challenges are usually easier to solve than the human challenges.
The post Exclusive Interview with Faisal Alam, EY Americas Consulting Emerging Technology Leader appeared first on .